User’s Guide

to

PARI / GP

(version 2.7.2)

The PARI Group

Institut de Mathématiques de Bordeaux, UMR 5251 du CNRS.

Université Bordeaux 1, 351 Cours de la Libération F-33405 TALENCE Cedex, FRANCE

e-mail:  pari@math.u-bordeaux.fr

Home Page:

http://pari.math.u-bordeaux.fr/

Copyright \begin{matrix}c \\ ◯ \end{matrix} 2000–2014 The PARI Group

Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies.

Permission is granted to copy and distribute modified versions, or translations, of this manual under the conditions for verbatim copying, provided also that the entire resulting derived work is distributed under the terms of a permission notice identical to this one.

PARI/GP is Copyright \begin{matrix}c \\ ◯ \end{matrix} 2000–2014 The PARI Group

PARI/GP is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation. It is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY WHATSOEVER.

Table of Contents

Chapter 1: Overview of the PARI system . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2 Multiprecision kernels / Portability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.3 The PARI types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.4 The PARI philosophy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.5 Operations and functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Chapter 2: The gp Calculator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2 The general gp input line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.3 The PARI types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.4 GP operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.5 Variables and symbolic expressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.6 Variables and Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 2.7 User defined functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.8 Member functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 2.9 Strings and Keywords . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 2.10 Errors and error recovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 2.11 Interfacing GP with other languages . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 2.12 Defaults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 2.13 Simple metacommands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 2.14 The preferences file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 2.15 Using readline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 2.16 GNU Emacs and PariEmacs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

Chapter 3: Functions and Operations Available in PARI and GP . . . . . . . . . . 63 3.1 Standard monadic or dyadic operators . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 3.2 Conversions and similar elementary functions or commands . . . . . . . . . . . . . . . 71 3.3 Transcendental functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 3.4 Arithmetic functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 3.5 Functions related to elliptic curves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 3.6 Functions related to general number fields . . . . . . . . . . . . . . . . . . . . . . . . . 152 3.7 Polynomials and power series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 3.8 Vectors, matrices, linear algebra and sets . . . . . . . . . . . . . . . . . . . . . . . . . . 228 3.9 Sums, products, integrals and similar functions . . . . . . . . . . . . . . . . . . . . . . 257 3.10 Plotting functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272 3.11 Programming in GP: control statements . . . . . . . . . . . . . . . . . . . . . . . . . 278 3.12 Programming in GP: other specific functions . . . . . . . . . . . . . . . . . . . . . . . 287 3.13 Parallel programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302 3.14 GP defaults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304

Appendix A: Installation Guide for the UNIX Versions . . . . . . . . . . . . . . . 315 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 4

Chapter 1: Overview of the PARI system

1.1 Introduction.

PARI/GP is a specialized computer algebra system, primarily aimed at number theorists, but has been put to good use in many other different fields, from topology or numerical analysis to physics.

Although quite an amount of symbolic manipulation is possible, PARI does badly compared to systems like Axiom, Magma, Maple, Mathematica, Maxima, or Reduce on such tasks, e.g. multivariate polynomials, formal integration, etc. On the other hand, the three main advantages of the system are its speed, the possibility of using directly data types which are familiar to mathematicians, and its extensive algebraic number theory module (from the above-mentioned systems, only Magma provides similar features).

Non-mathematical strong points include the possibility to program either in high-level scripting languages or with the PARI library, a mature system (development started in the mid eighties) that was used to conduct and disseminate original mathematical research, while building a large user community, linked by helpful mailing lists and a tradition of great user support from the developers. And, of course, PARI/GP is Free Software, covered by the GNU General Public License, either version 2 of the License or (at your option) any later version.

PARI is used in three different ways:

1) as a library libpari, which can be called from an upper-level language application, for instance written in ANSI C or C++;

2) as a sophisticated programmable calculator, named gp, whose language GP contains most of the control instructions of a standard language like C;

3) the compiler gp2c translates GP code to C, and loads it into the gp interpreter. A typical script compiled by gp2c runs 3 to 10 times faster. The generated C code can be edited and optimized by hand. It may also be used as a tutorial to libpari programming.

The present Chapter 1 gives an overview of the PARI/GP system; gp2c is distributed separately and comes with its own manual. Chapter 2 describes the GP programming language and the gp calculator. Chapter 3 describes all routines available in the calculator. Programming in library mode is explained in Chapters 4 and 5 in a separate booklet: User’s Guide to the PARI library

(libpari.dvi.

A tutorial for gp is provided in the standard distribution: A tutorial for PARI/GP (tutorial.dvi) and you should read this first. You can then start over and read the more boring stuff which lies ahead. You can have a quick idea of what is available by looking at the gp reference card (refcard.dvi or refcard.ps). In case of need, you can refer to the complete function description in Chapter 3.

How to get the latest version. Everything can be found on PARI’s home page:

      http://pari.math.u-bordeaux.fr/

From that point you may access all sources, some binaries, version information, the complete mailing list archives, frequently asked questions and various tips. All threaded and fully searchable.

How to report bugs. Bugs are submitted online to our Bug Tracking System, available from PARI’s home page, or directly from the URL

      http://pari.math.u-bordeaux.fr/Bugs/

Further instructions can be found on that page.

1.2 Multiprecision kernels / Portability.

The PARI multiprecision kernel comes in three non exclusive flavors. See Appendix A for how to set up these on your system; various compilers are supported, but the GNU gcc compiler is the definite favorite.

A first version is written entirely in ANSI C, with a C++-compatible syntax, and should be portable without trouble to any 32 or 64-bit computer having no drastic memory constraints. We do not know any example of a computer where a port was attempted and failed.

In a second version, time-critical parts of the kernel are written in inlined assembler. At present this includes

• the whole ix86 family (Intel, AMD, Cyrix) starting at the 386, up to the Xbox gaming console, including the Opteron 64 bit processor.

• three versions for the Sparc architecture: version 7, version 8 with SuperSparc processors, and version 8 with MicroSparc I or II processors. UltraSparcs use the MicroSparc II version;

• the DEC Alpha 64-bit processor;

• the Intel Itanium 64-bit processor;

• the PowerPC equipping old macintoshs (G3, G4, etc.);

• the HPPA processors (both 32 and 64 bit);

A third version uses the GNU MP library to implement most of its multiprecision kernel. It improves significantly on the native one for large operands, say 100 decimal digits of accuracy or more. You should enable it if GMP is present on your system. Parts of the first version are still in use within the GMP kernel, but are scheduled to disappear.

A historical version of the PARI/GP kernel, written in 1985, was specific to 680x0 based computers, and was entirely written in MC68020 assembly language. It ran on SUN-3/xx, Sony News, NeXT cubes and on 680x0 based Macs. It is no longer part of the PARI distribution; to run PARI with a 68k assembler micro-kernel, use the GMP kernel!

1.3 The PARI types.

The GP language is not typed in the traditional sense; in particular, variables have no type. In library mode, the type of all PARI objects is GEN, a generic type. On the other hand, it is dynamically typed: each object has a specific internal type, depending on the mathematical object it represents.

The crucial word is recursiveness: most of the PARI types are recursive. For example, the basic internal type t_COMPLEX exists. However, the components (i.e. the real and imaginary part) of such a “complex number” can be of any type. The only sensible ones are integers (we are then in Z[i]), rational numbers (Q[i]), real numbers (R[i] = C), or even elements of Z/nZ (in (Z/nZ)[t]/(t^{2}+1)), or p-adic numbers when p ≡ 3 mod 4 (Q_{p}[i]). This feature must not be used too rashly in library mode: for example you are in principle allowed to create objects which are “complex numbers of complex numbers”. (This is not possible under gp.) But do not expect PARI to make sensible use of such objects: you will mainly get nonsense.

On the other hand, it is allowed to have components of different, but compatible, types, which can be freely mixed in basic ring operations + or ×. For example, taking again complex numbers, the real part could be an integer, and the imaginary part a rational number. On the other hand, if the real part is a real number, the imaginary part cannot be an integer modulo n !

Let us now describe the types. As explained above, they are built recursively from basic types which are as follows. We use the letter T to designate any type; the symbolic names t_xxx correspond to the internal representations of the types.

type t_INT  Z  Integers (with arbitrary precision)
type t_REAL  R  Real numbers (with arbitrary precision)
type t_INTMOD  Z/nZ  Intmods (integers modulo n)
type t_FRAC  Q  Rational numbers (in irreducible form)
type t_FFELT  F_{q}  Finite field element
type t_COMPLEX T [i]  Complex numbers
type t_PADIC  Q_{p}  p-adic numbers
type t_QUAD  Q[w]  Quadratic Numbers (where [Z[w] : Z] = 2)
type t_POLMOD  T [X]/(P)  Polmods (polynomials modulo P ∈ T [X])
type t_POL  T [X]  Polynomials
type t_SER  T ((X))  Power series (finite Laurent series)
type t_RFRAC  T (X)  Rational functions (in irreducible form)
type t_VEC  T^{n}  Row (i.e. horizontal) vectors
type t_COL  T^{n}  Column (i.e. vertical) vectors
type t_MAT  M_{m,n}(T)  Matrices
type t_LIST  T^{n}  Lists
type t_STR  Character strings
type t_CLOSURE  Functions
type t_ERROR  Error messages

and where the types T in recursive types can be different in each component. The first nine basic types, from t_INT to t_POLMOD, are called scalar types because they essentially occur as coefficients of other more complicated objects. Type t_POLMOD is used to define algebraic extensions of a base ring, and as such is a scalar type.

In addition, there exist types t_QFR and t_QFI for integral binary quadratic forms, and the internal type t_VECSMALL. The latter holds vectors of small integers, whose absolute value is bounded by 2^{31} (resp. 2^{63}) on 32-bit, resp. 64-bit, machines. They are used internally to represent permutations, polynomials or matrices over a small finite field, etc.

Every PARI object (called GEN in the sequel) belongs to one of these basic types. Let us have a closer look.

1.3.1 Integers and reals. They are of arbitrary and varying length (each number carrying in its internal representation its own length or precision) with the following mild restrictions (given for 32-bit machines, the restrictions for 64-bit machines being so weak as to be considered nonexistent): integers must be in absolute value less than 2^{536870815} (i.e. roughly 161614219 decimal digits). The precision of real numbers is also at most 161614219 significant decimal digits, and the binary exponent must be in absolute value less than 2^{29}, resp. 2^{61}, on 32-bit, resp. 64-bit machines.

Integers and real numbers are non-recursive types.

1.3.2 Intmods, rational numbers, p-adic numbers, polmods, and rational functions. These are recursive, but in a restricted way.

For intmods or polmods, there are two components: the modulus, which must be of type integer (resp. polynomial), and the representative number (resp. polynomial).

For rational numbers or rational functions, there are also only two components: the numerator and the denominator, which must both be of type integer (resp. polynomial).

Finally, p-adic numbers have three components: the prime p, the “modulus” p^{k}, and an approximation to the p-adic number. Here Z_{p} is considered as the projective limit \underleftarrow{lim}Z/p^{k}Z via its finite quotients, and Q_{p} as its field of fractions. Like real numbers, the codewords contain an exponent, giving the p-adic valuation of the number, and also the information on the precision of the number, which is redundant with p^{k}, but is included for the sake of efficiency.

1.3.3 Finite field elements. The exact internal format depends of the finite field size, but it includes the field characteristic p, an irreducible polynomial T ∈ F_{p}[X] defining the finite field F_{p}[X]/(T) and the element expressed as a polynomial in (the class of) X.

1.3.4 Complex numbers and quadratic numbers. Quadratic numbers are numbers of the form a + bw, where w is such that [Z[w] : Z] = 2, and more precisely w = \sqrt{d}/2 when d ≡ 0 mod 4, and w = (1 + \sqrt{d})/2 when d ≡ 1 mod 4, where d is the discriminant of a quadratic order. Complex numbers correspond to the important special case w = \sqrt{−1}.

Complex numbers are partially recursive: the two components a and b can be of type t_INT, t_REAL, t_INTMOD, t_FRAC, or t_PADIC, and can be mixed, subject to the limitations mentioned above. For example, a+bi with a and b p-adic is in Q_{p}[i], but this is equal to Q_{p} when p ≡ 1 mod 4, hence we must exclude these p when one explicitly uses a complex p-adic type. Quadratic numbers are more restricted: their components may be as above, except that t_REAL is not allowed.

1.3.5 Polynomials, power series, vectors, matrices and lists. They are completely recursive: their components can be of any type, and types can be mixed (however beware when doing operations). Note in particular that a polynomial in two variables is simply a polynomial with polynomial coefficients.

In the present version 2.7.2 of PARI, it is not possible to handle conveniently power series of power series, i.e. power series in several variables. However power series of polynomials (which are power series in several variables of a special type) are OK. This is a difficult design problem: the mathematical problem itself contains some amount of imprecision, and it is not easy to design an intuitive generic interface for such beasts.

1.3.6 Strings. These contain objects just as they would be printed by the gp calculator.

1.3.7 Zero. What is zero? This is a crucial question in all computer systems. The answer we give in PARI is the following. For exact types, all zeros are equivalent and are exact, and thus are usually represented as an integer zero. The problem becomes non-trivial for imprecise types: there are infinitely many distinct zeros of each of these types! For p-adics and power series the answer is as follows: every such object, including 0, has an exponent e. This p-adic or X-adic zero is understood to be equal to O(p^{e}) or O(X^{e}) respectively.

Real numbers also have exponents and a real zero is in fact O(2^{e}) where e is now usually a negative binary exponent. This of course is printed as usual for a floating point number (0.00 ⋯ or 0.Exx depending on the output format) and not with a O symbol as with p-adics or power series. With respect to the natural ordering on the reals we make the following convention: whatever its exponent a real zero is smaller than any positive number, and any two real zeroes are equal.

1.4 The PARI philosophy.

The basic principles which govern PARI is that operations and functions should, firstly, give as exact a result as possible, and secondly, be permitted if they make any kind of sense.

In this respect, we make an important distinction between exact and inexact objects: by definition, types t_REAL, t_PADIC or t_SER are imprecise. A PARI object having one of these imprecise types anywhere in its tree is inexact, and exact otherwise. No loss of accuracy (rounding error) is involved when dealing with exact objects. Specifically, an exact operation between exact objects will yield an exact object. For example, dividing 1 by 3 does not give 0.333 ⋯, but the rational number (1/3). To get the result as a floating point real number, evaluate 1./3 or 0.+1/3.

Conversely, the result of operations between imprecise objects, although inexact by nature, will be as precise as possible. Consider for example the addition of two real numbers x and y. The accuracy of the result is a priori unpredictable; it depends on the precisions of x and y, on their sizes, and also on the size of x + y. From this data, PARI works out the right precision for the result. Even if it is working in calculator mode gp, where there is a notion of default precision, its value is only used to convert exact types to inexact ones.

In particular, if an operation involves objects of different accuracies, some digits will be disregarded by PARI. It is a common source of errors to forget, for instance, that a real number is given as r + 2^{e}ε where r is a rational approximation, e a binary exponent and ε is a nondescript real number less than 1 in absolute value. Hence, any number less than 2^{e} may be treated as an exact zero:

    ? 0.E-28 + 1.E-100

    %1 = 0.E-28
    ? 0.E100 + 1
    %2 = 0.E100

As an exercise, if a = 2^(-100), why do a + 0. and a * 1. differ?

The second principle is that PARI operations are in general quite permissive. For instance taking the exponential of a vector should not make sense. However, it frequently happens that one wants to apply a given function to all elements in a vector. This is easily done using a loop, or using the apply built-in function, but in fact PARI assumes that this is exactly what you want to do when you apply a scalar function to a vector. Taking the exponential of a vector will do just that, so no work is necessary. Most transcendental functions work in the same way*.

In the same spirit, when objects of different types are combined they are first automatically mapped to a suitable ring, where the computation becomes meaningful:

        ? 1/3 + Mod(1,5)
        %1 = Mod(3, 5)
        ? I + O(5^9)
        %2 = 2 + 5 + 2*5^2 + 5^3 + 3*5^4 + 4*5^5 + 2*5^6 + 3*5^7 + O(5^9)
        ? Mod(1,15) + Mod(1,10)
        %3 = Mod(2, 5)

The first example is straightforward: since 3 is invertible mod 5, (1/3) is easily mapped to Z/5Z. In the second example, I stands for the customary square root of −1; we obtain a 5-adic number, 5-adically close to a square root of −1. The final example is more problematic, but there are natural maps from Z/15Z and Z/10Z to Z/5Z, and the computation takes place there.

1.5 Operations and functions.

The available operations and functions in PARI are described in detail in Chapter 3. Here is a brief summary:

1.5.1 Standard arithmetic operations.

Of course, the four standard operators +, -, *, / exist. We emphasize once more that division is, as far as possible, an exact operation: 4 divided by 3 gives (4/3). In addition to this, operations on integers or polynomials, like \ (Euclidean division), % (Euclidean remainder) exist; for integers, \/ computes the quotient such that the remainder has smallest possible absolute value. There is also the exponentiation operator ^, when the exponent is of type integer; otherwise, it is considered as a transcendental function. Finally, the logical operators ! (not prefix operator), && (and operator), || (or operator) exist, giving as results 1 (true) or 0 (false).

1.5.2 Conversions and similar functions.

Many conversion functions are available to convert between different types. For example floor, ceiling, rounding, truncation, etc. . .. Other simple functions are included like real and imaginary part, conjugation, norm, absolute value, changing precision or creating an intmod or a polmod.

* An ambiguity arises with square matrices. PARI always considers that you want to do componentwise function evaluation in this context, hence to get for example the standard exponential of a square matrix you would need to implement a different function.

1.5.3 Transcendental functions.

They usually operate on any complex number, power series, and some also on p-adics. The list is ever-expanding and of course contains all the elementary functions (exp/log, trigonometric functions), plus many others (modular functions, Bessel functions, polylogarithms…). Recall that by extension, PARI usually allows a transcendental function to operate componentwise on vectors or matrices.

1.5.4 Arithmetic functions.

Apart from a few like the factorial function or the Fibonacci numbers, these are functions which explicitly use the prime factor decomposition of integers. The standard functions are included. A number of factoring methods are used by a rather sophisticated factoring engine (to name a few, Shanks’s SQUFOF, Pollard’s rho, Lenstra’s ECM, the MPQS quadratic sieve). These routines output strong pseudoprimes, which may be certified by the APRCL test.

There is also a large package to work with algebraic number fields. All the usual operations on elements, ideals, prime ideals, etc. are available. More sophisticated functions are also implemented, like solving Thue equations, finding integral bases and discriminants of number fields, computing class groups and fundamental units, computing in relative number field extensions, Galois and class field theory, and also many functions dealing with elliptic curves over Q or over local fields.

1.5.5 Other functions.

Quite a number of other functions dealing with polynomials (e.g. finding complex or p-adic roots, factoring, etc), power series (e.g. substitution, reversion), linear algebra (e.g. determinant, characteristic polynomial, linear systems), and different kinds of recursions are also included. In addition, standard numerical analysis routines like univariate integration (using the double exponential method), real root finding (when the root is bracketed), polynomial interpolation, infinite series evaluation, and plotting are included.

And now, you should really have a look at the tutorial before proceeding.

12

Chapter 2: The gp Calculator

2.1 Introduction.

Originally, gp was designed as a debugging device for the PARI system library. Over the years, it has become a powerful user-friendly stand-alone calculator. The mathematical functions available in PARI and gp are described in the next chapter. In the present one, we describe the specific use of the gp programmable calculator.

EMACS: If you have GNU Emacs and use the PariEmacs package, you can work in a special Emacs shell, described in Section 2.16. Specific features of this Emacs shell are indicated by an EMACS sign in the left margin.

2.1.1 Startup.

To start the calculator, the general command line syntax is:

gp [-D key=val] [files]

where items within brackets are optional. The [files] argument is a list of files written in the GP scripting language, which will be loaded on startup. There can be any number of arguments of the form -D key=val, setting some internal parameters of gp, or defaults: each sets the default key to the value val. See Section 2.12 below for a list and explanation of all defaults. These defaults can be changed by adding parameters to the input line as above, or interactively during a gp session, or in a preferences file also known as gprc.

If a preferences file (to be discussed in Section 2.14) is found, gp then reads it and executes the commands it contains. This provides an easy way to customize gp. The files argument is processed right after the gprc.

A copyright banner then appears which includes the version number, and a lot of useful technical information. After the copyright, the computer writes the top-level help information, some initial defaults, and then waits after printing its prompt, which is ’? ’ by default . Whether extended on-line help and line editing are available or not is indicated in this gp banner, between the version number and the copyright message. Consider investigating the matter with the person who installed gp if they are not. Do this as well if there is no mention of the GMP kernel.

2.1.2 Getting help.

To get help, type a ? and hit return. A menu appears, describing the main categories of available functions and how to get more detailed help. If you now type ?n with 1 ≤ n ≤ 11, you get the list of commands corresponding to category n and simultaneously to Section 3.n of this manual. If you type ?functionname where functionname is the name of a PARI function, you will get a short explanation of this function.

If extended help (see Section 2.13.1) is available on your system, you can double or triple the ? sign to get much more: respectively the complete description of the function (e.g. ??sqrt), or a list

of gp functions relevant to your query (e.g. ???"elliptic curve" or ???"quadratic field").

If gp was properly installed (see Appendix A), a line editor is available to correct the command line, get automatic completions, and so on. See Section 2.15 or ??readline for a short summary of the line editor’s commands.

If you type ?\ you will get a short description of the metacommands (keyboard shortcuts).

Finally, typing ?. will return the list of available (pre-defined) member functions. These are functions attached to specific kind of objects, used to retrieve easily some information from complicated structures (you can define your own but they won’t be shown here). We will soon describe these commands in more detail.

More generally, commands starting with the symbols \ or ?, are not computing commands, but are metacommands which allow you to exchange information with gp. The available metacommands can be divided into default setting commands (explained below) and simple commands (or keyboard shortcuts, to be dealt with in Section 2.13).

2.1.3 Input.

Just type in an instruction, e.g. 1 + 1, or Pi. No action is undertaken until you hit the <Return> key. Then computation starts, and a result is eventually printed. To suppress printing of the result, end the expression with a ; sign. Note that many systems use ; to indicate end of input. Not so in gp: a final semicolon means the result should not be printed. (Which is certainly useful if it occupies several screens.)

2.1.4 Interrupt, Quit.

Typing quit at the prompt ends the session and exits gp. At any point you can type Ctrl-C (that is press simultaneously the Control and C keys): the current computation is interrupted and control given back to you at the gp prompt, together with a message like

      ***   at top-level: gcd(a,b)
      ***                 ^--------
      *** gcd: user interrupt after 236 ms.

telling you how much time elapsed since the last command was typed in and in which GP function the computation was aborted. It does not mean that that much time was spent in the function, only that the evaluator was busy processing that specific function when you stopped it.

2.2 The general gp input line.

The gp calculator uses a purely interpreted language GP. The structure of this language is reminiscent of LISP with a functional notation, f(x,y) rather than (f x y): all programming constructs, such as if, while, etc… are functions*, and the main loop does not really execute, but rather evaluates (sequences of) expressions. Of course, it is by no means a true LISP, and has been strongly influenced by C and Perl since then.

* Not exactly, since not all their arguments need be evaluated. For instance it would be stupid to evaluate both branches of an if statement: since only one will apply, only this one is evaluated.

2.2.1 Introduction. User interaction with a gp session proceeds as follows. First, one types a sequence of characters at the gp prompt; see Section 2.15 for a description of the line editor. When you hit the <Return> key, gp gets your input, evaluates it, then prints the result and assigns it to an “history” array.

More precisely, the input is case-sensitive and, outside of character strings, blanks are completely ignored. Inputs are either metacommands or sequences of expressions. Metacommands are shortcuts designed to alter gp’s internal state, such as the working precision or general verbosity level; we shall describe them in Section 2.13, and ignore them for the time being.

The evaluation of a sequence of instructions proceeds in two phases: your input is first digested (byte-compiled) to a bytecode suitable for fast evaluation, in particular loop bodies are compiled only once but a priori evaluated many times; then the bytecode is evaluated.

An expression is formed by combining constants, variables, operator symbols, functions and control statements. It is evaluated using the conventions about operator priorities and left to right associativity. An expression always has a value, which can be any PARI object:

    ? 1 + 1

%1 = 2 \\ an ordinary integer

    ? x

%2 = x \\ a polynomial of degree 1 in the unknown x

    ? print("Hello")
    Hello           \\ void return value
    ? f(x) = x^2
    %3 = (x)->x^2   \\ a user function

In the third example, Hello is printed as a side effect, but is not the return value. The print command is a procedure, which conceptually returns nothing. But in fact procedures return a special void object, meant to be ignored (but which evaluates to 0 in a numeric context, and stored as 0 in the history or results). The final example assigns to the variable f the function x ↦ x^{2}, the alternative form f = x->x^2 achieving the same effect; the return value of a function definition is, unsurprisingly, a function object (of type t_CLOSURE).

Several expressions are combined on a single line by separating them with semicolons (’;’). Such an expression sequence will be called a seq. A seq also has a value, which is the value of the last expression in the sequence. Under gp, the value of the seq, and only this last value, becomes an history entry. The values of the other expressions in the seq are discarded after the execution of the seq is complete, except of course if they were assigned into variables. In addition, the value of the seq is printed if the line does not end with a semicolon ;.

2.2.2 The gp history of results.

This is not to be confused with the history of your commands, maintained by readline. The gp history contains the results they produced, in sequence.

The successive elements of the history array are called %1, %2, … As a shortcut, the latest computed expression can also be called %, the previous one %`, the one before that %`` and so on.

When you suppress the printing of the result with a semicolon, it is still stored in the history, but its history number will not appear either. It is a better idea to assign it to a variable for later use than to mentally recompute what its number is. Of course, on the next line, you may just use

%.

The time used to compute that history entry is also stored as part of the entry and can be recovered using the %# operator: %#1, %#2, %#`; %# by itself returns the time needed to compute the last result (the one returned by %).

Remark. The history “array” is in fact better thought of as a queue: its size is limited to 5000 entries by default, after which gp starts forgetting the initial entries. So %1 becomes unavailable as gp prints %5001. You can modify the history size using histsize.

2.2.3 Special editing characters. A GP program can of course have more than one line. Since your commands are executed as soon as you have finished typing them, there must be a way to tell gp to wait for the next line or lines of input before doing anything. There are three ways of doing this.

The first one is to use the backslash character \ at the end of the line that you are typing, just before hitting <Return>. This tells gp that what you will write on the next line is the physical continuation of what you have just written. In other words, it makes gp forget your newline character. You can type a \ anywhere. It is interpreted as above only if (apart from ignored whitespace characters) it is immediately followed by a newline. For example, you can type

    ? 3 + \
    4

instead of typing 3 + 4.

The second one is a variation on the first, and is mostly useful when defining a user function (see Section 2.7): since an equal sign can never end a valid expression, gp disregards a newline immediately following an =.

    ? a =
    123
    %1 = 123

The third one is in general much more useful, and uses braces { and }. An opening brace { signals that you are typing a multi-line command, and newlines are ignored until you type a closing brace }. There are two important, but easily obeyed, restrictions: first, braces do not nest; second, inside an open brace-close brace pair, all input lines are concatenated, suppressing any newlines. Thus, all newlines should occur after a semicolon (;), a comma (,) or an operator (for clarity’s sake, never split an identifier over two lines in this way). For instance, the following program

    {
      a = b
      b = c
    }

would silently produce garbage, since this is interpreted as a=bb=c which assigns the value of c to both bb and a. It should have been written

    {
      a = b;
      b = c;
    }

2.3 The PARI types.

We see here how to input values of the different data types known to PARI. Recall that blanks are ignored in any expression which is not a string (see below).

A note on efficiency. The following types are provided for convenience, not for speed: t_INTMOD, t_FRAC, t_PADIC, t_QUAD, t_POLMOD, t_RFRAC. Indeed, they always perform a reduction of some kind after each basic operation, even though it is usually more efficient to perform a single reduction at the end of some complex computation. For instance, in a convolution product ∑_{i+j=n} x_{i}y_{j} in Z/NZ — common when multiplying polynomials! —, it is quite wasteful to perform n reductions modulo N. In short, basic individual operations on these types are fast, but recursive objects with such components could be handled more efficiently: programming with libpari will save large constant factors here, compared to GP.

2.3.1 Integers (t_INT). After an (optional) leading + or -, type in the decimal digits of your integer. No decimal point!

    ? 1234567
    %1 = 1234567
    ? -3
    %2 = -3

? 1. \\ oops, not an integer

    %3 = 1.000000000000000000000000000

2.3.2 Real numbers (t_REAL).

Real numbers are represented (approximately) in a floating point system, internally in base 2, but converted to base 10 for input / output purposes. A t_REAL object has a given accuracy (or precision) ℓ ≥ 0; it comprises

• a sign s: +1, −1 or 0;

• a mantissa m: a multiprecision integer, 0 ≤ m < 10^{ℓ};

• an exponent e: a small integer in [−E, E], where E ≈ 2^{B} log_{10} 2, and B = 32 on a 32-bit machine and 64 otherwise.

This data may represent any real number x such that

|x − sm10^{e}| < 10^{e−ℓ}.

We consider that a t_REAL with sign s = 0 has accuracy ℓ = 0, so that its mantissa is useless, but it still has an exponent e and acts like a machine epsilon for all accuracies < e.

After an (optional) leading + or -, type a number with a decimal point. Leading zeroes may be omitted, up to the decimal point, but trailing zeroes are important: your t_REAL is assigned an internal precision, which is the supremum of the input precision, one more than the number of decimal digits input, and the default precision. For example, if the default precision is 28 digits, typing 2. yields a precision of 28 digits, but 2.0… 0 with 45 zeros gives a number with internal precision at least 45, although less may be printed.

You can also use scientific notation with the letter E or e. As usual, en is interpreted as ×10^{n} for all integers n. Since the result is converted to a t_REAL, you may often omit the decimal point in this case: 6.02 E 23 or 1e-5 are fine, but e10 is not.

By definition, 0.E n returns a real 0 of exponent n, whereas 0. returns a real 0 “of default precision” (of exponent −realprecision), see Section 1.3.7, behaving like the machine epsilon for the current default accuracy: any float of smaller absolute value is indistinguishable from 0.

Note on output formats. A zero real number is printed in e format as 0.Exx where xx is the (usually negative) decimal exponent of the number (cf. Section 1.3.7). This allows the user to check the accuracy of that particular zero.

When the integer part of a real number x is not known exactly because the exponent of x is greater than the internal precision, the real number is printed in e format.

2.3.3 Intmods (t_INTMOD). To create the image of the integer a in Z/bZ (for some non-zero integer b), type Mod(a,b); not a%b. Internally, all operations are done on integer representatives belonging to [0, b − 1].

Note that this type is available for convenience, not for speed: each elementary operation involves a reduction modulo b.

If x is a t_INTMOD Mod(a,b), the following member function is defined:

x.mod: return the modulus b.

2.3.4 Rational numbers (t_FRAC). All fractions are automatically reduced to lowest terms, so it is impossible to work with reducible fractions. To enter n/m just type it as written. As explained in Section 3.1.5, floating point division is not performed, only reduction to lowest terms.

Note that rational computation are almost never the fastest method to proceed: in the PARI implementation, each elementary operation involves computing a gcd. It is generally a little more efficient to cancel denominators and work with integers only:

? P = Pol( vector(10^3,i, 1/i) ); \\ big polynomial with small rational coeffs

    ? P^2
    time = 1,392 ms.
    ? c = content(P); c^2 * (P/c)^2;  \\ same computation in integers
    time = 1,116 ms.

And much more efficient (but harder to setup) to use homomorphic imaging schemes and modular computations. As the simple example below indicates, if you only need modular information, it is very worthwhile to work with t_INTMODs directly, rather than deal with t_FRACs all the way through:

    ? p = nextprime(10^7);
    ? sum(i=1, 10^5, 1/i) % p
    time = 13,288 ms.
    %1 = 2759492
    ? sum(i=1, 10^5, Mod(1/i, p))
    time = 60 ms.
    %2 = Mod(2759492, 10000019)

2.3.5 Finite field elements (t_FFELT). Let T ∈ F_{p}[X] be a monic irreducible polynomial defining your finite field over F_{p}, for instance obtained using ffinit. Then the ffgen function creates a generator of the finite field as an F_{p}-algebra, namely the class of X in F_{p}[X]/(T), from which you can build all other elements. For instance, to create the field F_{2^{8}}, we write

    ? T = ffinit(2, 8);
    ? y = ffgen(T, 'y);
    ? y^0    \\ the unit element in the field
    %3 = 1
    ? y^8
    %4 = y^6 + y^5 + y^4 + y^3 + y + 1

The second (optional) parameter to ffgen is only used to display the result; it is customary to use the name of the variable we assign the generator to. If g is a t_FFELT, the following member functions are defined:

g.pol: the polynomial (with reduced integer coefficients) expressing g in term of the field generator.

g.p: the characteristic of the finite field.

g.f: the dimension of the definition field over its prime field; the cardinality of the definition field is thus p^{f}.

g.mod: the minimal polynomial (with reduced integer coefficients) of the field generator.

2.3.6 Complex numbers (t_COMPLEX). To enter x + iy, type x + I*y. (That’s I, not i!) The letter I stands for \sqrt{−1}. The “real” and “imaginary” parts x and y can be of type t_INT, t_REAL,

t_INTMOD, t_FRAC, or t_PADIC.

2.3.7 p-adic numbers (t_PADIC):. Typing O(p^k), where p and k are integers, yields a p-adic 0 of accuracy k, representing any p-adic number whose valuation is ≥ k. To input a general non-0 p-adic number, write a suitably precise rational or integer approximation and add O(p^k) to it.

Note that it is not checked whether p is indeed prime but results are undefined if this is not the case: you can work on 10-adics if you want, but disasters will happen as soon as you do something non-trivial like taking a square root. Note that O(25) is not the same as O(5^2); you want the latter!

For example, you can type in the 7-adic number

    2*7^(-1) + 3 + 4*7 + 2*7^2 + O(7^3)

exactly as shown, or equivalently as 905/7 + O(7^3).

If a is a t_PADIC, the following member functions are defined:

a.mod: returns the modulus p^{k}.

a.p: returns p.

Note that this type is available for convenience, not for speed: internally, t_PADICs are stored as p-adic units modulo some p^{k}. Each elementary operation involves updating p^{k} (multiplying or dividing by powers of p) and a reduction mod p^{k}. In particular, additions are slow.

        ? n = 1+O(2^20);   for (i=1,10^6, n++)
        time = 841 ms.

        ? n = Mod(1,2^20); for (i=1,10^6, n++)
        time = 441 ms.
        ? n = 1;           for (i=1,10^6, n++)
        time = 328 ms.

The penalty associated with maintaining p^{k} decreases steeply as p increases (and updates become very rare). But t_INTMODs remain at least 25% more efficient. (But they do not have denominators!)

2.3.8 Quadratic numbers (t_QUAD). This type is used to work in the quadratic order of discriminant d, where d is a non-square integer congruent to 0 or 1 (modulo 4). The command

        w = quadgen(d)

assigns to w the “canonical” generator for the integer basis of the order of discriminant d, i.e. w = \sqrt{d}/2 if d ≡ 0 mod 4, and w = (1 + \sqrt{d})/2 if d ≡ 1 mod 4. The name w is of course just a suggestion, but corresponds to traditional usage. You can use any variable name that you like, but quadgen(d) is always printed as w, regardless of the discriminant. So beware, two t_QUADs can be printed in the same way and not be equal; however, gp will refuse to add or multiply them for example.

Since the order is Z + wZ, any other element can be input as x+y*w for some integers x and y. In fact, you may work in its fraction field Q(\sqrt{d}) and use t_FRAC values for x and y.

2.3.9 Polmods (t_POLMOD). Exactly as for intmods, to enter x mod y (where x and y are polynomials), type Mod(x,y), not x%y. Note that when y is an irreducible polynomial in one variable, polmods whose modulus is y are simply algebraic numbers in the finite extension defined by the polynomial y. This allows us to work easily in number fields, finite extensions of the p-adic field Q_{p}, or finite fields.

Note that this type is available for convenience, not for speed: each elementary operation involves a reduction modulo y. If p is a t_POLMOD, the following member functions are defined:

p.pol: return a representative of the polynomial class of minimal degree.

p.mod: return the modulus.

Important remark. Mathematically, the variables occurring in a polmod are not free variables. But internally, a congruence class in R[t]/(y) is represented by its representative of lowest degree, which is a t_POL in R[t], and computations occur with polynomials in the variable t. PARI will not recognize that Mod(y, y^2 + 1) is “the same” as Mod(x, x^2 + 1), since x and y are different variables.

To avoid inconsistencies, polmods must use the same variable in internal operations (i.e. between polmods) and variables of lower priority for external operations, typically between a polynomial and a polmod. See Section 2.5.3 for a definition of “priority” and a discussion of (PARI’s idea of) multivariate polynomial arithmetic. For instance:

        ? Mod(x, x^2+ 1) + Mod(x, x^2 + 1)
        %1 = Mod(2*x, x^2 + 1)    \\ 2i (or −2i), with i^{2} = −1
        ? x + Mod(y, y^2 + 1)
        %2 = x + Mod(y, y^2 + 1)  \\ in Q(i)[x]
        ? y + Mod(x, x^2 + 1)
        %3 = Mod(x + y, x^2 + 1)  \\ in Q(y)[i]

The first two are straightforward, but the last one may not be what you want: y is treated here as a numerical parameter, not as a polynomial variable.

If the main variables are the same, it is allowed to mix t_POL and t_POLMODs. The result is the expected t_POLMOD. For instance

    ? x + Mod(x, x^2 + 1)
    %1 = Mod(2*x, x^2 + 1)

2.3.10 Polynomials (t_POL). Type the polynomial in a natural way, not forgetting to put a “∗” between a coefficient and a formal variable;

? 1 + 2*x + 3*x^2
%1 = 3*x^2 + 2*x + 1

This assumes that x is still a ”free variable”.

? x = 1; 1 + 2*x + 3*x^2
%2 = 6

generates an integer, not a polynomial! It is good practice to never assign values to polynomial variables to avoid the above problem, but a foolproof construction is available using 'x instead of x: 'x is a constant evaluating to the free variable with name x, independently of the current value of x.

? x = 1; 1 + 2*'x + 3*'x^2
%3 = 1 + 2*x + 3*x^2
? x = 'x; 1 + 2*x + 3*x^2
%4 = 1 + 2*x + 3*x^2

You may also use the functions Pol or Polrev:

? Pol([1,2,3]) \\ Pol creates a polynomial in x by default

%1 = x^2 + 2*x + 3
? Polrev([1,2,3])
%2 = 3*x^2 + 2*x + 1
? Pol([1,2,3], 'y)   \\ we use 'y, safer than y
%3 = y^2 + 2*y + 3

The latter two are much more efficient constructors than an explicit summation (the latter is quadratic in the degree, the former linear):

? for (i=1, 10^4, Polrev( vector(100, i,i) ) )
time = 124ms
? for (i=1, 10^4, sum(i = 1, 100, (i+1) * 'x^i) )
time = 3,985ms

Polynomials are always printed as univariate polynomials, with monomials sorted by decreasing degree:

? (x+y+1)^2
%1 = x^2 + (2*y + 2)*x + (y^2 + 2*y + 1)

(Univariate polynomial in x whose coefficients are polynomials in y.) See Section 2.5 for valid variable names, and a discussion of multivariate polynomial rings.

2.3.11 Power series (t_SER). Typing O(X^k), where k is an integer, yields an X-adic 0 of accuracy k, representing any power series in X whose valuation is ≥ k. Of course, X can be replaced by any other variable name! To input a general non-0 power series, type in a polynomial or rational function (in X, say), and add O(X^k) to it. The discussion in the t_POL section about variables remains valid; a constructor Ser replaces Pol and Polrev.

Caveat. Power series with inexact coefficients sometimes have a non-intuitive behavior: if k significant terms are requested, an inexact zero is counted as significant, even if it is the coefficient of lowest degree. This means that useful higher order terms may be disregarded.

If a series with a zero leading coefficient must be inverted, then as a desperation measure that coefficient is discarded, and a warning is issued:

    ? C = 0. + y + O(y^2);
    ? 1/C
      *** _/_: Warning: normalizing a series with 0 leading term.
    %2 = y^-1 + O(1)

The last output could be construed as a bug since it is a priori impossible to deduce such a result from the input (0. represents any sufficiently small real number). But it was thought more useful to try and go on with an approximate computation than to raise an early exception.

If the series precision is insufficient, errors may occur (mostly division by 0), which could have been avoided by a better global understanding of the computation:

    ? A = 1/(y + 0.); B = 1. + O(y);
    ? B * denominator(A)
    %2 = 0.E-28 + O(y)
    ? A/B
      *** _/_: Warning: normalizing a series with 0 leading term.
    %3 = 1.000000000000000000000000000*y^-1 + O(1)
    ? A*B
      *** _*_: Warning: normalizing a series with 0 leading term.
    %4 = 1.000000000000000000000000000*y^-1 + O(1)

2.3.12 Rational functions (t_RFRAC). As for fractions, all rational functions are automatically reduced to lowest terms. All that was said about fractions in Section 2.3.4 remains valid here.

2.3.13 Binary quadratic forms of positive or negative discriminant (t_QFR and t_QFI). These are input using the function Qfb. For example Qfb(1,2,3) creates the binary form x^{2} + 2xy+3y^{2}. It is imaginary (of internal type t_QFI) since its discriminant 2^{2} −4×3 = −8 is negative. Although imaginary forms could be positive or negative definite, only positive definite forms are implemented.

In the case of forms with positive discriminant (t_QFR), you may add an optional fourth component (related to the regulator, more precisely to Shanks and Lenstra’s “distance”), which must be a real number. See also the function qfbprimeform which directly creates a prime form of given discriminant.

2.3.14 Row and column vectors (t_VEC and t_COL).) To enter a row vector, type the components separated by commas “,”, and enclosed between brackets “[ ” and “ ]”, e.g. [1,2,3]. To enter a column vector, type the vector horizontally, and add a tilde “˜ ” to transpose. [ ] yields the empty (row) vector. The function Vec can be used to transform any object into a vector (see Chapter 3). The construction [i..j], where i ≤ j are two integers returns the vector [i, i + 1, …, j − 1, j]

? [1,2,3]
%1 = [1, 2, 3]
? [-2..3]
%2 = [-2, -1, 0, 1, 2, 3]

Let the variable v contain a (row or column) vector:

• v[m] refers to its m-th entry; you can assign any value to v[m], i.e. write something like v[m] = expr.

• v[i..j], where i ≤ j, returns the vector slice containing elements v[i], …, v[j]; you can not assign a result to v[i..j].

• v[^i] returns the vector whose i-th entry has been removed; you can not assign a result to

v[^i].

In the last two constructions v[i..j] and v[^i], i and j are allowed to be negative integers, in which case, we start counting from the end of the vector: e.g., −1 is the index of the last element.

? v = [1,2,3,4];
? v[2..4]
%2 = [2, 3, 4]
? v[^3]
%3 = [1, 2, 4]
? v[^-1]
%3 = [1, 2, 3]
? v[-3..-1]
%4 = [2, 3, 4]

Remark. vector is the standard constructor for row vectors whose i-th entry is given by a simple function of i; vectorv is similar for column vectors:

 ? vector(10, i, i^2+1)
 %1 = [2, 5, 10, 17, 26, 37, 50, 65, 82, 101]

The functions Vec and Col convert objects to row and column vectors respectively (as well as Vecrev and Colrev, which revert the indexing):

 ? T = poltchebi(5)   \\ 5-th Chebyshev polynomial
 %1 = 16*x^5 - 20*x^3 + 5*x
 ? Vec(T)
 %2 = [16, 0, -20, 0, 5, 0]  \\ coefficients of T
 ? Vecrev(T)
 %3 = [0, 5, 0, -20, 0, 16]  \\ ... in reverse order

Remark. For v a t_VEC, t_COL, t_LIST or t_MAT, the alternative set-notations

     [g(x) | x <- v, f(x)]
     [x | x <- v, f(x)]
     [g(x) | x <- v]

are available as shortcuts for

     apply(g, select(f, Vec(v)))
     select(f, Vec(v))
     apply(g, Vec(v))

respectively, and may serve as t_VEC constructors:

     ? [ p | p <- primes(10), isprime(p+2) ]
     %2 = [3, 5, 11, 17, 29]

returns the primes p (among the first 10 primes) such that (p, p + 2) is a twin pair;

     ? [ p^2 | p <- primes(10), p % 4 == 1 ]
     %1 = [25, 169, 289, 841]

returns the squares of the primes congruent to 1 modulo 4, where p runs among the first 10 primes.

2.3.15 Matrices (t_MAT). To enter a matrix, type the components row by row, the components being separated by commas “,”, the rows by semicolons “;”, and everything enclosed in brackets “[ ” and “ ]”, e.g. [x,y; z,t; u,v]. [;] yields an empty (0 × 0) matrix. The function Mat transforms any object into a matrix, and matrix creates matrices whose (i, j)-th entry is described by a function f(i, j):

    ? Mat(1)
    %1 =
    [1]
    ? matrix(2,2, i,j, 2*i+j)
    %2 =
    [3 4]
    [5 6]

Let the variable M contain a matrix, and let i, j, k, l denote four integers:

• M[i,j] refers to its (i, j)-th entry; you can assign any result to M[i,j].

• M[i,] refers to its i-th row; you can assign a t_VEC of the right dimension to M[i,].

• M[,j] refers to its j-th column; you can assign a t_COL of the right dimension to M[,j].

But M[i] is meaningless and triggers an error. The “range” i..j and “caret” ^c notations are available as for vectors; you can not assign to any of these:

• M[i..j, k..l], i ≤ j, k ≤ l, returns the submatrix built from the rows i to j and columns k to l of M. assign to M[i..j, j..l].

• M[i..j,] returns the submatrix built from the rows i to j of M.

• M[,i..j] returns the submatrix built from the columns i to j of M.

• M[i..j, ^k], i ≤ j, returns the submatrix built from the rows i to j and column k removed.

• M[^k,] returns the submatrix with row k removed.

• M[,^k] returns the submatrix with column k removed.

Finally,

• M[i..j, k] returns the t_COL built from the k-th column (entries i to j).

• M[^i, k] returns the t_COL built from the k-th column (entry i removed).

• M[k, i..j] returns the t_VEC built from the k-th row (entries i to j).

• M[k, ^i] returns the t_VEC built from the k-th row (entry i removed).

? M = [1,2,3;4,5,6;7,8,9];
? M[1..2, 2..3]
%2 =
[2 3]
[5 6]
? M[1..2,]
%3 =
[1 2 3]
[4 5 6]
? M[,2..3]
%4 =
[2 3]
[5 6]
[8 9]

All this is recursive, so if M is a matrix of matrices of …, an expression such as M[1,1][,3][4] = 1 is perfectly valid (and actually identical to M[1,1][4,3] = 1), assuming that all matrices along the way have compatible dimensions.

Technical note (design flaw). Matrices are internally represented as a vector of columns. All matrices with 0 columns are thus represented by the same object (internally, an empty vector), and there is no way to distinguish between them. Thus it is not possible to create or represent matrices with zero columns and an actual nonzero number of rows. The empty matrix [;] is handled as though it had an arbitrary number of rows, exactly as many as needed for the current computation to make sense:

? [1,2,3; 4,5,6] * [;]
%1 = [;]

The empty matrix on the first line is understood as a 3 × 0 matrix, and the result as a 2 × 0 matrix. On the other hand, it is possible to create matrices with a given positive number of columns, each of which has zero rows, e.g. using Mat as above or using the matrix function.

Note that although the internal representation is essentially the same, a row vector of column vectors is not a matrix; for example, multiplication will not work in the same way. It is easy to go from one representation to the other using Vec / Mat, though:

    ? [1,2,3;4,5,6]
    %1 =
    [1 2 3]
    [4 5 6]
    ? Vec(%)
    %2 = [[1, 4]~, [2, 5]~, [3, 6]~]
    ? Mat(%)
    %3 =
    [1 2 3]
    [4 5 6]

2.3.16 Lists (t_LIST). Lists can be input directly, as in List([1,2,3,4]); but in most cases, one creates an empty list, then appends elements using listput:

      ? a = List(); listput(a,1); listput(a,2);
      ? a
      %2 = List([1, 2])

Elements can be accessed directly as with the vector types described above.

2.3.17 Strings (t_STR). To enter a string, enclose it between double quotes ", like this: "this is a string". The function Str can be used to transform any object into a string.

2.3.18 Small vectors (t_VECSMALL). This is an internal type, used to code in an efficient way vectors containing only small integers, such as permutations. Most gp functions will refuse to operate on these objects.

2.3.19 Functions (t_CLOSURE). We will explain this at length in Section 2.7. For the time being, suffice it to say that functions can be assigned to variables, as any other object, and the following equivalent basic forms are available to create new ones

      f = (x,y) -> x^2 + y^2
      f(x,y) = x^2 + y^2

2.3.20 Error contexts (t_ERROR). An object of this type is created whenever an error occurs: it contains some information about the error and the error context. Usually, an appropriate error is printed immediately, the computation is aborted, and GP enters the “break loop”:

      ? 1/0; 1 + 1
        ***   at top-level: 1/0;1+1
        ***                  ^------
        *** _/_: division by a non-invertible object
        ***   Break loop: type 'break' to go back to the GP prompt

Here the computation is aborted as soon as we try to evaluate 1/0, and 1 + 1 is never executed. Exceptions can be trapped using iferr, however: we can evaluate some expression and either recover an ordinary result (no error occurred), or an exception (an error did occur).

      ? i = Mod(6,12); iferr(1/i, E, print(E)); 1 + 1
      error("impossible inverse modulo: Mod(6, 12).")

      %1 = 2

One can ignore the exception, print it as above, or extract non trivial information from the error context:

      ? i = Mod(6,12); iferr(1/i, E, print(component(E,1)));
      Mod(6, 12)

We can also rethrow the exception: error(E).

2.4 GP operators.

Loosely speaking, an operator is a function, usually associated to basic arithmetic operations, whose name contains only non-alphanumeric characters. For instance + or -, but also = or +=, or even [ ] (the selection operator). As all functions, operators take arguments, and return a value; assignment operators also have side effects: besides returning a value, they change the value of some variable.

Each operator has a fixed and unchangeable priority, which means that, in a given expression, the operations with the highest priority is performed first. Unless mentioned otherwise, operators at the same priority level are left-associative (performed from left to right), unless they are assignments, in which case they are right-associative. Anything enclosed between parenthesis is considered a complete subexpression, and is resolved recursively, independently of the surrounding context. For instance,

      a + b + c    -->   (a + b) + c     \\ left-associative
      a = b = c    -->   a = (b = c)     \\ right-associative

Assuming that op_{1}, op_{2}, op_{3} are binary operators with increasing priorities (think of +, *, ^),

x op_{1} y op_{2} z op_{2} x op_{3} y

is equivalent to

x op_{1} ((y op_{2} z) op_{2} (x op_{3} y)).

GP contains many different operators, either unary (having only one argument) or binary, plus a few special selection operators. Unary operators are defined as either prefix or postfix, meaning that they respectively precede (op x) and follow (x op) their single argument. Some symbols are syntactically correct in both positions, like !, but then represent different operators: the ! symbol represents the negation and factorial operators when in prefix and postfix position respectively. Binary operators all use the (infix) syntax x op y.

Most operators are standard (+, %, =), some are borrowed from the C language (++, <<), and a few are specific to GP (\, #). Beware that some GP operators differ slightly from their C counterparts. For instance, GP’s postfix ++ returns the new value, like the prefix ++ of C, and the binary shifts <<, >> have a priority which is different from (higher than) that of their C counterparts. When in doubt, just surround everything by parentheses; besides, your code will be more legible.

Here is the list of available operators, ordered by decreasing priority, binary and left-associative unless mentioned otherwise. An expression is an lvalue if something can be assigned to it. (The name comes from left-value, to the left of a = operator; e.g. x, or v[1] are lvalues, but x + 1 is not.)

• Priority 14 : as in x:small, is used to indicate to the GP2C compiler that the variable on the left-hand

side always contains objects of the type specified on the right hand-side (here, a small integer) in order to produce more efficient or more readable C code. This is ignored by GP.

• Priority 13 ( ) is the function call operator. If f is a closure and args is a comma-separated list of

arguments (possibly empty), f(args) evaluates f on those arguments.

• Priority 12 ++ and -- (unary, postfix): if x is an lvalue, x++ assigns the value x + 1 to x, then returns

the new value of x. This corresponds to the C statement ++x: there is no prefix ++ operator in GP. x-- does the same with x − 1. These operators are not associative, i.e. x++++ is invalid, since x++ is not an lvalue.

• Priority 11 .member (unary, postfix): x.member extracts member from structure x (see Section 2.8).

[ ] is the selection operator. x[i] returns the i-th component of vector x; x[i,j], x[,j] and x[i,] respectively return the entry of coordinates (i, j), the j-th column, and the i-th row of matrix x. If the assignment operator (=) immediately follows a sequence of selections, it assigns its right hand side to the selected component. E.g x[1][1] = 0 is valid; but beware that (x[1])[1] = 0 is not (because the parentheses force the complete evaluation of x[1], and the result is not modifiable).

• Priority 10 ' (unary, postfix): derivative with respect to the main variable. If f is a function (t_CLOSURE),

f′ is allowed and defines a new function, which will perform numerical derivation when evaluated at a scalar x; this is defined as (f(x + ε) − f(x − ε))/2ε for a suitably small epsilon depending on current precision.

? (x^2 + y*x + y^2)' \\ derive with respect to main variable x

    %1 = 2*x + y
    ? SIN = cos'
    %2 = cos'

? SIN(Pi/6) \\ numerical derivation

    %3 = -0.5000000000000000000000000000

? cos'(Pi/6) \\ works directly: no need for intermediate SIN

    %4 = -0.5000000000000000000000000000

~ (unary, postfix): vector/matrix transpose.

! (unary, postfix): factorial. x! = x(x − 1) ⋯ 1.

! (unary, prefix): logical not. !x returns 1 if x is equal to 0 (specifically, if gequal0(x)==1), and 0 otherwise.

• Priority 9 # (unary, prefix): cardinality; #x returns length(x).

• Priority 8 ^: powering. This operator is right associative: 2 ^3^4 is understood as 2 ^(3^4).

• Priority 7 +, - (unary, prefix): - toggles the sign of its argument, + has no effect whatsoever.

• Priority 6 *: multiplication.

/: exact division (3/2 yields 3/2, not 1.5).

\, %: Euclidean quotient and remainder, i.e. if x = qy + r, then x\y = q, x%y = r. If x and y are scalars, then q is an integer and r satisfies 0 ≤ r < y; if x and y are polynomials, then q and r are polynomials such that deg r < deg y and the leading terms of r and x have the same sign.

\/: rounded Euclidean quotient for integers (rounded towards +∞ when the exact quotient would be a half-integer).

<<, >>: left and right binary shift. By definition, x<<n = x ∗ 2^{n} if n > 0, and truncate(x2^{−n}) otherwise. Right shift is defined by x>>n = x<<(-n).

• Priority 5 +, -: addition/subtraction.

• Priority 4 <, >, <=, >=: the usual comparison operators, returning 1 for true and 0 for false. For

instance, x<=1 returns 1 if x ≤ 1 and 0 otherwise.

<>, !=: test for (exact) inequality.

==: test for (exact) equality. t_QFR having the same coefficients but a different distance component are tested as equal.

===: test whether two objects are identical component-wise. This is stricter than ==: for instance, the integer 0, a 0 polynomial or a vector with 0 entries, are all tested equal by ==, but they are not identical.

• Priority 3 &&: logical and.

||: logical (inclusive) or. Any sequence of logical or and and operations is evaluated from left to right, and aborted as soon as the final truth value is known. Thus, for instance,

      x == 0 || test(1/x)

will never produce an error since test(1/x) is not even evaluated when the first test is true (hence the final truth value is true). Similarly

      type(p) == "t_INT" && isprime(p)

does not evaluate isprime(p) if p is not an integer.

• Priority 2 = (assignment, lvalue = expr). The result of x = y is the value of the expression y, which

is also assigned to the variable x. This assignment operator is right-associative. This is not the equality test operator; a statement like x = 1 is always true (i.e. non-zero), and sets x to 1; the equality test would be x == 1. The right hand side of the assignment operator is evaluated before the left hand side.

It is crucial that the left hand-side be an lvalue there, it avoids ambiguities in expressions like 1 + x = 1. The latter evaluates as 1 + (x = 1), not as (1 + x) = 1, even though the priority of = is lower than the priority of +: 1 + x is not an lvalue.

If the expression cannot be parsed in a way where the left hand side is an lvalue, raise an error.

? x + 1 = 1
  ***   unused characters: x+1=1
  ***                         ^--

op=, where op is any binary operator among +, -, *, %, /, \, \/, <<, or >> (composed assignment lvalue op= expr). The expression x op= y assigns (x op y) to x, and returns the new value of x. The result is not an lvalue; thus

  (x += 2) = 3

is invalid. These assignment operators are right-associative:

  ? x = 'x; x += x *= 2
  %1 = 3*x

• Priority 1 -> (function definition): (vars)->expr returns a function object, of type t_CLOSURE.

Remark. Use the op= operators as often as possible since they make complex assignments more legible: one needs not parse complicated expressions twice to make sure they are indeed identical. Compare

v[i+j-1] = v[i+j-1] + 1    -->    v[i+j-1]++
M[i,i+j] = M[i,i+j] * 2    -->    M[i,i+j] *= 2

Remark. Less important but still interesting. The ++, -- and op= operators are slightly more efficient:

? a = 10^6;
? i = 0; while(i<a, i=i+1)
time = 365 ms.
? i = 0; while(i<a, i++)
time = 352ms.

For the same reason, the shift operators should be preferred to multiplication:

? a = 1<<(10^5);
? i = 1; while(i<a, i=i*2);
time = 1,052 ms.
? i = 1; while(i<a, i<<=1);
time = 617 ms.

2.5 Variables and symbolic expressions.

In this section we use variable in the standard mathematical sense, symbols representing algebraically independent elements used to build rings of polynomials and power series, and explain the all-important concept of variable priority. In the next Section 2.6, we shall no longer consider only free variables, but adopt the viewpoint of computer programming and assign values to these symbols: (bound) variables are names associated to values in a given scope.

2.5.1 Variable names. A valid name starts with a letter, followed by any number of keyword characters: or alphanumeric characters ([A-Za-z0-9]). The built-in function names are reserved and cannot be used; see the list with \c, including the constants Pi, Euler, Catalan and I = \sqrt{−1}.

GP names are case sensitive. For instance, the symbol i is perfectly safe to use, and will not be mistaken for I = \sqrt{−1}; analogously, o is not synonymous to O.

In GP you can use up to 16383 variable names (up to 65535 on 64-bit machines). If you ever need thousands of variables and this becomes a serious limitation, you should probably be using vectors instead: e.g. instead of variables X1, X2, X3, …, you might equally well store their values

in X[1], X[2], X[3], . . .

2.5.2 Variables and polynomials. What happens when you use a valid variable name,t say, for the first time before assigning a value into it? This registers a new free variable with the interpreter (which will be written as t), and evaluates to a monomial of degree 1 in the said variable t. It is important to understand that PARI/GP is not a symbolic manipulation package: even free variables already have default values*, there is no such thing as an “unbound” variable in GP. You have access to this default value using the quote operator: 't always evaluates to the above monomial of degree 1, independently of assignments made since then (e.g. t = 1).

    ? t^2 + 1
    %1 = t^2 + 1
    ? t = 2; t^2 + 1
    %2 = 5
    ? %1
    %3 = t^2 + 1
    ? eval(%1)
    %4 = 5

In the above, t is initially a free variable, later bound to 2. We see that assigning a value to a variable does not affect previous expressions involving it; to take into account the new variable’s value, one must force a new evaluation, using the function eval (see Section 3.7.5). It is preferable to leave alone your “polynomial variables”, never assigning values to them, and to use subst and its more powerful variants rather than eval. You will avoid the following kind of problems:

    ? p = t^2 + 1; subst(p, t, 2)
    %1 = 5
    ? t = 2;

? subst(p, t, 3) \\ t is no longer free: it evaluates to 2

      ***   at top-level: subst(p,t,3)
      ***                         ^----

* More generally, any expression has a value, and is replaced by its value as soon as it is read; it never stays in an abstract form.

      ***   variable name expected.
    ? subst(p, 't, 3)   \\ OK
    %3 = 10

A statement like x = 'x in effect restores x as a free variable.

2.5.3 Variable priorities, multivariate objects. A multivariate polynomial in PARI is just a polynomial (in one variable), whose coefficients are themselves polynomials, arbitrary but for the fact that they do not involve the main variable. (PARI currently has no sparse representation for polynomials, listing only non-zero monomials.) All computations are then done formally on the coefficients as if the polynomial was univariate.

This is not symmetrical. So if I enter x + y in a clean session, what happens? This is understood as

x^{1} + (y^{1} + 0 ∗ y^{0}) ∗ x^{0} ∈ (Z[y])[x]

but how do we know that x is “more important” than y ? Why not y^{1} + x ∗ y^{0}, which is the same mathematical entity after all?

The answer is that variables are ordered implicitly by the interpreter: when a new identifier (e.g x, or y as above) is input, the corresponding variable is registered as having a strictly lower priority than any variable in use at this point*. To see the ordering used by gp at any given time,

type variable().

Given such an ordering, multivariate polynomials are stored so that the variable with the highest priority is the main variable. And so on, recursively, until all variables are exhausted. A different storage pattern (which could only be obtained via libpari programming and low-level constructors) would produce an invalid object, and eventually a disaster.

In any case, if you are working with expressions involving several variables and want to have them ordered in a specific manner in the internal representation just described, the simplest is just to write down the variables one after the other under gp before starting any real computations. You could also define variables from your gprc to have a consistent ordering of common variable names in all your gp sessions, e.g read in a file variables.gp containing

    x;y;z;t;a;b;c;d;

* This is not strictly true: the variable x is predefined and always has the highest possible priority.

Important note. PARI allows Euclidean division of multivariate polynomials, but assumes that the computation takes place in the fraction field of the coefficient ring (if it is not an integral domain, the result will a priori not make sense). This can become tricky; for instance assume x has highest priority (which is always the case), then y:

    ? x % y
    %1 = 0
    ? y % x

%2 = y \\ these two take place in Q(y)[x]

    ? x * Mod(1,y)

%3 = Mod(1, y)*x \\ in (Q(y)/yQ(y))[x] ∼ Q[x]

    ? Mod(x,y)
    %4 = 0

In the last example, the division by y takes place in Q(y)[x], hence the Mod object is a coset in (Q(y)[x])/(yQ(y)[x]), which is the null ring since y is invertible! So be very wary of variable ordering when your computations involve implicit divisions and many variables. This also affects

functions like numerator/denominator or content:
    ? denominator(x / y)
    %1 = 1
    ? denominator(y / x)
    %2 = x
    ? content(x / y)
    %3 = 1/y
    ? content(y / x)
    %4 = y
    ? content(2 / x)
    %5 = 2

Can you see why? Hint: x/y = (1/y) ∗ x is in Q(y)[x] and denominator is taken with respect to Q(y)(x); y/x = (y ∗ x^{0})/x is in Q(y)(x) so y is invertible in the coefficient ring. On the other hand, 2/x involves a single variable and the coefficient ring is simply Z.

These problems arise because the variable ordering defines an implicit variable with respect to which division takes place. This is the price to pay to allow % and / operators on polynomials instead of requiring a more cumbersome divrem(x, y, var) (which also exists). Unfortunately, in some functions like content and denominator, there is no way to set explicitly a main variable like in divrem and remove the dependence on implicit orderings. This will hopefully be corrected in future versions.

2.5.4 Multivariate power series. Just like multivariate polynomials, power series are fundamentally single-variable objects. It is awkward to handle many variables at once, since PARI’s implementation cannot handle multivariate error terms like O(x^{i}y^{j}). (It can handle the polynomial O(y^{j}) × x^{i} which is a very different thing, see below.)

The basic assumption in our model is that if variable x has higher priority than y, then y does not depend on x: setting y to a function of x after some computations with bivariate power series does not make sense a priori. This is because implicit constants in expressions like O(x^{i}) depend on y (whereas in O(y^{j}) they can not depend on x). For instance

      ? O(x) * y

      %1 = O(x)
      ? O(y) * x
      %2 = O(y)*x

Here is a more involved example:

      ? A = 1/x^2 + 1 + O(x); B = 1/x + 1 + O(x^3);
      ? subst(z*A, z, B)
      %2 = x^-3 + x^-2 + x^-1 + 1 + O(x)
      ? B * A
      %3 = x^-3 + x^-2 + x^-1 + O(1)
      ? z * A
      %4 = z*x^-2 + z + O(x)

The discrepancy between %2 and %3 is surprising. Why does %2 contain a spurious constant term, which cannot be deduced from the input? Well, we ignored the rule that forbids to substitute an expression involving high-priority variables to a low-priority variable. The result %4 is correct according to our rules since the implicit constant in O(x) may depend on z. It is obviously wrong if z is allowed to have negative valuation in x. Of course, the correct error term should be O(xz), but this is not possible in PARI.

2.6 Variables and Scope.

This section is rather technical, and strives to explain potentially confusing concepts. Skip to the last subsection for practical advice, if the next discussion does not make sense to you. After learning about user functions, study the example in Section 2.7.3 then come back.

Definitions.

A scope is an enclosing context where names and values are associated. A user’s function body, the body of a loop, an individual command line, all define scopes; the whole program defines the global scope. The argument of eval is evaluated in the enclosing scope.

Variables are bound to values within a given scope. This is traditionally implemented in two different ways:

• lexical (or static) scoping: the binding makes sense within a given block of program text. The value is private to the block and may not be accessed from outside. Where to find the value is determined at compile time.

• dynamic scoping: introducing a local variable, say x, pushes a new value on a stack associated to the name x (possibly empty at this point), which is popped out when the control flow leaves the scope. Evaluating x in any context, possibly outside of the given block, always yields the top value on this dynamic stack.

GP implements both lexical and dynamic scoping, using the keywords* my (lexical) and local (dynamic):

      x = 0;
      f() = x
      g() =    my(x = 1); f()

* The names are borrowed from the Perl scripting language.

      h() = local(x = 1); f()

The function g returns 0 since the global x binding is unaffected by the introduction of a private variable of the same name in g. On the other hand, h returns 1; when it calls f(), the binding stack for the x identifier contains two items: the global binding to 0, and the binding to 1 introduced in h, which is still present on the stack since the control flow has not left h yet.

2.6.1 Scoping rules.

Named parameters in a function definition, as well as all loop indices*, have lexical scope within the function body and the loop body respectively.

    p = 0;
    forprime (p = 2, 11, print(p)); p   \\ prints 0 at the end
    x = 0;
    f(x) = x++;
    f(1)  \\ returns 2, and leave global x unaffected (= 0)

If you exit the loop prematurely, e.g. using the break statement, you must save the loop index in another variable since its value prior the loop will be restored upon exit. For instance

      for(i = 1, n,
        if (ok(i), break);
      );
      if (i > n, return(failure));

is incorrect, since the value of i tested by the (i > n) is quite unrelated to the loop index. One ugly workaround is

      for(i = 1, n,
        if (ok(i), isave = i; break);
      );
      if (isave > n, return(failure));

But it is usually more natural to wrap the loop in a user function and use return instead of break:

    try() =
    {
      for(i = 1, n,
        if (ok(i), return (i));
      );
      0 \\ failure
    }

A list of variables can be lexically or dynamically scoped (to the block between the declaration and the end of the innermost enclosing scope) using a my or local declaration:

    for (i = 1, 10,
      my(x, y, z, i2 = i^2); \\ temps needed within the loop body
      ...
    )

* More generally, in all iterative constructs which use a variable name (for, prod, sum, vector, matrix, plot, etc.) the given variable is lexically scoped to the construct’s body.

Note how the declaration can include (optional) initial values, i2 = i^2 in the above. Variables for which no explicit default value is given in the declaration are initialized to 0. It would be more natural to initialize them to free variables, but this would break backward compatibility. To obtain this behavior, you may explicitly use the quoting operator:

    my(x = 'x, y = 'y, z = 'z);

A more complicated example:

    for (i = 1, 3,
      print("main loop");
      my(x = i);          \\ local to the outermost loop
      for (j = 1, 3,
        my (y = x^2);     \\ local to the innermost loop
        print (y + y^2);
        x++;
      )
    )

When we leave the loops, the values of x, y, i, j are the same as before they were started.

Note that eval is evaluated in the given scope, and can access values of lexical variables:

    ? x = 1;
    ? my(x = 0); eval("x")

%2 = 0 \\ we see the local x scoped to this command line, not the global one

Variables dynamically scoped using local should more appropriately be called temporary values since they are in fact local to the function declaring them and any subroutine called from within. In practice, you almost certainly want true private variables, hence should use almost exclusively my.

We strongly recommended to explicitly scope (lexically) all variables to the smallest possible block. Should you forget this, in expressions involving such “rogue” variables, the value used will be the one which happens to be on top of the value stack at the time of the call; which depends on the whole calling context in a non-trivial way. This is in general not what you want.

2.7 User defined functions.

The most important thing to understand about user-defined functions is that they are ordinary GP objects, bound to variables just like any other object. Those variables are subject to scoping rules as any other: while you can define all your functions in global scope, it is usually possible and cleaner to lexically scope your private helper functions to the block of text where they will be needed.

Whenever gp meets a construction of the form expr(argument list) and the expression expr evaluates to a function (an object of type t_CLOSURE), the function is called with the proper arguments. For instance, constructions like funcs[i](x) are perfectly valid, assuming funcs is an array of functions.

2.7.1 Defining a function.

A user function is defined as follows:

(list of formal variables) -> seq.

The list of formal variables is a comma-separated list of distinct variable names and allowed to be empty. It there is a single formal variable, the parentheses are optional. This list corresponds to the list of parameters you will supply to your function when calling it.

In most cases you want to assign a function to a variable immediately, as in

    R = (x,y) -> sqrt( x^2+y^2 );
    sq = x -> x^2;  \\ or equivalently (x) -> x^2

but it is quite possible to define (a priori short-lived) anonymous functions. The trailing semicolon is not part of the definition, but as usual prevents gp from printing the result of the evaluation, i.e. the function object. The construction

f(list of formal variables) = seq

is available as an alias for

f = (list of formal variables) -> seq

Using that syntax, it is not possible to define anonymous functions (obviously), and the above two examples become:

    R(x,y) = sqrt( x^2+y^2 );
    sq(x) = x^2;

The semicolon serves the same purpose as above: preventing the printing of the resulting function object; compare

    ? sq(x) = x^2;  \\ no output

? sq(x) = x^2 \\ print the result: a function object

    %2 = (x)->x^2

Of course, the sequence seq can be arbitrarily complicated, in which case it will look better written on consecutive lines, with properly scoped variables:

    {

f(x_{0}, x_{1}, …) = my(t_{0}, t_{1}, …); \\ variables lexically scoped to the function body …

    }

Note that the following variant would also work:

f(x_{0}, x_{1}, …) =

    {

my(t_{0}, t_{1}, …); \\ variables lexically scoped to the function body …

    }

(the first newline is disregarded due to the preceding = sign, and the others because of the enclosing braces). The my statements can actually occur anywhere within the function body, scoping the variables to more restricted blocks than the whole function body.

Arguments are passed by value, not as variables: modifying a function’s argument in the function body is allowed, but does not modify its value in the calling scope. In fact, a copy of the actual parameter is assigned to the formal parameter when the function is called. Formal parameters are lexically scoped to the function body. It is not allowed to use the same variable name for different parameters of your function:

    ? f(x,x) = 1
      ***   variable declared twice: f(x,x)=1
      ***                                ^----

Finishing touch. You can add a specific help message for your function using addhelp, but the online help system already handles it. By default ?name will print the definition of the function name: the list of arguments, as well as their default values, the text of seq as you input it. Just as \c prints the list of all built-in commands, \u outputs the list of all user-defined functions.

Backward compatibility (lexical scope). Lexically scoped variables were introduced in version 2.4.2. Before that, the formal parameters were dynamically scoped. If your script depends on this behavior, you may use the following trick: replace the initial f(x) = by

    f(x_orig) = local(x = x_orig)

Backward compatibility (disjoint namespaces). Before version 2.4.2, variables and functions lived in disjoint namespaces and it was not possible to have a variable and a function share the same name. Hence the need for a kill function allowing to reuse symbols. This is no longer the case.

There is now no distinction between variable and function names: we have PARI objects (functions of type t_CLOSURE, or more mundane mathematical entities, like t_INT, etc.) and variables bound to them. There is nothing wrong with the following sequence of assignments:

? f = 1 \\ assigns the integer 1 to f

    %1 = 1;

? f() = 1 \\ a function with a constant value

    %2 = ()->1

? f = x^2 \\ f now holds a polynomial

    %3 = x^2

? f(x) = x^2 \\ … and now a polynomial function

    %4 = (x)->x^2

? g(fun) = fun(Pi);\\ a function taking a function as argument

    ? g(cos)
    %6 = -1.000000000000000000000000000

Previously used names can be recycled as above: you are just redefining the variable. The previous definition is lost of course.

Important technical note. Built-in functions are a special case since they are read-only (you cannot overwrite their default meaning), and they use features not available to user functions, in particular pointer arguments. In the present version 2.7.2, it is possible to assign a built-in function to a variable, or to use a built-in function name to create an anonymous function, but some special argument combinations may not be available:

    ? issquare(9, &e)
    %1 = 1
    ? e
    %2 = 3
    ? g = issquare;
    ? g(9)
    %4 = 1

? g(9, &e) \\ pointers are not implemented for user functions

      ***   unexpected &: g(9,&e)
      ***                     ^---

2.7.2 Function call, Default arguments.

You may now call your function, as in f(1,2), supplying values for the formal variables. The number of parameters actually supplied may be less than the number of formal variables in the function definition. An uninitialized formal variable is given an implicit default value of (the integer) 0, i.e. after the definition

    f(x, y) = ...

you may call f(1, 2), supplying values for the two formal parameters, or for example

f(2)  equivalent to  f(2,0),

    f()                      f(0,0),

f(,3)                    f(0,3). (“Empty argument” trick)

This implicit default value of 0, is actually deprecated and setting

      default(strictargs, 1)

allows to disable it (see Section 3.14.39).

The recommended practice is to explicitly set a default value: in the function definition, you can append =expr to a formal parameter, to give that variable a default value. The expression gets evaluated the moment the function is called, and may involve the preceding function parameters: a default value for x_{i} may involve x_{j} for j < i. For instance, after

    f(x = 1, y = 2, z = y+1) = ....

typing in f(3,4) would give you f(3,4,5). In the rare case when you want to set some far away argument, and leave the defaults in between as they stand, use the “empty argument” trick: f(6,,1) would yield f(6,2,1). Of course, f() by itself yields f(1,2,3) as was to be expected.

In short, the argument list is filled with user supplied values, in order. A comma or closing parenthesis, where a value should have been, signals we must use a default value. When no input arguments are left, the defaults are used instead to fill in remaining formal parameters. A final example:

    f(x, y=2, z=3) = print(x, ":", y, ":", z);

defines a function which prints its arguments (at most three of them), separated by colons.

? f(6,7)
6:7:3
? f(,5)
0:5:3
? f()
0:2:3

If strictargs is set (recommended), x is now a mandatory argument, and the above becomes:

? default(strictargs,1)
? f(6,7)
6:7:3
? f(,5)
  ***   at top-level: f(,5)
  ***                 ^-----
  ***   in function f: x,y=2,z=3
  ***                  ^---------
  ***   missing mandatory argument 'x' in user function.

Example. We conclude with an amusing example, intended to illustrate both user-defined functions and the power of the sumalt function. Although the Riemann zeta-function is included (as zeta) among the standard functions, let us assume that we want to check other implementations. Since we are highly interested in the critical strip, we use the classical formula

(2^{1−s} − 1)ζ(s) = ∑_{n≥1}(−1)^{n}n^{−s}, ℜs > 0.

The implementation is obvious:

ZETA(s) = sumalt(n=1, (-1)^n*n^(-s)) / (2^(1-s) - 1)

Note that n is automatically lexically scoped to the sumalt “loop”, so that it is unnecessary to add a my(n) declaration to the function body. Surprisingly, this gives very good accuracy in a larger region than expected:

? check = z -> ZETA(z) / zeta(z);
? check(2)
%1 = 1.000000000000000000000000000
? check(200)
%2 = 1.000000000000000000000000000
? check(0)
%3 = 0.9999999999999999999999999994
? check(-5)
%4 = 1.00000000000000007549266557
? check(-11)
%5 = 0.9999752641047824902660847745

? check(1/2+14.134*I) \\ very close to a non-trivial zero

%6 = 1.000000000000000000003747432 + 7.62329066 E-21*I
? check(-1+10*I)
%7 = 1.000000000000000000000002511 + 2.989950968 E-24*I

Now wait a minute; not only are we summing a series which is certainly no longer alternating (it has complex coefficients), but we are also way outside of the region of convergence, and still get decent results! No programming mistake this time: sumalt is a “magic” function*, providing very good convergence acceleration; in effect, we are computing the analytic continuation of our original function. To convince ourselves that sumalt is a non-trivial implementation, let us try a simpler example:

    ? sum(n=1, 10^7, (-1)^n/n, 0.) / (-log(2)) \\ approximates the well-known formula
    time = 7,417 ms.
    %1 = 0.9999999278652515622893405457
    ? sumalt(n=1, (-1)^n/n) / (-log(2))        \\ accurate and fast
    time = 0 ms.
    %2 = 1.000000000000000000000000000

No, we are not using a powerful simplification tool here, only numerical computations. Remember, PARI is not a computer algebra system!

2.7.3 Beware scopes. Be extra careful with the scopes of variables. What is wrong with the following definition?

    FirstPrimeDiv(x) =
    { my(p);
      forprime(p=2, x, if (x%p == 0, break));
      p
    }
    ? FirstPrimeDiv(10)
    %1 = 0

Hint. The function body is equivalent to

    { my(newp = 0);
      forprime(p=2, x, if (x%p == 0, break));
      newp
    }

Detailed explanation. The index p in the forprime loop is lexically scoped to the loop and is not visible to the outside world. Hence, it will not survive the break statement. More precisely, at this point the loop index is restored to its preceding value. The initial my(p), although wellmeant, adds to the confusion: it indeed scopes p to the function body, with initial value 0, but the forprime loop introduces another variable, unfortunately also called p, scoped to the loop body, which shadows the one we wanted. So we always return 0, since the value of the p scoped to the function body never changes and is initially 0.

To sum up, the routine returns the p declared local to it, not the one which was local to forprime and ran through consecutive prime numbers. Here is a corrected version:

    ? FirstPrimeDiv(x) = forprime(p=2, x, if (x%p == 0, return(p)))

* sumalt is heuristic, but its use can be rigorously justified for a given function, in particular our ζ(s) formula. Indeed, Peter Borwein (An efficient algorithm for the Riemann zeta function, CMS Conf. Proc. 27 (2000), pp. 29–34) proved that the formula used in sumalt with n terms computes (1 − 2^{1−s})ζ(s) with a relative error of the order of (3 + \sqrt{8})^{−n}|Γ(s)|^{−1}.

2.7.4 Recursive functions. Recursive functions can easily be written as long as one pays proper attention to variable scope. Here is an example, used to retrieve the coefficient array of a multivariate polynomial (a non-trivial task due to PARI’s unsophisticated representation for those objects):

    coeffs(P, nbvar) =
    {
      if (type(P) != "t_POL",
        for (i=1, nbvar, P = [P]);
        return (P)
      );
      vector(poldegree(P)+1, i, coeffs(polcoeff(P, i-1), nbvar-1))
    }

If P is a polynomial in k variables, show that after the assignment v = coeffs(P,k), the coefficient of x_{1}^{n_{1}} … x_{k}^{n_{k}} in P is given by v[n_{1}+1][…][n_{k}+1].

The operating system automatically limits the recursion depth:

    ? dive(n) = dive(n+1)
    ? dive(0);
      ***   [...] at: dive(n+1)
      ***             ^---------
      ***   in function dive: dive(n+1)
      ***                     ^---------

\\ (last 2 lines repeated 19 times)

      ***   deep recursion.

There is no way to increase the recursion limit (which may be different on your machine) from within gp. To increase it before launching gp, you can use ulimit or limit, depending on your shell, and raise the process available stack space (increase stacksize).

2.7.5 Function which take functions as parameters. This is done as follows:

    ? calc(f, x) = f(x)
    ? calc(sin, Pi)
    %2 = -5.04870979 E-29
    ? g(x) = x^2;
    ? calc(g, 3)
    %4 = 9

If we do not need g elsewhere, we should use an anonymous function here, calc(x->x^2, 3). Here is a variation:

    ? funs = [cos, sin, tan, x->x^3+1]; \\ an array of functions
    ? call(i, x) = funs[i](x)

evaluates the appropriate function on argument x, provided 1 ≤ i ≤ 4. Finally, a more useful example:

    APPLY(f, v) = vector(#v, i, f(v[i]))

applies the function f to every element in the vector v. (The built-in function apply is more powerful since it also applies to lists and matrices.)

2.7.6 Defining functions within a function. Defining a single function is easy:

    init(x) = (add = y -> x+y);

Basically, we are defining a global variable add whose value is the function y->x+y. The parentheses were added for clarity and are not mandatory.

    ? init(5);
    ? add(2)
    %2 = 7

A more refined approach is to avoid global variables and return the function:

    init(x) = y -> x+y
    add = init(5)

Then add(2) still returns 7, as expected! Of course, if add is in global scope, there is no gain, but we can lexically scope it to the place where it is useful:

      my ( add = init(5) );

How about multiple functions then? We can use the last idea and return a vector of functions, but if we insist on global variables? The first idea

    init(x) = add(y) = x+y; mul(y) = x*y;

does not work since in the construction f() = seq, the function body contains everything until the end of the expression. Hence executing init defines the wrong function add (itself defining a function mul). The way out is to use parentheses for grouping, so that enclosed subexpressions will be evaluated independently:

    ? init(x) = ( add(y) = x+y ); ( mul(y) = x*y );
    ? init(5);
    ? add(2)
    %3 = 7
    ? mul(3)
    %4 = 15

This defines two global functions which have access to the lexical variables private to init! The following would work in exactly the same way:

    ? init5() = my(x = 5); ( add(y) = x+y ); ( mul(y) = x*y );

2.7.7 Closures as Objects. Contrary to what you might think after the preceding examples, GP’s closures may not be used to simulate true “objects”, with private and public parts and methods to access and manipulate them. In fact, closures indeed incorporate an existing context (they may access lexical variables that existed at the time of their definition), but then may not change it. More precisely, they access a copy, which they are welcome to change, but a further function call still accesses the original context, as it existed at the time the function was defined:

    init() =
    { my(count = 0);
      inc()=count++;
      dec()=count--;
    }
    ? inc()

    %1 = 1
    ? inc()
    %2 = 1
    ? inc()
    %3 = 1

2.8 Member functions.

Member functions use the ‘dot’ notation to retrieve information from complicated structures. The built-in structures are bid, ell, galois, ff , nf , bnf , bnr and prid, which will be described at length in Chapter 3. The syntax structure.member is taken to mean: retrieve member from structure, e.g. E.j returns the j-invariant of the elliptic curve E, or outputs an error message if E is not a proper ell structure. To define your own member functions, use the syntax

var.member = seq,

where the formal variable var is scoped to the function body seq. This is of course reminiscent of a user function with a single formal variable var. For instance, the current implementation of the ell type is a vector, the j-invariant being the thirteenth component. It could be implemented as

    x.j =
    {
      if (type(x) != "t_VEC" || #x < 14, error("not an elliptic curve: " x));
      x[13]
    }

As for user functions, you can redefine your member functions simply by typing new definitions. On the other hand, as a safety measure, you cannot redefine the built-in member functions, so attempting to redefine x.j as above would in fact produce an error; you would have to call it e.g. x.myj in order for gp to accept it.

Rationale. In most cases, member functions are simple accessors of the form

      x.a = x[1];
      x.b = x[2];
      x.c = x[3];

where x is a vector containing relevant data. There are at least three alternative approaches to the above member functions: 1) hardcode x[1], etc. in the program text, 2) define constant global variables AINDEX = 1, BINDEX = 2 and hardcode x[AINDEX], 3) user functions a(x) = x[1] and so on.

Even if 2) improves on 1), these solutions are neither elegant nor flexible, and they scale badly.

3) is a genuine possibility, but the main advantage of member functions is that their namespace is independent from the variables (and functions) namespace, hence we can use very short identifiers without risk. The j-invariant is a good example: it would clearly not be a good idea to define j(E) = E[13], because clashes with loop indices are likely.

Note. Typing \um will output all user-defined member functions.

Member function names. A valid name starts with a letter followed by any number of keyword characters: or alphanumeric characters ([A-Za-z0-9]). The built-in member function names are reserved and cannot be used (see the list with ?.). Finally, names starting with e or E followed by a digit are forbidden, due to a clash with the floating point exponent notation: we understand 1.e2 as 100.000 …, not as extracting member e2 of object 1.

2.9 Strings and Keywords.

2.9.1 Strings. GP variables can hold values of type character string (internal type t_STR). This section describes how they are actually used, as well as some convenient tricks (automatic concatenation and expansion, keywords) valid in string context.

As explained above, the general way to input a string is to enclose characters between quotes ". This is the only input construct where whitespace characters are significant: the string will contain the exact number of spaces you typed in. Besides, you can “escape” characters by putting a \ just before them; the translation is as follows

       \e: <Escape>
       \n: <Newline>
       \t: <Tab>

For any other character x, \x is expanded to x. In particular, the only way to put a " into a string is to escape it. Thus, for instance, "\"a\"" would produce the string whose content is “a”. This is definitely not the same thing as typing "a", whose content is merely the one-letter string a.

You can concatenate two strings using the concat function. If either argument is a string, the other is automatically converted to a string if necessary (it will be evaluated first).

    ? concat("ex", 1+1)
    %1 = "ex2"
    ? a = 2; b = "ex"; concat(b, a)
    %2 = "ex2"
    ? concat(a, b)
    %3 = "2ex"

Some functions expect strings for some of their arguments: print would be an obvious example, Str is a less obvious but useful one (see the end of this section for a complete list). While typing in such an argument, you will be said to be in string context. The rest of this section is devoted to special syntactical tricks which can be used with such arguments (and only here; you will get an error message if you try these outside of string context):

• Writing two strings alongside one another will just concatenate them, producing a longer string. Thus it is equivalent to type in "a " "b" or "a b". A little tricky point in the first expression: the first whitespace is enclosed between quotes, and so is part of a string; while the second (before the "b") is completely optional and gp actually suppresses it, as it would with any number of whitespace characters at this point (i.e. outside of any string).

• If you insert any expression when a string is expected, it gets “expanded”: it is evaluated as a standard GP expression, and the final result (as would have been printed if you had typed it by itself) is then converted to a string, as if you had typed it directly. For instance "a" 1+1 "b" is equivalent to "a2b": three strings get created, the middle one being the expansion of 1+1, and these are then concatenated according to the rule described above. Another tricky point here: assume you did not assign a value to aaa in a GP expression before. Then typing aaa by itself in a string context will actually produce the correct output (i.e. the string whose content is aaa), but in a fortuitous way. This aaa gets expanded to the monomial of degree one in the variable aaa, which is of course printed as aaa, and thus will expand to the three letters you were expecting.

Warning. Expression involving strings are not handled in a special way; even in string context, the largest possible expression is evaluated, hence print("a"[1]) is incorrect since "a" is not an object whose first component can be extracted. On the other hand print("a", [1]) is correct (two distinct argument, each converted to a string), and so is print("a" 1) (since "a"1 is not a valid expression, only "a" gets expanded, then 1, and the result is concatenated as explained above).

2.9.2 Keywords. Since there are cases where expansion is not desirable, we now distinguish between “Keywords” and “Strings”. String is what has been described so far. Keywords are special relatives of Strings which are automatically assumed to be quoted, whether you actually type in the quotes or not. Thus expansion is never performed on them. They get concatenated, though. The analyzer supplies automatically the quotes you have “forgotten” and treats Keywords just as normal strings otherwise. For instance, if you type "a"b+b in Keyword context, you will get the string whose contents are ab+b. In String context, on the other hand, you would get a2*b.

All GP functions have prototypes (described in Chapter 3 below) which specify the types of arguments they expect: either generic PARI objects (GEN), or strings, or keywords, or unevaluated expression sequences. In the keyword case, only a very small set of words will actually be meaningful (the default function is a prominent example).

Reference. The arguments of the following functions are processed in string context:

Str

addhelp (second argument) default (second argument)

error
extern

plotstring (second argument) plotterm (first argument)

read and readvec
system

all the printxxx functions all the writexxx functions

The arguments of the following functions are processed as keywords:

alias

default (first argument) install (all arguments but the last) trap (first argument)

whatnow

2.9.3 Useful examples. The function Str converts its arguments into strings and concatenate them. Coupled with eval, it is very powerful. The following example creates generic matrices:

? genmat(u,v,s="x") = matrix(u,v,i,j, eval( Str(s,i,j) ))
? genmat(2,3) + genmat(2,3,"m")
%1 =
[x11 + m11 x12 + m12 x13 + m13]
[x21 + m21 x22 + m22 x23 + m23]

Two last examples: hist(10,20) returns all history entries from %10 to %20 neatly packed into a single vector; histlast(10) returns the last 10 history entries:

      hist(a,b) = vector(b-a+1, i, eval(Str("%", a-1+i)))
      histlast(n) = vector(n, i, eval(Str("%", %#-i+1)))

2.10 Errors and error recovery.

2.10.1 Errors. Your input program is first compiled to a more efficient bytecode; then the latter is evaluated, calling appropriate functions from the PARI library. Accordingly, there are two kind of errors: syntax errors produced by the compiler, and runtime errors produced by the PARI library either by the evaluator itself, or in a mathematical function. Both kinds are fatal to your computation: gp will report the error, perform some cleanup (restore variables modified while evaluating the erroneous command, close open files, reclaim unused memory, etc.), and output its usual prompt.

When reporting a syntax error, gp gives meaningful context by copying (part of) the expression it was trying to compile, indicating where the error occurred with a caret ^-, as in

    ? factor()
      ***   too few arguments: factor()
      ***                             ^-
    ? 1+
      ***   syntax error, unexpected $end: 1+
      ***                                   ^-

possibly enlarged to a full arrow given enough trailing context

    ? if (isprime(1+, do_something())
      ***   syntax error, unexpected ',': if(isprime(1+,do_something()))
      ***                                              ^----------------

These error messages may be mysterious, because gp cannot guess what you were trying to do, and the error may occur once gp has been sidetracked. The first error is straightforward: factor has one mandatory argument, which is missing.

The other two are simple typos involving an ill-formed addition 1 + missing its second operand. The error messages differ because the parsing context is slightly different: in the first case we reach the end of input ($end) while still expecting a token, and in the second one, we received an unexpected token (the comma).

Here is a more complicated one:

    ? factor(x
      ***   syntax error, unexpected $end, expecting )-> or ',' or ')': factor(x
      ***                                                                      ^-

The error is a missing parenthesis, but from gp’s point of view, you might as well have intended to give further arguments to factor (this is possible and useful, see the description of the function). In fact gp expected either a closing parenthesis, or a second argument separated from the first by a comma. And this is essentially what the error message says: we reached the end of the input ($end) while expecting a ')' or a ','.

Actually, a third possibility is mentioned in the error message )->, which could never be valid in the above context, but a subexpression like (x)->sin(x), defining an inline closure would be valid, and the parser is not clever enough to rule that out, so we get the same message as in

? (x
***   syntax error, unexpected $end, expecting )-> or ',' or ')': (x
***                                                                ^-

where all three proposed continuations would be valid.

Runtime errors from the evaluator are nicer because they answer a correctly worded query, otherwise the bytecode compiler would have protested first; here is a slightly pathological case:

? if (siN(x) < eps, do_something())
***   at top-level: if(siN(x)<eps,do_someth
***                    ^--------------------
***   not a function in function call

(no arrow!) The code is syntactically correct and compiled correctly, even though the siN function, a typo for sin, was not defined at this point. When trying to evaluate the bytecode, however, it turned out that siN is still undefined so we cannot evaluate the function call siN(x).

Library runtime errors are even nicer because they have more mathematical content, which is easier to grasp than a parser’s logic:

? 1/Mod(2,4)
***   at top-level: 1/Mod(2,4)
***                  ^---------
*** _/_: impossible inverse in Fp_inv: Mod(2, 4).

telling us that a runtime error occurred while evaluating the binary / operator (the surrounding the operator are placeholders), more precisely the Fp inv library function was fed the argument Mod(2,4) and could not invert it. More context is provided if the error occurs deep in the call chain:

? f(x) = 1/x;
? g(N) = for(i = -N, N, f(i + O(5)));
? g(10)
***   at top-level: g(10)
***                 ^-----
***   in function g: for(i=-N,N,f(i))
***                             ^-----
***   in function f: 1/x
***                   ^--
*** _/_: impossible inverse in ginv: O(5).

In this example, the debugger reports (at least) 3 enclosed frames: last (innermost) is the body of user function f, the body of g, and the top-level (global scope). In fact, the for loop in g’s body defines an extra frame, since there exist variables scoped to the loop body.

2.10.2 Error recovery.

It is annoying to wait for a program to finish and find out the hard way that there was a mistake in it (like the division by 0 above), sending you back to the prompt. First you may lose some valuable intermediate data. Also, correcting the error may not be obvious; you might have to change your program, adding a number of extra statements and tests to narrow down the problem.

A different situation, still related to error recovery, is when you actually foresee that some error may occur, are unable to prevent it, but quite capable of recovering from it, given the chance. Examples include lazy factorization, where you knowingly use a pseudo prime N as if it were prime; you may then encounter an “impossible” situation, but this would usually exhibit a factor of N, enabling you to refine the factorization and go on. Or you might run an expensive computation at low precision to guess the size of the output, hence the right precision to use. You can then encounter errors like “precision loss in truncation”, e.g when trying to convert 1E1000, known to 28 digits of accuracy, to an integer; or “division by 0”, e.g inverting 0E1000 when all accuracy has been lost, and no significant digit remains. It would be enough to restart part of the computation at a slightly higher precision.

We now describe error trapping, a useful mechanism which alleviates much of the pain in the first situation (the break loop debugger), and provides satisfactory ways out of the second one (the iferr exception handler).

2.10.3 Break loop.

A break loop is a special debugging mode that you enter whenever a user interrupt (Control-C) or runtime error occurs, freezing the gp state, and preventing cleanup until you get out of the loop. By runtime error, we mean an error from the evaluator, the library or a user error (from error), not syntax errors. When a break loop starts, a prompt is issued (break>). You can type in a gp command, which is evaluated when you hit the <Return> key, and the result is printed as during the main gp loop, except that no history of results is kept. Then the break loop prompt reappears and you can type further commands as long as you do not exit the loop. If you are using readline, the history of commands is kept, and line editing is available as usual. If you type in a command that results in an error, you are sent back to the break loop prompt: errors do not terminate the loop.

To get out of a break loop, you can use next, break, return, or type C-d (EOF), any of which will let gp perform its usual cleanup, and send you back to the gp prompt. Note that C-d is slightly dangerous, since typing it twice will not only send you back to the gp prompt, but to your shell prompt! (Since C-d at the gp prompt exits the gp session.)

If the break loop was started by a user interrupt Control-C, and not by an error, inputting an empty line, i.e hitting the <Return> key at the break> prompt, resumes the temporarily interrupted computation. A single empty line has no effect in case of a fatal error, to avoid getting get out of the loop prematurely, thereby losing valuable debugging data. Any of next, break, return, or C-d will abort the computation and send you back to the gp prompt as above.

Break loops are useful as a debugging tool. You may inspect the values of gp variables to understand why an error occurred, or change gp’s state in the middle of a computation (increase debugging level, start storing results in a log file, set variables to different values…): hit C-c, type in your modifications, then let the computation go on as explained above. A break loop looks like this:

    ? v = 0; 1/v

***   at top-level: v=0;1/v
***                      ^--
*** _/_: impossible inverse in gdiv: 0.
***   Break loop (type 'break' to go back to the GP prompt)
break>

So the standard error message is printed first. The break> at the bottom is a prompt, and hitting v then <Return>, we see:

break> v
0

explaining the problem. We could have typed any gp command, not only the name of a variable, of course. Lexically-scoped variables are accessible to the evaluator during the break loop:

? for(v = -2, 2, print(1/v))
-1/2
-1
***   at top-level: for(v=-2,2,print(1/v))
***                                   ^----
*** _/_: impossible inverse in gdiv: 0.
***   Break loop (type 'break' to go back to the GP prompt)
break> v
0

Even though loop indices are automatically lexically scoped and no longer exist when the break loop is run, enough debugging information is retained in the bytecode to reconstruct the evaluation context. Of course, when the error occurs in a nested chain of user function calls, lexically scoped variables are available only in the corresponding frame:

? f(x) = 1/x;
? g(x) = for(i = 1, 10, f(x+i));
? for(j = -5,5, g(j))
***   at top-level: for(j=-5,5,g(j))
***                            ^-----
***   in function g: for(i=1,10,f(x+i))
***                             ^-------
***   in function f: 1/x
***                   ^--
*** _/_: impossible inverse in gdiv: 0.
***   Break loop: type 'break' to go back to GP prompt
break> [i,j,x]     \\ the x in f’s body.
[i, j, 0]
break> dbg_up      \\ go up one frame
***   at top-level: for(j=-5,5,g(j))
***                            ^-----
***   in function g: for(i=1,10,f(x+i))
***                             ^-------

break> [i,j,x] \\ the x in g’s body, i in the for loop.

[5, j, -5]

The following GP commands are available during a break loop to help debugging: dbg_up(n): go up n frames, as seen above.

dbg_down(n): go down n frames, cancelling previous dbg up’s.

dbg_x(t): examine t, as \x but more flexible.

dbg_err(): returns the current error context t_ERROR. The error components often provide useful additional information:

      ? O(2) + O(3)
        ***   at top-level: O(2)+O(3)
        ***                     ^-----
        *** _+_: inconsistent addition t_PADIC + t_PADIC.
        ***   Break loop: type 'break' to go back to GP prompt
      break> E = dbg_err()
      error("inconsistent addition t_PADIC + t_PADIC.")
      break> Vec(E)
      ["e_OP", "+", O(2), O(3)]

Note. The debugger is enabled by default, and fires up as soon as a runtime error occurs. If you do not like this behavior, you may disable it by setting the default breakloop to 0 in for gprc. A runtime error will send you back to the prompt. Note that the break loop is automatically disabled when running gp in non interactive mode, i.e. when the program’s standard input is not attached to a terminal.

Technical Note. When you enter a break loop due to a PARI stack overflow, the PARI stack is reset so that you can run commands. Otherwise the stack would immediately overflow again! Still, as explained above, you do not lose the value of any gp variable in the process.

2.10.4 Protecting code. The expression

iferr(statements, ERR, recovery)

evaluates and returns the value of statements, unless an error occurs during the evaluation in which case the value of recovery is returned. As in an if/else clause, with the difference that statements has been partially evaluated, with possible side effects. We shall give a lot more details about the ERR argument shortly; it is the name of a variable, lexically scoped to the recovery expression sequence, whose value is set by the exception handler to help the recovery code decide what to do about the error.

For instance one can define a fault tolerant inversion function as follows:

    ? inv(x) = iferr(1/x, ERR, "oo")    \\ ERR is unused...
    ? for (i=-1,1, print(inv(i)))
    -1
    oo
    1

Protected codes can be nested without adverse effect. Let’s now see how ERR can be used; as written, inv is too tolerant:

    ? inv("blah")
    %2 = "oo"

Let’s improve it by checking that we caught a “division by 0” exception, and not an unrelated one like the type error 1 / "blah".

    ? inv2(x) = {
      iferr(1/x,
            ERR, if (errname(ERR) != "e_INV", error(ERR)); "oo")
    }
    ? inv2(0)
    %3 = "oo"  \\ as before
    ? inv2("blah")
      ***   at top-level: inv2("blah")
      ***                 ^------------
      ***   in function inv2: ...f(errname(ERR)!="e_INV",error(ERR));"oo")
      ***                                                 ^-----------------
      *** error: forbidden division t_INT / t_STR.

In the inv2("blah") example, the error type was not expected, so we rethrow the exception: error(ERR) triggers the original error that we mistakenly trapped. Since the recovery code should always check whether the error is the one expected, this construction is very common and can be simplified to

    ? inv3(x) = iferr(1/x,
                      ERR, "oo",
                      errname(ERR) == "e_INV")

More generally

iferr(statements, ERR, recovery, predicate)

only catches the exception if predicate (allowed to check various things about ERR, not only its name) is non-zero.

Rather than trapping everything, then rethrowing whatever we do not like, we advise to only trap errors of a specific kind, as above. Of course, sometimes, one just want to trap everything because we do not know what to expect. The following function check whether install works correctly in your gp:

    broken_install() =
    { \\ can we install?
      iferr(install(addii,GG),
            ERR, return ("OS"));
      \\ can we use the installed function?
      iferr(if (addii(1,1) != 2, return("BROKEN")),
            ERR, return("USE"));
      return (0);
    }

The function returns OS if the operating system does not support install, USE if using an installed function triggers an error, BROKEN if the installed function did not behave as expected, and 0 if everything works.

The ERR formal parameter contains more useful data than just the error name, which we recovered using errname(ERR). In fact, a t_ERROR object usually has extra components, which can be accessed as component(ERR,1), component(ERR,2), and so on. Or globally by casting the error to a t_VEC: Vec(ERR) returns the vector of all components at once. See Section 3.11.17 for the list of all exception types, and the corresponding contents of ERR.

2.11 Interfacing GP with other languages.

The PARI library was meant to be interfaced with C programs. This specific use is dealt with extensively in the User’s guide to the PARI library. Of course, gp itself provides a convenient interpreter to execute rather intricate scripts (see Section 3.11).

Scripts, when properly written, tend to be shorter and clearer than C programs, and are certainly easier to write, maintain or debug. You don’t need to deal with memory management, garbage collection, pointers, declarations, and so on. Because of their intrinsic simplicity, they are more robust as well. They are unfortunately somewhat slower. Thus their use will remain complementary: it is suggested that you test and debug your algorithms using scripts, before actually coding them in C if speed is paramount. The GP2C compiler often eases this part.

The install command (see Section 3.12.21) efficiently imports foreign functions for use under gp, which can of course be written using other libraries than PARI. Thus you may code only critical parts of your program in C, and still maintain most of the program as a GP script.

We are aware of three PARI-related Free Software packages to embed PARI in other languages. We neither endorse nor support any of them, but you may want to give them a try if you are familiar with the languages they are based on. The first is William Stein’s Python-based SAGE* system. The second is the Math::Pari Perl module (see any CPAN mirror), written by Ilya Zakharevich. Finally, Michael Stoll has integrated PARI into CLISP***, which is a Common Lisp implementation by Bruno Haible, Marcus Daniels and others; this interface has been updated for pari-2 by Sam Steingold.

These provide interfaces to gp functions for use in python, perl, or Lisp programs, respectively.

2.12 Defaults.

There are many internal variables in gp, defining how the system will behave in certain situations, unless a specific override has been given. Most of them are a matter of basic customization (colors, prompt) and will be set once and for all in your preferences file (see Section 2.14), but some of them are useful interactively (set timer on, increase precision, etc.).

The function used to manipulate these values is called default, which is described in Section 3.12.7. The basic syntax is

    default(def , value),

which sets the default def to value. In interactive use, most of these can be abbreviated using gp metacommands (mostly, starting with \), which we shall describe in the next section.

Here we will only describe the available defaults and how they are used. Just be aware that typing default by itself will list all of them, as well as their current values (see \d). Just after the default name, we give between parentheses the initial value when gp starts, assuming you did not tamper with factory settings using command-line switches or a gprc.

  * see http://sagemath.org/
*** see http://clisp.cons.org/

Note. The suffixes k, M or G can be appended to a value which is a numeric argument, with the effect of multiplying it by 10^{3}, 10^{6} and 10^{9} respectively. Case is not taken into account there, so for instance 30k and 30K both stand for 30000. This is mostly useful to modify or set the default parisize which typically involve a lot of trailing zeroes.

(somewhat technical) Note. As we saw in Section 2.9, the second argument to default is subject to string context expansion, which means you can use run-time values. In other words, something like

      a = 3;
      default(logfile, "file" a ".log")

logs the output in file3.log.

Some special defaults, corresponding to file names and prompts, expand further the resulting value at the time they are set. Two kinds of expansions may be performed:

• time expansion: the string is sent through the library function strftime. This means that %char combinations have a special meaning, usually related to the time and date. For instance, %H = hour (24-hour clock) and %M = minute [00,59] (on a Unix system, you can try man strftime at your shell prompt to get a complete list). This is applied to prompt, psfile, and logfile. For instance,

    default(prompt,"(%H:%M) ? ")

will prepend the time of day, in the form (hh:mm) to gp’s usual prompt.

• environment expansion: When the string contains a sequence of the form $SOMEVAR, e.g. $HOME, the environment is searched and if SOMEVAR is defined, the sequence is replaced by the corresponding value. Also the ~ symbol has the same meaning as in many shells — ~ by itself stands for your home directory, and ~user is expanded to user’s home directory. This is applied to all file names.

Available defaults are described in the reference guide, Section 3.14.

2.13 Simple metacommands.

Simple metacommands are meant as shortcuts and should not be used in GP scripts (see Section 3.11). Beware that these, as all of gp input, are case sensitive. For example, \Q is not identical to \q. In the following list, braces are used to denote optional arguments, with their default values when applicable, e.g. {n = 0} means that if n is not there, it is assumed to be 0. Whitespace (or spaces) between the metacommand and its arguments and within arguments is optional. (This can cause problems only with \w, when you insist on having a file name whose first character is a digit, and with \r or \w, if the file name itself contains a space. In such cases, just use the underlying read or write function; see Section 3.12.42).

2.13.1 ?{command}. The gp on-line help interface. If you type ?n where n is a number from 1 to 11, you will get the list of functions in Section 3.n of the manual (the list of sections being obtained by simply typing ?).

These names are in general not informative enough. More details can be obtained by typing ?function, which gives a short explanation of the function’s calling convention and effects. Of course, to have complete information, read Chapter 3 of this manual (the source code is at your disposal as well, though a trifle less readable).

If the line before the copyright message indicates that extended help is available (this means perl is present on your system and the PARI distribution was correctly installed), you can add more ? signs for extended functionality:

?? keyword yields the function description as it stands in this manual, usually in Chapter 2 or 3. If you’re not satisfied with the default chapter chosen, you can impose a given chapter by ending the keyword with @ followed by the chapter number, e.g. ?? Hello@2 will look in Chapter 2 for section heading Hello (which doesn’t exist, by the way).

All operators (e.g. +, &&, etc.) are accepted by this extended help, as well as a few other keywords describing key gp concepts, e.g. readline (the line editor), integer, nf (“number field” as used in most algebraic number theory computations), ell (elliptic curves), etc.

In case of conflicts between function and default names (e.g log, simplify), the function has higher priority. To get the default help, use

      ?? default(log)
      ?? default(simplify)

??? pattern produces a list of sections in Chapter 3 of the manual related to your query. As before, if pattern ends by @ followed by a chapter number, that chapter is searched instead; you also have the option to append a simple @ (without a chapter number) to browse through the whole manual.

If your query contains dangerous characters (e.g ? or blanks) it is advisable to enclose it within double quotes, as for GP strings (e.g ??? "elliptic curve").

Note that extended help is much more powerful than the short help, since it knows about operators as well: you can type ?? * or ?? &&, whereas a single ? would just yield a not too helpful

    &&: unknown identifier.}

message. Also, you can ask for extended help on section number n in Chapter 3, just by typing ?? n (where ?n would yield merely a list of functions). Finally, a few key concepts in gp are documented in this way: metacommands (e.g ?? "??"), defaults (e.g ?? psfile) and type names (e.g t_INT or integer), as well as various miscellaneous keywords such as edit (short summary of

line editor commands), operator, member, "user defined", nf, ell, . . .

Last but not least: ?? without argument will open a dvi previewer (xdvi by default, $GPXDVI if it is defined in your environment) containing the full user’s manual. ??tutorial and ??refcard do the same with the tutorial and reference card respectively.

Technical note. This functionality is provided by an external perl script that you are free to use outside any gp session (and modify to your liking, if you are perl-knowledgeable). It is called gphelp, lies in the doc subdirectory of your distribution (just make sure you run Configure first, see Appendix A) and is really two programs in one. The one which is used from within gp is gphelp which runs TEX on a selected part of this manual, then opens a previewer. gphelp -detex is a text mode equivalent, which looks often nicer especially on a colour-capable terminal (see misc/gprc.dft for examples). The default help selects which help program will be used from within gp. You are welcome to improve this help script, or write new ones (and we would like to know about it so that we may include them in future distributions). By the way, outside of gp you can give more than one keyword as argument to gphelp.

2.13.2 /*...*/. A comment. Everything between the stars is ignored by gp. These comments can span any number of lines.

2.13.3 \\. A one-line comment. The rest of the line is ignored by gp.

2.13.4 \a {n}. Prints the object number n (%n) in raw format. If the number n is omitted, print the latest computed object (%).

2.13.5 \c. Prints the list of all available hardcoded functions under gp, not including operators written as special symbols (see Section 2.4). More information can be obtained using the ? metacommand (see above). For user-defined functions / member functions, see \u and \um.

2.13.6 \d. Prints the defaults as described in the previous section (shortcut for default(), see Section 3.12.7).

2.13.7 \e {n}. Switches the echo mode on (1) or off (0). If n is explicitly given, set echo to n.

2.13.8 \g {n}. Sets the debugging level debug to the non-negative integer n.

2.13.9 \gf {n}. Sets the file usage debugging level debugfiles to the non-negative integer n.

2.13.10 \gm {n}. Sets the memory debugging level debugmem to the non-negative integer n.

2.13.11 \h {m-n}. Outputs some debugging info about the hashtable. If the argument is a number n, outputs the contents of cell n. Ranges can be given in the form m-n (from cell m to cell n, $ = last cell). If a function name is given instead of a number or range, outputs info on the internal structure of the hash cell this function occupies (a struct entree in C). If the range is reduced to a dash (’-’), outputs statistics about hash cell usage.

2.13.12 \l {logfile}. Switches log mode on and off. If a logfile argument is given, change the default logfile name to logfile and switch log mode on.

2.13.13 \m. As \a, but using prettymatrix format.

2.13.14 \o {n}. Sets output mode to n (0: raw, 1: prettymatrix, 3: external prettyprint).

2.13.15 \p {n}. Sets realprecision to n decimal digits. Prints its current value if n is omitted.

2.13.16 \ps {n}. Sets seriesprecision to n significant terms. Prints its current value if n is omitted.

2.13.17 \q. Quits the gp session and returns to the system. Shortcut for quit() (see Section 3.12.29).

2.13.18 \r {filename}. Reads into gp all the commands contained in the named file as if they had been typed from the keyboard, one line after the other. Can be used in combination with the \w command (see below). Related but not equivalent to the function read (see Section 3.12.30); in particular, if the file contains more than one line of input, there will be one history entry for each of them, whereas read would only record the last one. If filename is omitted, re-read the previously used input file (fails if no file has ever been successfully read in the current session). If a gpbinary file (see Section 3.12.44) is read using this command, it is silently loaded, without cluttering the history.

Assuming gp figures how to decompress files on your machine, this command accepts compressed files in compressed (.Z) or gzipped (.gz or .z) format. They will be uncompressed on the fly as gp reads them, without changing the files themselves.

2.13.19 \s. Prints the state of the PARI stack and heap. This is used primarily as a debugging device for PARI.

2.13.20 \t. Prints the internal longword format of all the PARI types. The detailed bit or byte format of the initial codeword(s) is explained in Chapter 4, but its knowledge is not necessary for a gp user.

2.13.21 \u. Prints the definitions of all user-defined functions.

2.13.22 \um. Prints the definitions of all user-defined member functions.

2.13.23 \v. Prints the version number and implementation architecture (680x0, Sparc, Alpha, other) of the gp executable you are using.

2.13.24 \w {n} {filename}. Writes the object number n ( %n ) into the named file, in raw format. If the number n is omitted, writes the latest computed object ( % ). If filename is omitted, appends to logfile (the GP function write is a trifle more powerful, as you can have arbitrary file names).

2.13.25 \x {n}. Prints the complete tree with addresses and contents (in hexadecimal) of the internal representation of the object number n ( %n ). If the number n is omitted, uses the latest computed object in gp. As for \s, this is used primarily as a debugging device for PARI, and the format should be self-explanatory. The underlying GP function dbg_x is more versatile, since it can be applied to other objects than history entries.

2.13.26 \y {n}. Switches simplify on (1) or off (0). If n is explicitly given, set simplify to n.

2.13.27 #. Switches the timer on or off.

2.13.28 ##. Prints the time taken by the latest computation. Useful when you forgot to turn on

the timer.

2.14 The preferences file.

This file, called gprc in the sequel, is used to modify or extend gp default behavior, in all gp sessions: e.g customize default values or load common user functions and aliases. gp opens the gprc file and processes the commands in there, before doing anything else, e.g. creating the PARI stack. If the file does not exist or cannot be read, gp will proceed to the initialization phase at once, eventually emitting a prompt. If any explicit command line switches are given, they override the values read from the preferences file.

2.14.1 Syntax. The syntax in the gprc file (and valid in this file only) is simple-minded, but should be sufficient for most purposes. The file is read line by line; as usual, white space is ignored unless surrounded by quotes and the standard multiline constructions using braces, \, or = are available (multiline comments between /* … */ are also recognized).

2.14.1.1 Preprocessor:. Two types of lines are first dealt with by a preprocessor:

• comments are removed. This applies to all text surrounded by /* … */ as well as to everything following \\ on a given line.

• lines starting with #if boolean are treated as comments if boolean evaluates to false, and read normally otherwise. The condition can be negated using either #if not (or #if !). If the rest of the current line is empty, the test applies to the next line (same behavior as = under gp). Only three tests can be performed:

EMACS: true if gp is running in an Emacs or TeXmacs shell (see Section 2.16).

READL: true if gp is compiled with readline support (see Section 2.15).

VERSION op number: where op is in the set {>, <, <=, >=}, and number is a PARI version number of the form Major.Minor.patch, where the last two components can be omitted (i.e. 1 is understood as version 1.0.0). This is true if gp’s version number satisfies the required inequality.

2.14.1.2 Commands:. After preprocessing, the remaining lines are executed as sequence of expressions (as usual, separated by ; if necessary). Only two kinds of expressions are recognized:

• default = value, where default is one of the available defaults (see Section 2.12), which will be set to value on actual startup. Don’t forget the quotes around strings (e.g. for prompt or help).

• read "some GP file" where some GP file is a regular GP script this time, which will be read just before gp prompts you for commands, but after initializing the defaults. In particular, file input is delayed until the gprc has been fully loaded. This is the right place to input files containing alias commands, or your favorite macros.

For instance you could set your prompt in the following portable way:

    \\ self modifying prompt looking like (18:03) gp >
    prompt   = "(%H:%M) \e[1mgp\e[m > "
    \\ readline wants non-printing characters to be braced between ^A/^B pairs
    #if READL prompt = "(%H:%M) ^A\e[1m^Bgp^A\e[m^B > "
    \\ escape sequences not supported under emacs
    #if EMACS prompt = "(%H:%M) gp > "

Note that any of the last two lines could be broken in the following way

    #if EMACS

      prompt = "(%H:%M) gp > "

since the preprocessor directive applies to the next line if the current one is empty.

A sample gprc file called misc/gprc.dft is provided in the standard distribution. It is a good idea to have a look at it and customize it to your needs. Since this file does not use multiline constructs, here is one (note the terminating ; to separate the expressions):

    #if VERSION > 2.2.3
    {
      read "my_scripts";     \\ syntax errors in older versions
      new_galois_format = 1; \\ default introduced in 2.2.4
    }
    #if ! EMACS
    {
      colors = "9, 5, no, no, 4, 1, 2";
      help   = "gphelp -detex -ch 4 -cb 0 -cu 2";
    }

2.14.2 The gprc location. When gp is started, it looks for a customization file, or gprc in the following places (in this order, only the first one found will be loaded):

• gp checks whether the environment variable GPRC is set. On Unix, this can be done with something like:

    GPRC=/my/dir/anyname; export GPRC in sh syntax (for instance in your .profile),
    setenv GPRC /my/dir/anyname  in csh syntax (in your .login or .cshrc file).
    env GPRC=/my/dir/anyname gp  on the command line launching gp.

If so, the file named by $GPRC is the gprc.

• If GPRC is not set, and if the environment variable HOME is defined, gp then tries $HOME/.gprc on a Unix system $HOME\gprc.txt on a DOS, OS/2, or Windows system.

• If no gprc was found among the user files mentioned above we look for /etc/gprc for a systemwide gprc file (you will need root privileges to set up such a file yourself).

• Finally, we look in pari’s datadir for a file named .gprc on a Unix system gprc.txt on a DOS, OS/2, or Windows system. If you are using our Windows installer, this

is where the default preferences file is written. Note that on Unix systems, the gprc’s default name starts with a ’.’ and thus is hidden to regular ls commands; you need to type ls -a to list it.

2.15 Using readline.

This very useful library provides line editing and contextual completion to gp. You are encouraged to read the readline user manual, but we describe basic usage here.

A (too) short introduction to readline. In the following, C- stands for “the Control key combined with another” and the same for M- with the Meta key; generally C- combinations act on characters, while the M- ones operate on words. The Meta key might be called Alt on some keyboards, will display a black diamond on most others, and can safely be replaced by Esc in any case.

Typing any ordinary key inserts text where the cursor stands, the arrow keys enabling you to move in the line. There are many more movement commands, which will be familiar to the Emacs user, for instance C-a/C-e will take you to the start/end of the line, M-b/M-f move the cursor backward/forward by a word, etc. Just press the <Return> key at any point to send your command to gp.

All the commands you type at the gp prompt are stored in a history, a multiline command being saved as a single concatenated line. The Up and Down arrows (or C-p/C-n) will move you through the history, M-</M-> sending you to the start/end of the history. C-r/C-s will start an incremental backward/forward search. You can kill text (C-k kills till the end of line, M-d to the end of current word) which you can then yank back using the C-y key (M-y will rotate the kill-ring). C- will undo your last changes incrementally (M-r undoes all changes made to the current line). C-t and M-t will transpose the character (word) preceding the cursor and the one under the cursor.

Keeping the M- key down while you enter an integer (a minus sign meaning reverse behavior) gives an argument to your next readline command (for instance M-- C-k will kill text back to the start of line). If you prefer Vi–style editing, M-C-j will toggle you to Vi mode.

Of course you can change all these default bindings. For that you need to create a file named .inputrc in your home directory. For instance (notice the embedding conditional in case you would want specific bindings for gp):

    $if Pari-GP
      set show-all-if-ambiguous
      "\C-h": backward-delete-char
      "\e\C-h": backward-kill-word
      "\C-xd": dump-functions

(: "\C-v()\C-b" # can be annoying when copy-pasting!

      [: "\C-v[]\C-b"
    $endif

C-x C-r will re-read this init file, incorporating any changes made to it during the current session.

Note. By default, ( and [ are bound to the function pari-matched-insert which, if “electric parentheses” are enabled (default: off) will automatically insert the matching closure (respectively ) and ]). This behavior can be toggled on and off by giving the numeric argument −2 to ( (M--2(), which is useful if you want, e.g to copy-paste some text into the calculator. If you do not want a toggle, you can use M--0 / M--1 to specifically switch it on or off).

Note. In some versions of readline (2.1 for instance), the Alt or Meta key can give funny results (output 8-bit accented characters for instance). If you do not want to fall back to the Esc combination, put the following two lines in your .inputrc:

      set convert-meta on
      set output-meta off

Command completion and online help. Hitting <TAB> will complete words for you. This mechanism is context-dependent: gp will strive to only give you meaningful completions in a given context (it will fail sometimes, but only under rare and restricted conditions).

For instance, shortly after a ~, we expect a user name, then a path to some file. Directly after default( has been typed, we would expect one of the default keywords. After whatnow( , we expect the name of an old function, which may well have disappeared from this version. After a ’.’, we expect a member keyword. And generally of course, we expect any GP symbol which may be found in the hashing lists: functions (both yours and GP’s), and variables.

If, at any time, only one completion is meaningful, gp will provide it together with

• an ending comma if we are completing a default,

• a pair of parentheses if we are completing a function name. In that case hitting <TAB> again will provide the argument list as given by the online help*.

Otherwise, hitting <TAB> once more will give you the list of possible completions. Just experiment with this mechanism as often as possible, you will probably find it very convenient. For

instance, you can obtain default(seriesprecision,10), just by hitting def<TAB>se<TAB>10,

which saves 18 keystrokes (out of 27).

Hitting M-h will give you the usual short online help concerning the word directly beneath the cursor, M-H will yield the extended help corresponding to the help default program (usually opens a dvi previewer, or runs a primitive tex-to-ASCII program). None of these disturb the line you were editing.

2.16 GNU Emacs and PariEmacs.

If you install the PariEmacs package (see Appendix A), you may use gp as a subprocess in Emacs. You then need to include in your .emacs file the following lines:

      (autoload 'gp-mode "pari" nil t)
      (autoload 'gp-script-mode "pari" nil t)
      (autoload 'gp "pari" nil t)
      (autoload 'gpman "pari" nil t)
      (setq auto-mode-alist
        (cons '("\\.gp$" . gp-script-mode) auto-mode-alist))

which autoloads functions from the PariEmacs package and ensures that file with the .gp suffix are edited in gp-script mode.

Once this is done, under GNU Emacs if you type M-x gp (where as usual M is the Meta key), a special shell will be started launching gp with the default stack size and prime limit. You can then work as usual under gp, but with all the facilities of an advanced text editor. See the PariEmacs documentation for customizations, menus, etc.

* recall that you can always undo the effect of the preceding keys by hitting C-62

Chapter 3: Functions and Operations Available in PARI and GP

The functions and operators available in PARI and in the GP/PARI calculator are numerous and ever-expanding. Here is a description of the ones available in version 2.7.2. It should be noted that many of these functions accept quite different types as arguments, but others are more restricted. The list of acceptable types will be given for each function or class of functions. Except when stated otherwise, it is understood that a function or operation which should make natural sense is legal. In this chapter, we will describe the functions according to a rough classification. The general entry looks something like:

foo(x, {flag = 0}): short description.

    The library syntax is GEN foo(GEN x, long fl = 0).

This means that the GP function foo has one mandatory argument x, and an optional one, flag, whose default value is 0. (The {} should not be typed, it is just a convenient notation we will use throughout to denote optional arguments.) That is, you can type foo(x,2), or foo(x), which is then understood to mean foo(x,0). As well, a comma or closing parenthesis, where an optional argument should have been, signals to GP it should use the default. Thus, the syntax foo(x,) is also accepted as a synonym for our last expression. When a function has more than one optional argument, the argument list is filled with user supplied values, in order. When none are left, the defaults are used instead. Thus, assuming that foo’s prototype had been

foo({x = 1}, {y = 2}, {z = 3}),

typing in foo(6,4) would give you foo(6,4,3). In the rare case when you want to set some far away argument, and leave the defaults in between as they stand, you can use the “empty arg” trick alluded to above: foo(6,,1) would yield foo(6,2,1). By the way, foo() by itself yields foo(1,2,3) as was to be expected.

In this rather special case of a function having no mandatory argument, you can even omit the (): a standalone foo would be enough (though we do not recommend it for your scripts, for the sake of clarity). In defining GP syntax, we strove to put optional arguments at the end of the argument list (of course, since they would not make sense otherwise), and in order of decreasing usefulness so that, most of the time, you will be able to ignore them.

Finally, an optional argument (between braces) followed by a star, like {x}∗, means that any number of such arguments (possibly none) can be given. This is in particular used by the various print routines.

Flags. A flag is an argument which, rather than conveying actual information to the routine, instructs it to change its default behavior, e.g. return more or less information. All such flags are optional, and will be called flag in the function descriptions to follow. There are two different kind of flags

• generic: all valid values for the flag are individually described (“If flag is equal to 1, then… ”).

• binary: use customary binary notation as a compact way to represent many toggles with just one integer. Let (p_{0}, …, p_{n}) be a list of switches (i.e. of properties which take either the value 0 or 1), the number 2^{3} + 2^{5} = 40 means that p_{3} and p_{5} are set (that is, set to 1), and none of the others are (that is, they are set to 0). This is announced as “The binary digits of flag mean 1: p_{0}, 2: p_{1}, 4: p_{2}”, and so on, using the available consecutive powers of 2.

Mnemonics for flags. Numeric flags as mentioned above are obscure, error-prone, and quite rigid: should the authors want to adopt a new flag numbering scheme (for instance when noticing flags with the same meaning but different numeric values across a set of routines), it would break backward compatibility. The only advantage of explicit numeric values is that they are fast to type, so their use is only advised when using the calculator gp.

As an alternative, one can replace a numeric flag by a character string containing symbolic identifiers. For a generic flag, the mnemonic corresponding to the numeric identifier is given after it as in

    fun(x, {flag = 0} ):
      If flag is equal to 1 = AGM, use an agm formula ...

which means that one can use indifferently fun(x, 1) or fun(x, "AGM").

For a binary flag, mnemonics corresponding to the various toggles are given after each of them. They can be negated by prepending no to the mnemonic, or by removing such a prefix. These toggles are grouped together using any punctuation character (such as ’,’ or ’;’). For instance (taken from description of ploth(X = a, b, expr, {flag = 0}, {n = 0}))

Binary digits of flags mean: 1 = Parametric, 2 = Recursive, …

so that, instead of 1, one could use the mnemonic "Parametric; no Recursive", or simply "Parametric" since Recursive is unset by default (default value of flag is 0, i.e. everything unset). People used to the bit-or notation in languages like C may also use the form "Parametric |

no Recursive".

Pointers. If a parameter in the function prototype is prefixed with a & sign, as in

foo(x, &e)

it means that, besides the normal return value, the function may assign a value to e as a side effect. When passing the argument, the & sign has to be typed in explicitly. As of version 2.7.2, this pointer argument is optional for all documented functions, hence the & will always appear between brackets as in Z issquare(x, {&e}).

About library programming. The library function foo, as defined at the beginning of this section, is seen to have two mandatory arguments, x and flag: no function seen in the present chapter has been implemented so as to accept a variable number of arguments, so all arguments are mandatory when programming with the library (usually, variants are provided corresponding to the various flag values). We include an = default value token in the prototype to signal how a missing argument should be encoded. Most of the time, it will be a NULL pointer, or -1 for a variable number. Refer to the User’s Guide to the PARI library for general background and details.

3.1 Standard monadic or dyadic operators.

3.1.1 +/-. The expressions +x and -x refer to monadic operators (the first does nothing, the second negates x).

The library syntax is GEN gneg(GEN x) for -x.

3.1.2 +. The expression x + y is the sum of x and y. Addition between a scalar type x and a t_COL or t_MAT y returns respectively [y[1] + x, y[2], …] and y + xId. Other additions between a scalar type and a vector or a matrix, or between vector/matrices of incompatible sizes are forbidden.

The library syntax is GEN gadd(GEN x, GEN y).

3.1.3 -. The expression x - y is the difference of x and y. Subtraction between a scalar type x and a t_COL or t_MAT y returns respectively [y[1] − x, y[2], …] and y − xId. Other subtractions between a scalar type and a vector or a matrix, or between vector/matrices of incompatible sizes are forbidden.

The library syntax is GEN gsub(GEN x, GEN y) for x - y.

3.1.4 *. The expression x * y is the product of x and y. Among the prominent impossibilities are multiplication between vector/matrices of incompatible sizes, between a t_INTMOD or t_PADIC Restricted to scalars, * is commutative; because of vector and matrix operations, it is not commutative in general.

Multiplication between two t_VECs or two t_COLs is not allowed; to take the scalar product of two vectors of the same length, transpose one of the vectors (using the operator ~ or the function mattranspose, see Section 3.8) and multiply a line vector by a column vector:

    ? a = [1,2,3];
    ? a * a
      ***   at top-level: a*a
      ***                  ^--
      *** _*_: forbidden multiplication t_VEC * t_VEC.
    ? a * a~
    %2 = 14

If x, y are binary quadratic forms, compose them; see also qfbnucomp and qfbnupow. If x, y are t_VECSMALL of the same length, understand them as permutations and compose them.

The library syntax is GEN gmul(GEN x, GEN y) for x * y. Also available is GEN gsqr(GEN x) for x * x.

3.1.5 /. The expression x / y is the quotient of x and y. In addition to the impossibilities for multiplication, note that if the divisor is a matrix, it must be an invertible square matrix, and in that case the result is x∗y^{−1}. Furthermore note that the result is as exact as possible: in particular, division of two integers always gives a rational number (which may be an integer if the quotient is exact) and not the Euclidean quotient (see x \ y for that), and similarly the quotient of two polynomials is a rational function in general. To obtain the approximate real value of the quotient of two integers, add 0. to the result; to obtain the approximate p-adic value of the quotient of two integers, add O(p^k) to the result; finally, to obtain the Taylor series expansion of the quotient of two polynomials, add O(X^k) to the result or use the taylor function (see Section 3.7.45).

The library syntax is GEN gdiv(GEN x, GEN y) for x / y.

3.1.6 \. The expression x \ y is the Euclidean quotient of x and y. If y is a real scalar, this is defined as floor(x/y) if y > 0, and ceil(x/y) if y < 0 and the division is not exact. Hence the remainder x - (x\y)*y is in [0, |y|[.

Note that when y is an integer and x a polynomial, y is first promoted to a polynomial of degree 0. When x is a vector or matrix, the operator is applied componentwise.

The library syntax is GEN gdivent(GEN x, GEN y) for x \ y.

3.1.7 \/. The expression x \/ y evaluates to the rounded Euclidean quotient of x and y. This is the same as x \ y except for scalar division: the quotient is such that the corresponding remainder is smallest in absolute value and in case of a tie the quotient closest to +∞ is chosen (hence the remainder would belong to ]−|y|/2, |y|/2]).

When x is a vector or matrix, the operator is applied componentwise.

    The library syntax is GEN gdivround(GEN x, GEN y) for x \/ y.

3.1.8 %. The expression x % y evaluates to the modular Euclidean remainder of x and y, which we now define. When x or y is a non-integral real number, x%y is defined as x - (x\y)*y. Otherwise, if y is an integer, this is the smallest non-negative integer congruent to x modulo y. (This actually coincides with the previous definition if and only if x is an integer.) If y is a polynomial, this is the polynomial of smallest degree congruent to x modulo y. For instance:

    ? (1/2) % 3
    %1 = 2
    ? 0.5 % 3
    %2 = 0.5000000000000000000000000000
    ? (1/2) % 3.0
    %3 = 1/2

Note that when y is an integer and x a polynomial, y is first promoted to a polynomial of degree 0. When x is a vector or matrix, the operator is applied componentwise.

The library syntax is GEN gmod(GEN x, GEN y) for x % y.

3.1.9 ^. The expression x^n is powering. If the exponent is an integer, then exact operations are performed using binary (left-shift) powering techniques. In particular, in this case x cannot be a vector or matrix unless it is a square matrix (invertible if the exponent is negative). If x is a p-adic number, its precision will increase if v_{p}(n) > 0. Powering a binary quadratic form (types t_QFI and t_QFR) returns a reduced representative of the class, provided the input is reduced. In particular, x^1 is identical to x.

PARI is able to rewrite the multiplication x ∗ x of two identical objects as x^{2}, or sqr(x). Here, identical means the operands are two different labels referencing the same chunk of memory; no equality test is performed. This is no longer true when more than two arguments are involved.

If the exponent is not of type integer, this is treated as a transcendental function (see Section 3.3), and in particular has the effect of componentwise powering on vector or matrices.

As an exception, if the exponent is a rational number p/q and x an integer modulo a prime or a p-adic number, return a solution y of y^{q} = x^{p} if it exists. Currently, q must not have large prime factors. Beware that

    ? Mod(7,19)^(1/2)

%1 = Mod(11, 19) /* is any square root */
? sqrt(Mod(7,19))
%2 = Mod(8, 19)  /* is the smallest square root */
? Mod(7,19)^(3/5)
%3 = Mod(1, 19)
? %3^(5/3)
%4 = Mod(1, 19)  /* Mod(7,19) is just another cubic root */

If the exponent is a negative integer, an inverse must be computed. For non-invertible t_INTMOD, this will fail and implicitly exhibit a non trivial factor of the modulus:

? Mod(4,6)^(-1)
  ***   at top-level: Mod(4,6)^(-1)
  ***                         ^-----
  *** _^_: impossible inverse modulo: Mod(2, 6).

(Here, a factor 2 is obtained directly. In general, take the gcd of the representative and the modulus.) This is most useful when performing complicated operations modulo an integer N whose factorization is unknown. Either the computation succeeds and all is well, or a factor d is discovered and the computation may be restarted modulo d or N/d.

For non-invertible t_POLMOD, this will fail without exhibiting a factor.

? Mod(x^2, x^3-x)^(-1)
  ***   at top-level: Mod(x^2,x^3-x)^(-1)
  ***                               ^-----
  *** _^_: non-invertible polynomial in RgXQ_inv.
? a = Mod(3,4)*y^3 + Mod(1,4); b = y^6+y^5+y^4+y^3+y^2+y+1;
? Mod(a, b)^(-1);
  ***   at top-level: Mod(a,b)^(-1)
  ***                         ^-----
  *** _^_: impossible inverse modulo: Mod(0, 4).

In fact the latter polynomial is invertible, but the algorithm used (subresultant) assumes the base ring is a domain. If it is not the case, as here for Z/4Z, a result will be correct but chances are an error will occur first. In this specific case, one should work with 2-adics. In general, one can try the following approach

? inversemod(a, b) =
{ my(m);
  m = polsylvestermatrix(polrecip(a), polrecip(b));
  m = matinverseimage(m, matid(#m)[,1]);
  Polrev( vecextract(m, Str("..", poldegree(b))), variable(b) )
}
? inversemod(a,b)
%2 = Mod(2,4)*y^5 + Mod(3,4)*y^3 + Mod(1,4)*y^2 + Mod(3,4)*y + Mod(2,4)

This is not guaranteed to work either since it must invert pivots. See Section 3.8.

The library syntax is GEN gpow(GEN x, GEN n, long prec) for x^n.

3.1.10 cmp(x, y). Gives the result of a comparison between arbitrary objects x and y (as −1, 0 or 1). The underlying order relation is transitive, the function returns 0 if and only if x === y, and its restriction to integers coincides with the customary one. Besides that, it has no useful mathematical meaning.

In case all components are equal up to the smallest length of the operands, the more complex is considered to be larger. More precisely, the longest is the largest; when lengths are equal, we have matrix > vector > scalar. For example:

? cmp(1, 2)
%1 = -1
? cmp(2, 1)
%2 = 1
? cmp(1, 1.0)   \\ note that 1 == 1.0, but (1===1.0) is false.
%3 = -1
? cmp(x + Pi, [])
%4 = -1

This function is mostly useful to handle sorted lists or vectors of arbitrary objects. For instance, if v is a vector, the construction vecsort(v, cmp) is equivalent to Set(v).

The library syntax is GEN cmp_universal(GEN x, GEN y).

3.1.11 divrem(x, y, {v}). Creates a column vector with two components, the first being the Euclidean quotient (x \ y), the second the Euclidean remainder (x - (x\y)*y), of the division of x by y. This avoids the need to do two divisions if one needs both the quotient and the remainder. If v is present, and x, y are multivariate polynomials, divide with respect to the variable v.

Beware that divrem(x,y)[2] is in general not the same as x % y; no GP operator corresponds to it:

? divrem(1/2, 3)[2]
%1 = 1/2
? (1/2) % 3
%2 = 2
? divrem(Mod(2,9), 3)[2]
 ***   at top-level: divrem(Mod(2,9),3)[2
 ***                 ^--------------------
 ***   forbidden division t_INTMOD \ t_INT.
? Mod(2,9) % 6
%3 = Mod(2,3)

The library syntax is GEN divrem(GEN x, GEN y, long v = -1), where v is a variable number. Also available is GEN gdiventres(GEN x, GEN y) when v is not needed.

3.1.12 lex(x, y). Gives the result of a lexicographic comparison between x and y (as −1, 0 or 1). This is to be interpreted in quite a wide sense: It is admissible to compare objects of different types (scalars, vectors, matrices), provided the scalars can be compared, as well as vectors/matrices of different lengths. The comparison is recursive.

In case all components are equal up to the smallest length of the operands, the more complex is considered to be larger. More precisely, the longest is the largest; when lengths are equal, we have matrix > vector > scalar. For example:

    ? lex([1,3], [1,2,5])
    %1 = 1
    ? lex([1,3], [1,3,-1])
    %2 = -1
    ? lex([1], [[1]])
    %3 = -1
    ? lex([1], [1]~)
    %4 = 0
    The library syntax is GEN lexcmp(GEN x, GEN y).

3.1.13 max(x, y). Creates the maximum of x and y when they can be compared.

The library syntax is GEN gmax(GEN x, GEN y).

3.1.14 min(x, y). Creates the minimum of x and y when they can be compared.

The library syntax is GEN gmin(GEN x, GEN y).

3.1.15 shift(x, n). Shifts x componentwise left by n bits if n ≥ 0 and right by |n| bits if n < 0. May be abbreviated as x << n or x >> (−n). A left shift by n corresponds to multiplication by 2^{n}. A right shift of an integer x by |n| corresponds to a Euclidean division of x by 2^{|n|} with a remainder of the same sign as x, hence is not the same (in general) as x\2^{n}.

    The library syntax is GEN gshift(GEN x, long n).

3.1.16 shiftmul(x, n). Multiplies x by 2^{n}. The difference with shift is that when n < 0, ordinary division takes place, hence for example if x is an integer the result may be a fraction, while for shifts Euclidean division takes place when n < 0 hence if x is an integer the result is still an integer.

    The library syntax is GEN gmul2n(GEN x, long n).

3.1.17 sign(x). sign (0, 1 or −1) of x, which must be of type integer, real or fraction.

The library syntax is GEN gsigne(GEN x).

3.1.18 vecmax(x, {&v}). If x is a vector or a matrix, returns the largest entry of x, otherwise returns a copy of x. Error if x is empty.

If v is given, set it to the index of a largest entry (indirect maximum), when x is a vector. If x is a matrix, set v to coordinates [i, j] such that x[i, j] is a largest entry. This flag is ignored if x is not a vector or matrix.

? vecmax([10, 20, -30, 40])
%1 = 40
? vecmax([10, 20, -30, 40], &v); v
%2 = 4
? vecmax([10, 20; -30, 40], &v); v
%3 = [2, 2]
The library syntax is GEN vecmax0(GEN x, GEN *v = NULL). Also available is GEN vec-
max(GEN x).

3.1.19 vecmin(x, {&v}). If x is a vector or a matrix, returns the smallest entry of x, otherwise returns a copy of x. Error if x is empty.

If v is given, set it to the index of a smallest entry (indirect minimum), when x is a vector. If x is a matrix, set v to coordinates [i, j] such that x[i, j] is a smallest entry. This is ignored if x is not a vector or matrix.

? vecmin([10, 20, -30, 40])
%1 = -30
? vecmin([10, 20, -30, 40], &v); v
%2 = 3
? vecmin([10, 20; -30, 40], &v); v
%3 = [2, 1]

The library syntax is GEN vecmin0(GEN x, GEN *v = NULL). Also available is GEN

vecmin(GEN x).

3.1.20 Comparison and Boolean operators. The six standard comparison operators <=, <, >=, >, ==, != are available in GP. The result is 1 if the comparison is true, 0 if it is false. The operator == is quite liberal : for instance, the integer 0, a 0 polynomial, and a vector with 0 entries are all tested equal.

The extra operator === tests whether two objects are identical and is much stricter than == : objects of different type or length are never identical.

For the purpose of comparison, t_STR objects are strictly larger than any other non-string type; two t_STR objects are compared using the standard lexicographic order.

GP accepts <> as a synonym for !=. On the other hand, = is definitely not a synonym for ==: it is the assignment statement.

The standard boolean operators || (inclusive or), && (and) and ! (not) are also available.

3.2 Conversions and similar elementary functions or commands.

Many of the conversion functions are rounding or truncating operations. In this case, if the argument is a rational function, the result is the Euclidean quotient of the numerator by the denominator, and if the argument is a vector or a matrix, the operation is done componentwise. This will not be restated for every function.

3.2.1 Col(x, {n}). Transforms the object x into a column vector. The dimension of the resulting vector can be optionally specified via the extra parameter n.

If n is omitted or 0, the dimension depends on the type of x; the vector has a single component, except when x is

• a vector or a quadratic form (in which case the resulting vector is simply the initial object considered as a row vector),

• a polynomial or a power series. In the case of a polynomial, the coefficients of the vector start with the leading coefficient of the polynomial, while for power series only the significant coefficients are taken into account, but this time by increasing order of degree. In this last case, Vec is the reciprocal function of Pol and Ser respectively,

• a matrix (the column of row vector comprising the matrix is returned),

• a character string (a vector of individual characters is returned).

In the last two cases (matrix and character string), n is meaningless and must be omitted or an error is raised. Otherwise, if n is given, 0 entries are appended at the end of the vector if n > 0, and prepended at the beginning if n < 0. The dimension of the resulting vector is |n|.

Note that the function Colrev does not exist, use Vecrev.

    The library syntax is GEN gtocol0(GEN x, long n). GEN gtocol(GEN x) is also available.

3.2.2 Colrev(x, {n}). As Col(x, n), then reverse the result. In particular

    The library syntax is GEN gtocolrev0(GEN x, long n). GEN gtocolrev(GEN x) is also avail-

able.

3.2.3 List({x = [ ]}). Transforms a (row or column) vector x into a list, whose components are the entries of x. Similarly for a list, but rather useless in this case. For other types, creates a list with the single element x. Note that, except when x is omitted, this function creates a small memory leak; so, either initialize all lists to the empty list, or use them sparingly.

    The library syntax is GEN gtolist(GEN x = NULL). The variant GEN listcreate(void) cre-

ates an empty list.

3.2.4 Mat({x = [ ]}). Transforms the object x into a matrix. If x is already a matrix, a copy of x is created. If x is a row (resp. column) vector, this creates a 1-row (resp. 1-column) matrix, unless all elements are column (resp. row) vectors of the same length, in which case the vectors are concatenated sideways and the associated big matrix is returned. If x is a binary quadratic form, creates the associated 2 × 2 matrix. Otherwise, this creates a 1 × 1 matrix containing x.

? Mat(x + 1)
%1 =
[x + 1]
? Vec( matid(3) )
%2 = [[1, 0, 0]~, [0, 1, 0]~, [0, 0, 1]~]
? Mat(%)
%3 =
[1 0 0]
[0 1 0]
[0 0 1]
? Col( [1,2; 3,4] )
%4 = [[1, 2], [3, 4]]~
? Mat(%)
%5 =
[1 2]
[3 4]
? Mat(Qfb(1,2,3))
%6 =
[1 1]
[1 3]
The library syntax is GEN gtomat(GEN x = NULL).

3.2.5 Mod(a, b). In its basic form, creates an intmod or a polmod (a mod b); b must be an integer or a polynomial. We then obtain a t_INTMOD and a t_POLMOD respectively:

? t = Mod(2,17); t^8
%1 = Mod(1, 17)
? t = Mod(x,x^2+1); t^2
%2 = Mod(-1, x^2+1)

If a%b makes sense and yields a result of the appropriate type (t_INT or scalar/t_POL), the operation succeeds as well:

? Mod(1/2, 5)
%3 = Mod(3, 5)
? Mod(7 + O(3^6), 3)
%4 = Mod(1, 3)
? Mod(Mod(1,12), 9)
%5 = Mod(1, 3)
? Mod(1/x, x^2+1)
%6 = Mod(-1, x^2+1)
? Mod(exp(x), x^4)
%7 = Mod(1/6*x^3 + 1/2*x^2 + x + 1, x^4)

If a is a complex object, “base change” it to Z/bZ or K[x]/(b), which is equivalent to, but faster than, multiplying it by Mod(1,b):

? Mod([1,2;3,4], 2)
%8 =
[Mod(1, 2) Mod(0, 2)]
[Mod(1, 2) Mod(0, 2)]
? Mod(3*x+5, 2)
%9 = Mod(1, 2)*x + Mod(1, 2)
? Mod(x^2 + y*x + y^3, y^2+1)
%10 = Mod(1, y^2 + 1)*x^2 + Mod(y, y^2 + 1)*x + Mod(-y, y^2 + 1)

This function is not the same as x % y, the result of which has no knowledge of the intended modulus y. Compare

? x = 4 % 5; x + 1
%1 = 5
? x = Mod(4,5); x + 1
%2 = Mod(0,5)
The library syntax is GEN gmodulo(GEN a, GEN b).

3.2.6 Pol(t, {v =′ x}). Transforms the object t into a polynomial with main variable v. If t is a scalar, this gives a constant polynomial. If t is a power series with non-negative valuation or a rational function, the effect is similar to truncate, i.e. we chop off the O(X^{k}) or compute the Euclidean quotient of the numerator by the denominator, then change the main variable of the result to v.

The main use of this function is when t is a vector: it creates the polynomial whose coefficients are given by t, with t[1] being the leading coefficient (which can be zero). It is much faster to evaluate Pol on a vector of coefficients in this way, than the corresponding formal expression a_{n}X^{n} + … + a_{0}, which is evaluated naively exactly as written (linear versus quadratic time in n). Polrev can be used if one wants x[1] to be the constant coefficient:

? Pol([1,2,3])
%1 = x^2 + 2*x + 3
? Polrev([1,2,3])
%2 = 3*x^2 + 2*x + 1

The reciprocal function of Pol (resp. Polrev) is Vec (resp. Vecrev).

? Vec(Pol([1,2,3]))
%1 = [1, 2, 3]
? Vecrev( Polrev([1,2,3]) )
%2 = [1, 2, 3]

Warning. This is not a substitution function. It will not transform an object containing variables of higher priority than v.

    ? Pol(x + y, y)
      ***   at top-level: Pol(x+y,y)
      ***                 ^----------
      *** Pol: variable must have higher priority in gtopoly.

The library syntax is GEN gtopoly(GEN t, long v = -1), where v is a variable number.

3.2.7 Polrev(t, {v =′ x}). Transform the object t into a polynomial with main variable v. If t is a scalar, this gives a constant polynomial. If t is a power series, the effect is identical to truncate, i.e. it chops off the O(X^{k}).

The main use of this function is when t is a vector: it creates the polynomial whose coefficients are given by t, with t[1] being the constant term. Pol can be used if one wants t[1] to be the leading coefficient:

    ? Polrev([1,2,3])
    %1 = 3*x^2 + 2*x + 1
    ? Pol([1,2,3])
    %2 = x^2 + 2*x + 3

The reciprocal function of Pol (resp. Polrev) is Vec (resp. Vecrev).

The library syntax is GEN gtopolyrev(GEN t, long v = -1), where v is a variable number.

3.2.8 Qfb(a, b, c, {D = 0.}). Creates the binary quadratic form ax^{2} + bxy + cy^{2}. If b^{2} − 4ac > 0, initialize Shanks’ distance function to D. Negative definite forms are not implemented, use their positive definite counterpart instead.

    The library syntax is GEN Qfb0(GEN a, GEN b, GEN c, GEN D = NULL, long prec). Also
available are GEN qfi(GEN a, GEN b, GEN c) (assumes b^{2} − 4ac < 0) and GEN qfr(GEN a, GEN

b, GEN c, GEN D) (assumes b^{2} − 4ac > 0).

3.2.9 Ser(s, {v =′ x}, {d = seriesprecision}). Transforms the object s into a power series with main variable v (x by default) and precision (number of significant terms) equal to d (= the default seriesprecision by default). If s is a scalar, this gives a constant power series in v with precision d. If s is a polynomial, the polynomial is truncated to d terms if needed

    ? Ser(1, 'y, 5)
    %1 = 1 + O(y^5)
    ? Ser(x^2,, 5)
    %2 = x^2 + O(x^7)
    ? T = polcyclo(100)
    %3 = x^40 - x^30 + x^20 - x^10 + 1
    ? Ser(T, 'x, 11)
    %4 = 1 - x^10 + O(x^11)

The function is more or less equivalent with multiplication by 1+O(v^{d}) in theses cases, only faster.

If s is a vector, on the other hand, the coefficients of the vector are understood to be the coefficients of the power series starting from the constant term (as in Polrev(x)), and the precision d is ignored: in other words, in this case, we convert t_VEC / t_COL to the power series whose significant terms are exactly given by the vector entries. Finally, if s is already a power series in v, we return it verbatim, ignoring d again. If d significant terms are desired in the last two cases, convert/truncate to t_POL first.

? v = [1,2,3]; Ser(v, t, 7)
%5 = 1 + 2*t + 3*t^2 + O(t^3)  \\ 3 terms: 7 is ignored!
? Ser(Polrev(v,t), t, 7)
%6 = 1 + 2*t + 3*t^2 + O(t^7)
? s = 1+x+O(x^2); Ser(s, x, 7)
%7 = 1 + x + O(x^2)  \\ 2 terms: 7 ignored
? Ser(truncate(s), x, 7)
%8 = 1 + x + O(x^7)

The warning given for Pol also applies here: this is not a substitution function.

The library syntax is GEN gtoser(GEN s, long v = -1, long precdl), where v is a variable

number.

3.2.10 Set({x = [ ]}). Converts x into a set, i.e. into a row vector, with strictly increasing entries with respect to the (somewhat arbitrary) universal comparison function cmp. Standard container types t_VEC, t_COL, t_LIST and t_VECSMALL are converted to the set with corresponding elements. All others are converted to a set with one element.

? Set([1,2,4,2,1,3])
%1 = [1, 2, 3, 4]
? Set(x)
%2 = [x]
? Set(Vecsmall([1,3,2,1,3]))
%3 = [1, 2, 3]
The library syntax is GEN gtoset(GEN x = NULL).

3.2.11 Str({x}∗). Converts its argument list into a single character string (type t_STR, the empty string if x is omitted). To recover an ordinary GEN from a string, apply eval to it. The arguments of Str are evaluated in string context, see Section 2.9.

? x2 = 0; i = 2; Str(x, i)
%1 = "x2"
? eval(%)
%2 = 0

This function is mostly useless in library mode. Use the pair strtoGEN/GENtostr to convert between GEN and char*. The latter returns a malloced string, which should be freed after usage.

3.2.12 Strchr(x). Converts x to a string, translating each integer into a character.

? Strchr(97)
%1 = "a"
? Vecsmall("hello world")
%2 = Vecsmall([104, 101, 108, 108, 111, 32, 119, 111, 114, 108, 100])
? Strchr(%)
%3 = "hello world"

The library syntax is GEN Strchr(GEN x).

3.2.13 Strexpand({x}∗). Converts its argument list into a single character string (type t_STR, the empty string if x is omitted). Then perform environment expansion, see Section 2.12. This feature can be used to read environment variable values.

    ? Strexpand("$HOME/doc")
    %1 = "/home/pari/doc"

The individual arguments are read in string context, see Section 2.9.

3.2.14 Strtex({x}∗). Translates its arguments to TeX format, and concatenates the results into a single character string (type t_STR, the empty string if x is omitted).

The individual arguments are read in string context, see Section 2.9.

3.2.15 Vec(x, {n}). Transforms the object x into a row vector. The dimension of the resulting vector can be optionally specified via the extra parameter n.

If n is omitted or 0, the dimension depends on the type of x; the vector has a single component, except when x is

• a vector or a quadratic form (in which case the resulting vector is simply the initial object considered as a row vector),

• a polynomial or a power series. In the case of a polynomial, the coefficients of the vector start with the leading coefficient of the polynomial, while for power series only the significant coefficients are taken into account, but this time by increasing order of degree. In this last case, Vec is the reciprocal function of Pol and Ser respectively,

• a matrix: return the vector of columns comprising the matrix.

• a character string: return the vector of individual characters.

• an error context (t_ERROR): return the error components, see iferr.

In the last three cases (matrix, character string, error), n is meaningless and must be omitted or an error is raised. Otherwise, if n is given, 0 entries are appended at the end of the vector if n > 0, and prepended at the beginning if n < 0. The dimension of the resulting vector is |n|. Variant: GEN gtovec(GEN x) is also available.

    The library syntax is GEN gtovec0(GEN x, long n).

3.2.16 Vecrev(x, {n}). As Vec(x, n), then reverse the result. In particular In this case, Vecrev is the reciprocal function of Polrev: the coefficients of the vector start with the constant coefficient of the polynomial and the others follow by increasing degree.

    The library syntax is GEN gtovecrev0(GEN x, long n). GEN gtovecrev(GEN x) is also avail-

able.

3.2.17 Vecsmall(x, {n}). Transforms the object x into a row vector of type t_VECSMALL. The dimension of the resulting vector can be optionally specified via the extra parameter n.

This acts as Vec(x, n), but only on a limited set of objects: the result must be representable as a vector of small integers. If x is a character string, a vector of individual characters in ASCII encoding is returned (Strchr yields back the character string).

    The library syntax is GEN gtovecsmall0(GEN x, long n). GEN gtovecsmall(GEN x) is also

available.

3.2.18 binary(x). Outputs the vector of the binary digits of |x|. Here x can be an integer, a real number (in which case the result has two components, one for the integer part, one for the fractional part) or a vector/matrix.

The library syntax is GEN binaire(GEN x).

3.2.19 bitand(x, y). Bitwise and of two integers x and y, that is the integer

∑_{i}(x_{i} and y_{i})2^{i}

Negative numbers behave 2-adically, i.e. the result is the 2-adic limit of bitand(x_{n}, y_{n}), where x_{n} and y_{n} are non-negative integers tending to x and y respectively. (The result is an ordinary integer, possibly negative.)

    ? bitand(5, 3)
    %1 = 1
    ? bitand(-5, 3)
    %2 = 3
    ? bitand(-5, -3)
    %3 = -7
    The library syntax is GEN gbitand(GEN x, GEN y). Also available is GEN ibitand(GEN x,

GEN y), which returns the bitwise and of |x| and |y|, two integers.

3.2.20 bitneg(x, {n = −1}). bitwise negation of an integer x, truncated to n bits, n ≥ 0, that is the integer

∑_{i=0}^{n−1} not(x_{i})2^{i}.

The special case n = −1 means no truncation: an infinite sequence of leading 1 is then represented as a negative number.

See Section 3.2.19 for the behavior for negative arguments.

    The library syntax is GEN gbitneg(GEN x, long n).

3.2.21 bitnegimply(x, y). Bitwise negated imply of two integers x and y (or not (x ⇒ y)), that is the integer

∑(x_{i} andnot(y_{i}))2^{i}

See Section 3.2.19 for the behavior for negative arguments.

    The library syntax is GEN gbitnegimply(GEN x, GEN y). Also available is GEN ibitnegim-

ply(GEN x, GEN y), which returns the bitwise negated imply of |x| and |y|, two integers.

3.2.22 bitor(x, y). bitwise (inclusive) or of two integers x and y, that is the integer

∑(x_{i} or y_{i})2^{i}

See Section 3.2.19 for the behavior for negative arguments.

    The library syntax is GEN gbitor(GEN x, GEN y). Also available is GEN ibitor(GEN x, GEN

y), which returns the bitwise ir of |x| and |y|, two integers.

3.2.23 bittest(x, n). Outputs the n^{th} bit of x starting from the right (i.e. the coefficient of 2^{n} in the binary expansion of x). The result is 0 or 1.

    ? bittest(7, 3)
    %1 = 1 \\ the 3rd bit is 1
    ? bittest(7, 4)
    %2 = 0 \\ the 4th bit is 0

See Section 3.2.19 for the behavior at negative arguments.

The library syntax is GEN gbittest(GEN x, long n). For a t_INT x, the variant long bittest(GEN x, long n) is generally easier to use, and if furthermore n ≥ 0 the low-level function

ulong int_bit(GEN x, long n) returns bittest(abs(x),n).

3.2.24 bitxor(x, y). Bitwise (exclusive) or of two integers x and y, that is the integer

∑(x_{i} xor y_{i})2^{i}

See Section 3.2.19 for the behavior for negative arguments.

    The library syntax is GEN gbitxor(GEN x, GEN y). Also available is GEN ibitxor(GEN x,

GEN y), which returns the bitwise xor of |x| and |y|, two integers.

3.2.25 ceil(x). Ceiling of x. When x is in R, the result is the smallest integer greater than or equal to x. Applied to a rational function, ceil(x) returns the Euclidean quotient of the numerator by the denominator.

The library syntax is GEN gceil(GEN x).

3.2.26 centerlift(x, {v}). Same as lift, except that t_INTMOD and t_PADIC components are lifted using centered residues:

• for a t_INTMOD x ∈ Z/nZ, the lift y is such that −n/2 < y ≤ n/2.

• a t_PADIC x is lifted in the same way as above (modulo p^{padicprec(x)}) if its valuation v is nonnegative; if not, returns the fraction p^{v} centerlift(xp^{−v}); in particular, rational reconstruction is not attempted. Use bestappr for this.

For backward compatibility, centerlift(x,'v) is allowed as an alias for lift(x,'v).

The library syntax is centerlift(GEN x).

3.2.27 characteristic(x). Returns the characteristic of the base ring over which x is defined (as defined by t_INTMOD and t_FFELT components). The function raises an exception if incompatible primes arise from t_FFELT and t_PADIC components.

    ? characteristic(Mod(1,24)*x + Mod(1,18)*y)
    %1 = 6
    The library syntax is GEN characteristic(GEN x).

3.2.28 component(x, n). Extracts the n^{th}-component of x. This is to be understood as follows: every PARI type has one or two initial code words. The components are counted, starting at 1, after these code words. In particular if x is a vector, this is indeed the n^{th}-component of x, if x is a matrix, the n^{th} column, if x is a polynomial, the n^{th} coefficient (i.e. of degree n − 1), and for power series, the n^{th} significant coefficient.

For polynomials and power series, one should rather use polcoeff, and for vectors and matrices, the [] operator. Namely, if x is a vector, then x[n] represents the n^{th} component of x. If x is a matrix, x[m,n] represents the coefficient of row m and column n of the matrix, x[m,] represents the m^{th} row of x, and x[,n] represents the n^{th} column of x.

Using of this function requires detailed knowledge of the structure of the different PARI types, and thus it should almost never be used directly. Some useful exceptions:

        ? x = 3 + O(3^5);
        ? component(x, 2)
        %2 = 81   \\ p^(p-adic accuracy)
        ? component(x, 1)
        %3 = 3    \\ p
        ? q = Qfb(1,2,3);
        ? component(q, 1)
        %5 = 1
    The library syntax is GEN compo(GEN x, long n).

3.2.29 conj(x). Conjugate of x. The meaning of this is clear, except that for real quadratic numbers, it means conjugation in the real quadratic field. This function has no effect on integers, reals, intmods, fractions or p-adics. The only forbidden type is polmod (see conjvec for this).

The library syntax is GEN gconj(GEN x).

3.2.30 conjvec(z). Conjugate vector representation of z. If z is a polmod, equal to Mod(a, T), this gives a vector of length degree(T) containing:

• the complex embeddings of z if T has rational coefficients, i.e. the a(r[i]) where r =

polroots(T);

• the conjugates of z if T has some intmod coefficients;

if z is a finite field element, the result is the vector of conjugates [z, z^{p}, z^{p^{2}}, …, z^{p^{n−1}}] where n = degree(T).

If z is an integer or a rational number, the result is z. If z is a (row or column) vector, the result is a matrix whose columns are the conjugate vectors of the individual elements of z.

    The library syntax is GEN conjvec(GEN z, long prec).

3.2.31 denominator(x). Denominator of x. The meaning of this is clear when x is a rational number or function. If x is an integer or a polynomial, it is treated as a rational number or function, respectively, and the result is equal to 1. For polynomials, you probably want to use

    denominator( content(x) )

instead. As for modular objects, t_INTMOD and t_PADIC have denominator 1, and the denominator of a t_POLMOD is the denominator of its (minimal degree) polynomial representative.

If x is a recursive structure, for instance a vector or matrix, the lcm of the denominators of its components (a common denominator) is computed. This also applies for t_COMPLEXs and t_QUADs.

Warning. Multivariate objects are created according to variable priorities, with possibly surprising side effects (x/y is a polynomial, but y/x is a rational function). See Section 2.5.3.

The library syntax is GEN denom(GEN x).

3.2.32 digits(x, {b = 10}). Outputs the vector of the digits of |x| in base b, where x and b are integers.

The library syntax is GEN digits(GEN x, GEN b = NULL).

3.2.33 floor(x). Floor of x. When x is in R, the result is the largest integer smaller than or equal to x. Applied to a rational function, floor(x) returns the Euclidean quotient of the numerator by the denominator.

The library syntax is GEN gfloor(GEN x).

3.2.34 frac(x). Fractional part of x. Identical to x − floor(x). If x is real, the result is in [0, 1[.

The library syntax is GEN gfrac(GEN x).

3.2.35 hammingweight(x). If x is a t_INT, return the binary Hamming weight of |x|. Otherwise x must be of type t_POL, t_VEC, t_COL, t_VECSMALL, or t_MAT and the function returns the number of non-zero coefficients of x.

? hammingweight(15)
%1 = 4
? hammingweight(x^100 + 2*x + 1)
%2 = 3
? hammingweight([Mod(1,2), 2, Mod(0,3)])
%3 = 2
? hammingweight(matid(100))
%4 = 100
The library syntax is long hammingweight(GEN x).

3.2.36 imag(x). Imaginary part of x. When x is a quadratic number, this is the coefficient of ω in the “canonical” integral basis (1, ω).

The library syntax is GEN gimag(GEN x).

3.2.37 length(x). Length of x; #x is a shortcut for length(x). This is mostly useful for

• vectors: dimension (0 for empty vectors),

• lists: number of entries (0 for empty lists),

• matrices: number of columns,

• character strings: number of actual characters (without trailing \0, should you expect it from C char*).

 ? #"a string"
 %1 = 8
 ? #[3,2,1]
 %2 = 3

 ? #[]
 %3 = 0
 ? #matrix(2,5)
 %4 = 5
 ? L = List([1,2,3,4]); #L
 %5 = 4

The routine is in fact defined for arbitrary GP types, but is awkward and useless in other cases: it returns the number of non-code words in x, e.g. the effective length minus 2 for integers since the t_INT type has two code words.

The library syntax is long glength(GEN x).

3.2.38 lift(x, {v}). If v is omitted, lifts intmods from Z/nZ in Z, p-adics from Q_{p} to Q (as truncate), and polmods to polynomials. Otherwise, lifts only polmods whose modulus has main variable v. t_FFELT are not lifted, nor are List elements: you may convert the latter to vectors first, or use apply(lift,L). More generally, components for which such lifts are meaningless (e.g. character strings) are copied verbatim.

? lift(Mod(5,3))
%1 = 2
? lift(3 + O(3^9))
%2 = 3
? lift(Mod(x,x^2+1))
%3 = x
? lift(Mod(x,x^2+1))
%4 = x

Lifts are performed recursively on an object components, but only by one level: once a t_POLMOD is lifted, the components of the result are not lifted further.

? lift(x * Mod(1,3) + Mod(2,3))
%4 = x + 2
? lift(x * Mod(y,y^2+1) + Mod(2,3))

%5 = y*x + Mod(2, 3) \\ do you understand this one?

? lift(x * Mod(y,y^2+1) + Mod(2,3), 'x)
%6 = Mod(y, y^2 + 1)*x + Mod(Mod(2, 3), y^2 + 1)
? lift(%, y)
%7 = y*x + Mod(2, 3)

To recursively lift all components not only by one level, but as long as possible, use liftall. To lift only t_INTMODs and t_PADICs components, use liftint. To lift only t_POLMODs components, use liftpol. Finally, centerlift allows to lift t_INTMODs and t_PADICs using centered residues (lift of smallest absolute value).

The library syntax is GEN lift0(GEN x, long v = -1), where v is a variable number. Also available is GEN lift(GEN x) corresponding to lift0(x,-1).

3.2.39 liftall(x). Recursively lift all components of x from Z/nZ to Z, from Q_{p} to Q (as truncate), and polmods to polynomials. t_FFELT are not lifted, nor are List elements: you may convert the latter to vectors first, or use apply(liftall,L). More generally, components for which such lifts are meaningless (e.g. character strings) are copied verbatim.

    ? liftall(x * (1 + O(3)) + Mod(2,3))
    %1 = x + 2
    ? liftall(x * Mod(y,y^2+1) + Mod(2,3)*Mod(z,z^2))
    %2 = y*x + 2*z

The library syntax is GEN liftall(GEN x).

3.2.40 liftint(x). Recursively lift all components of x from Z/nZ to Z and from Q_{p} to Q (as truncate). t_FFELT are not lifted, nor are List elements: you may convert the latter to vectors first, or use apply(liftint,L). More generally, components for which such lifts are meaningless (e.g. character strings) are copied verbatim.

    ? liftint(x * (1 + O(3)) + Mod(2,3))
    %1 = x + 2
    ? liftint(x * Mod(y,y^2+1) + Mod(2,3)*Mod(z,z^2))
    %2 = Mod(y, y^2 + 1)*x + Mod(Mod(2*z, z^2), y^2 + 1)

The library syntax is GEN liftint(GEN x).

3.2.41 liftpol(x). Recursively lift all components of x which are polmods to polynomials. t_FFELT are not lifted, nor are List elements: you may convert the latter to vectors first, or use apply(liftpol,L). More generally, components for which such lifts are meaningless (e.g. character strings) are copied verbatim.

    ? liftpol(x * (1 + O(3)) + Mod(2,3))
    %1 = (1 + O(3))*x + Mod(2, 3)
    ? liftpol(x * Mod(y,y^2+1) + Mod(2,3)*Mod(z,z^2))
    %2 = y*x + Mod(2, 3)*z

The library syntax is GEN liftpol(GEN x).

3.2.42 norm(x). Algebraic norm of x, i.e. the product of x with its conjugate (no square roots are taken), or conjugates for polmods. For vectors and matrices, the norm is taken componentwise and hence is not the L^{2}-norm (see norml2). Note that the norm of an element of R is its square, so as to be compatible with the complex norm.

The library syntax is GEN gnorm(GEN x).

3.2.43 numerator(x). Numerator of x. The meaning of this is clear when x is a rational number or function. If x is an integer or a polynomial, it is treated as a rational number or function, respectively, and the result is x itself. For polynomials, you probably want to use

    numerator( content(x) )

instead.

In other cases, numerator(x) is defined to be denominator(x)*x. This is the case when x is a vector or a matrix, but also for t_COMPLEX or t_QUAD. In particular since a t_PADIC or t_INTMOD has denominator 1, its numerator is itself.

Warning. Multivariate objects are created according to variable priorities, with possibly surprising side effects (x/y is a polynomial, but y/x is a rational function). See Section 2.5.3.

The library syntax is GEN numer(GEN x).

3.2.44 numtoperm(n, k). Generates the k-th permutation (as a row vector of length n) of the numbers 1 to n. The number k is taken modulo n! , i.e. inverse function of permtonum. The numbering used is the standard lexicographic ordering, starting at 0.

    The library syntax is GEN numtoperm(long n, GEN k).

3.2.45 padicprec(x, p). Absolute p-adic precision of the object x. This is the minimum precision of the components of x. The result is LONG_MAX (2^{31} − 1 for 32-bit machines or 2^{63} − 1 for 64-bit machines) if x is an exact object.

    The library syntax is long padicprec(GEN x, GEN p).

3.2.46 permtonum(x). Given a permutation x on n elements, gives the number k such that x = numtoperm(n, k), i.e. inverse function of numtoperm. The numbering used is the standard lexicographic ordering, starting at 0.

The library syntax is GEN permtonum(GEN x).

3.2.47 precision(x, {n}). The function has two different behaviors according to whether n is present or not.

If n is missing, the function returns the precision in decimal digits of the PARI object x. If x is an exact object, the largest single precision integer is returned.

    ? precision(exp(1e-100))
    %1 = 134                \\ 134 significant decimal digits
    ? precision(2 + x)
    %2 = 2147483647         \\ exact object
    ? precision(0.5 + O(x))
    %3 = 28                 \\ floating point accuracy, NOT series precision
    ? precision( [ exp(1e-100), 0.5 ] )
    %4 = 28                 \\ minimal accuracy among components

The return value for exact objects is meaningless since it is not even the same on 32 and 64-bit machines. The proper way to test whether an object is exact is

    ? isexact(x) = precision(x) == precision(0)

If n is present, the function creates a new object equal to x with a new “precision” n. (This never changes the type of the result. In particular it is not possible to use it to obtain a polynomial from a power series; for that, see truncate.) Now the meaning of precision is different from the above (floating point accuracy), and depends on the type of x:

For exact types, no change. For x a vector or a matrix, the operation is done componentwise.

For real x, n is the number of desired significant decimal digits. If n is smaller than the precision of x, x is truncated, otherwise x is extended with zeros.

For x a p-adic or a power series, n is the desired number of significant p-adic or X-adic digits, where X is the main variable of x. (Note: yes, this is inconsistent.) Note that the precision is a priori distinct from the exponent k appearing in O(∗^{k}); it is indeed equal to k if and only if x is a p-adic or X-adic unit.

? precision(1 + O(x), 10)
%1 = 1 + O(x^10)
? precision(x^2 + O(x^10), 3)
%2 = x^2 + O(x^5)
? precision(7^2 + O(7^10), 3)
%3 = 7^2 + O(7^5)

For the last two examples, note that x^{2} +O(x^{5}) = x^{2}(1+O(x^{3})) indeed has 3 significant coefficients

The library syntax is GEN precision0(GEN x, long n). Also available are GEN gprec(GEN

x, long n) and long precision(GEN x). In both, the accuracy is expressed in words (32-bit or 64-bit depending on the architecture).

3.2.48 random({N = 2^{31}}). Returns a random element in various natural sets depending on the argument N.

• t_INT: returns an integer uniformly distributed between 0 and N −1. Omitting the argument is equivalent to random(2^31).

• t_REAL: returns a real number in [0, 1[ with the same accuracy as N (whose mantissa has the same number of significant words).

• t_INTMOD: returns a random intmod for the same modulus.

• t_FFELT: returns a random element in the same finite field.

• t_VEC of length 2, N = [a, b]: returns an integer uniformly distributed between a and b.

• t_VEC generated by ellinit over a finite field k (coefficients are t_INTMODs modulo a prime or t_FFELTs): returns a “random” k-rational affine point on the curve. More precisely if the curve has a single point (at infinity!) we return it; otherwise we return an affine point by drawing an abscissa uniformly at random until ellordinate succeeds. Note that this is definitely not a uniform distribution over E(k), but it should be good enough for applications.

• t_POL return a random polynomial of degree at most the degree of N. The coefficients are drawn by applying random to the leading coefficient of N.

? random(10)
%1 = 9
? random(Mod(0,7))
%2 = Mod(1, 7)
? a = ffgen(ffinit(3,7), 'a); random(a)
%3 = a^6 + 2*a^5 + a^4 + a^3 + a^2 + 2*a
? E = ellinit([3,7]*Mod(1,109)); random(E)
%4 = [Mod(103, 109), Mod(10, 109)]
? E = ellinit([1,7]*a^0); random(E)
%5 = [a^6 + a^5 + 2*a^4 + 2*a^2, 2*a^6 + 2*a^4 + 2*a^3 + a^2 + 2*a]
? random(Mod(1,7)*x^4)
%6 = Mod(5, 7)*x^4 + Mod(6, 7)*x^3 + Mod(2, 7)*x^2 + Mod(2, 7)*x + Mod(5, 7)

These variants all depend on a single internal generator, and are independent from your operating system’s random number generators. A random seed may be obtained via getrand, and reset using setrand: from a given seed, and given sequence of randoms, the exact same values will be generated. The same seed is used at each startup, reseed the generator yourself if this is a problem. Note that internal functions also call the random number generator; adding such a function call in the middle of your code will change the numbers produced.

Technical note. Up to version 2.4 included, the internal generator produced pseudo-random numbers by means of linear congruences, which were not well distributed in arithmetic progressions. We now use Brent’s XORGEN algorithm, based on Feedback Shift Registers, see

http://wwwmaths.anu.edu.au/~brent/random.html. The generator has period 2^{4096} − 1, passes

the Crush battery of statistical tests of L’Ecuyer and Simard, but is not suitable for cryptographic purposes: one can reconstruct the state vector from a small sample of consecutive values, thus predicting the entire sequence.

    The library syntax is GEN genrand(GEN N = NULL).
    Also available: GEN ellrandom(GEN E) and GEN ffrandom(GEN a).

3.2.49 real(x). Real part of x. In the case where x is a quadratic number, this is the coefficient of 1 in the “canonical” integral basis (1, ω).

The library syntax is GEN greal(GEN x).

3.2.50 round(x, {&e}). If x is in R, rounds x to the nearest integer (rounding to +∞ in case of ties), then and sets e to the number of error bits, that is the binary exponent of the difference between the original and the rounded value (the “fractional part”). If the exponent of x is too large compared to its precision (i.e. e > 0), the result is undefined and an error occurs if e was not given.

Important remark. Contrary to the other truncation functions, this function operates on every coefficient at every level of a PARI object. For example

truncate (\frac{2.4 ∗ X^{2} − 1.7}{X}) = 2.4 ∗ X,

whereas

round (\frac{2.4 ∗ X^{2} − 1.7}{X}) = \frac{2 ∗ X^{2} − 2}{X}.

An important use of round is to get exact results after an approximate computation, when theory tells you that the coefficients must be integers.

The library syntax is GEN round0(GEN x, GEN *e = NULL). Also available are GEN grnd-

toi(GEN x, long *e) and GEN ground(GEN x).

3.2.51 simplify(x). This function simplifies x as much as it can. Specifically, a complex or quadratic number whose imaginary part is the integer 0 (i.e. not Mod(0,2) or 0.E-28) is converted to its real part, and a polynomial of degree 0 is converted to its constant term. Simplifications occur recursively.

This function is especially useful before using arithmetic functions, which expect integer arguments:

    ? x = 2 + y - y
    %1 = 2
    ? isprime(x)
      ***   at top-level: isprime(x)
      ***                 ^----------
      *** isprime: not an integer argument in an arithmetic function
    ? type(x)
    %2 = "t_POL"
    ? type(simplify(x))
    %3 = "t_INT"

Note that GP results are simplified as above before they are stored in the history. (Unless you disable automatic simplification with \y, that is.) In particular

    ? type(%1)
    %4 = "t_INT"

The library syntax is GEN simplify(GEN x).

3.2.52 sizebyte(x). Outputs the total number of bytes occupied by the tree representing the PARI object x.

    The library syntax is long gsizebyte(GEN x). Also available is long gsizeword(GEN x)

returning a number of words.

3.2.53 sizedigit(x). Outputs a quick bound for the number of decimal digits of (the components of) x, off by at most 1. If you want the exact value, you can use #Str(x), which is slower.

    The library syntax is long sizedigit(GEN x).

3.2.54 truncate(x, {&e}). Truncates x and sets e to the number of error bits. When x is in R, this means that the part after the decimal point is chopped away, e is the binary exponent of the difference between the original and the truncated value (the “fractional part”). If the exponent of x is too large compared to its precision (i.e. e > 0), the result is undefined and an error occurs if e was not given. The function applies componentwise on vector / matrices; e is then the maximal number of error bits. If x is a rational function, the result is the “integer part” (Euclidean quotient of numerator by denominator) and e is not set.

Note a very special use of truncate: when applied to a power series, it transforms it into a polynomial or a rational function with denominator a power of X, by chopping away the O(X^{k}). Similarly, when applied to a p-adic number, it transforms it into an integer or a rational number by chopping away the O(p^{k}).

The library syntax is GEN trunc0(GEN x, GEN *e = NULL). The following functions are also

available: GEN gtrunc(GEN x) and GEN gcvtoi(GEN x, long *e).

3.2.55 valuation(x, p). Computes the highest exponent of p dividing x. If p is of type integer, x must be an integer, an intmod whose modulus is divisible by p, a fraction, a q-adic number with q = p, or a polynomial or power series in which case the valuation is the minimum of the valuation of the coefficients.

If p is of type polynomial, x must be of type polynomial or rational function, and also a power series if x is a monomial. Finally, the valuation of a vector, complex or quadratic number is the minimum of the component valuations.

If x = 0, the result is LONG_MAX (2^{31} − 1 for 32-bit machines or 2^{63} − 1 for 64-bit machines) if x is an exact object. If x is a p-adic numbers or power series, the result is the exponent of the zero. Any other type combinations gives an error.

    The library syntax is long gvaluation(GEN x, GEN p).

3.2.56 variable({x}). Gives the main variable of the object x (the variable with the highest priority used in x), and p if x is a p-adic number. Return 0 if x has no variable associated to it.

    ? variable(x^2 + y)
    %1 = x
    ? variable(1 + O(5^2))
    %2 = 5
    ? variable([x,y,z,t])
    %3 = x
    ? variable(1)
    %4 = 0

The construction

       if (!variable(x),...)

can be used to test whether a variable is attached to x.

If x is omitted, returns the list of user variables known to the interpreter, by order of decreasing priority. (Highest priority is x, which always come first.)

The library syntax is GEN gpolvar(GEN x = NULL). However, in library mode, this function should not be used for x non-NULL, since gvar is more appropriate. Instead, for x a p-adic (type t_PADIC), p is gel(x, 2); otherwise, use long gvar(GEN x) which returns the variable number of x if it exists, NO VARIABLE otherwise, which satisfies the property varncmp(NO VARIABLE, v) > 0 for all valid variable number v, i.e. it has lower priority than any variable.

3.3 Transcendental functions.

Since the values of transcendental functions cannot be exactly represented, these functions will always return an inexact object: a real number, a complex number, a p-adic number or a power series. All these objects have a certain finite precision.

As a general rule, which of course in some cases may have exceptions, transcendental functions operate in the following way:

• If the argument is either a real number or an inexact complex number (like 1.0 + I or Pi*I but not 2 - 3*I), then the computation is done with the precision of the argument. In the example below, we see that changing the precision to 50 digits does not matter, because x only had a precision of 19 digits.

    ? \p 15
       realprecision = 19 significant digits (15 digits displayed)
    ? x = Pi/4
    %1 = 0.785398163397448
    ? \p 50
       realprecision = 57 significant digits (50 digits displayed)
    ? sin(x)
    %2 = 0.7071067811865475244

Note that even if the argument is real, the result may be complex (e.g. acos(2.0) or acosh(0.0)). See each individual function help for the definition of the branch cuts and choice of principal value.

• If the argument is either an integer, a rational, an exact complex number or a quadratic number, it is first converted to a real or complex number using the current precision held in the default realprecision. This precision (the number of decimal digits) can be changed using \p or default(realprecision,...)). After this conversion, the computation proceeds as above for real or complex arguments.

In library mode, the realprecision does not matter; instead the precision is taken from the prec parameter which every transcendental function has. As in gp, this prec is not used when the argument to a function is already inexact. Note that the argument prec stands for the length in words of a real number, including codewords. Hence we must have prec ≥ 3.

Some accuracies attainable on 32-bit machines cannot be attained on 64-bit machines for parity reasons. For example the default gp accuracy is 28 decimal digits on 32-bit machines, corresponding to prec having the value 5, but this cannot be attained on 64-bit machines.

• If the argument is a polmod (representing an algebraic number), then the function is evaluated for every possible complex embedding of that algebraic number. A column vector of results is returned, with one component for each complex embedding. Therefore, the number of components equals the degree of the t_POLMOD modulus.

• If the argument is an intmod or a p-adic, at present only a few functions like sqrt (square root), sqr (square), log, exp, powering, teichmuller (Teichmüller character) and agm (arithmeticgeometric mean) are implemented.

Note that in the case of a 2-adic number, sqr(x) may not be identical to x ∗ x: for example if x = 1+O(2^{5}) and y = 1+O(2^{5}) then x∗y = 1+O(2^{5}) while sqr(x) = 1+O(2^{6}). Here, x∗x yields the same result as sqr(x) since the two operands are known to be identical. The same statement holds true for p-adics raised to the power n, where v_{p}(n) > 0.

Remark. If we wanted to be strictly consistent with the PARI philosophy, we should have x ∗ y = (4 mod 8) and sqr(x) = (4 mod 32) when both x and y are congruent to 2 modulo 4. However, since intmod is an exact object, PARI assumes that the modulus must not change, and the result is hence (0 mod 4) in both cases. On the other hand, p-adics are not exact objects, hence are treated differently.

• If the argument is a polynomial, a power series or a rational function, it is, if necessary, first converted to a power series using the current series precision, held in the default seriesprecision. This precision (the number of significant terms) can be changed using \ps or default(seriesprecision,...). Then the Taylor series expansion of the function around X = 0 (where X is the main variable) is computed to a number of terms depending on the number of terms of the argument and the function being computed.

Under gp this again is transparent to the user. When programming in library mode, however, it is strongly advised to perform an explicit conversion to a power series first, as in x = gtoser(x, seriesprec), where the number of significant terms seriesprec can be specified explicitly. If you do not do this, a global variable precdl is used instead, to convert polynomials and rational functions to a power series with a reasonable number of terms; tampering with the value of this global variable is deprecated and strongly discouraged.

• If the argument is a vector or a matrix, the result is the componentwise evaluation of the function. In particular, transcendental functions on square matrices, which are not implemented in the present version 2.7.2, will have a different name if they are implemented some day.

3.3.1 ^. If y is not of type integer, x^y has the same effect as exp(y*log(x)). It can be applied to p-adic numbers as well as to the more usual types.

    The library syntax is GEN gpow(GEN x, GEN n, long prec) for x^n.

3.3.2 Catalan. Catalan’s constant G = ∑_{n>=0} \frac{(−1)^{n}}{(2n+1)^{2}} = 0.91596 · · ·. Note that Catalan is one of the few reserved names which cannot be used for user variables.

    The library syntax is GEN mpcatalan(long prec).

3.3.3 Euler. Euler’s constant γ = 0.57721 · · ·. Note that Euler is one of the few reserved names which cannot be used for user variables.

    The library syntax is GEN mpeuler(long prec).

3.3.4 I. The complex number \sqrt{−1}.

The library syntax is GEN gen_I().

3.3.5 Pi. The constant π (3.14159 ⋯). Note that Pi is one of the few reserved names which cannot be used for user variables.

The library syntax is GEN mppi(long prec).

3.3.6 abs(x). Absolute value of x (modulus if x is complex). Rational functions are not allowed. Contrary to most transcendental functions, an exact argument is not converted to a real number before applying abs and an exact result is returned if possible.

    ? abs(-1)
    %1 = 1
    ? abs(3/7 + 4/7*I)
    %2 = 5/7
    ? abs(1 + I)
    %3 = 1.414213562373095048801688724

If x is a polynomial, returns −x if the leading coefficient is real and negative else returns x. For a power series, the constant coefficient is considered instead.

    The library syntax is GEN gabs(GEN x, long prec).

3.3.7 acos(x). Principal branch of cos^{−1}(x) = −i log(x + i\sqrt{1 − x^{2}}). In particular, Re(acos(x)) ∈ [0, π] and if x ∈ R and |x| > 1, then acos(x) is complex. The branch cut is in two pieces: ] − ∞, −1] , continuous with quadrant II, and [1, +∞[, continuous with quadrant IV. We have acos(x) = π/2 − asin(x) for all x.

    The library syntax is GEN gacos(GEN x, long prec).

3.3.8 acosh(x). Principal branch of cosh^{−1}(x) = 2 log(\sqrt{(x + 1)/2} + \sqrt{(x − 1)/2}). In particular, Re(acosh(x)) ≥ 0 and In(acosh(x)) ∈] − π, π]0; if x ∈ R and x < 1, then acosh(x) is complex.

    The library syntax is GEN gacosh(GEN x, long prec).

3.3.9 agm(x, y). Arithmetic-geometric mean of x and y. In the case of complex or negative numbers, the optimal AGM is returned (the largest in absolute value over all choices of the signs of the square roots). p-adic or power series arguments are also allowed. Note that a p-adic agm exists only if x/y is congruent to 1 modulo p (modulo 16 for p = 2). x and y cannot both be vectors or matrices.

    The library syntax is GEN agm(GEN x, GEN y, long prec).

3.3.10 arg(x). Argument of the complex number x, such that −π < arg(x) ≤ π.

    The library syntax is GEN garg(GEN x, long prec).

3.3.11 asin(x). Principal branch of sin^{−1}(x) = −i log(ix + \sqrt{1 − x^{2}}). In particular, Re(asin(x)) ∈ [−π/2, π/2] and if x ∈ R and |x| > 1 then asin(x) is complex. The branch cut is in two pieces: ] − ∞, −1], continuous with quadrant II, and [1, +∞[ continuous with quadrant IV. The function satisfies iasin(x) = asinh(ix).

    The library syntax is GEN gasin(GEN x, long prec).

3.3.12 asinh(x). Principal branch of sinh^{−1}(x) = log(x + \sqrt{1 + x^{2}}). In particular Im(asinh(x)) ∈ [−π/2, π/2]. The branch cut is in two pieces: [-i oo ,-i], continuous with quadrant III and [i,+i oo [ continuous with quadrant I.

    The library syntax is GEN gasinh(GEN x, long prec).

3.3.13 atan(x). Principal branch of tan^{−1}(x) = log((1 + ix)/(1 − ix))/2i. In particular the real part of atan(x)) belongs to ] − π/2, π/2[. The branch cut is in two pieces: ] − i∞, −i[, continuous with quadrant IV, and ]i, +i∞[ continuous with quadrant II. The function satisfies iatan(x) = −iatanh(ix) for all x ≠ ±i.

    The library syntax is GEN gatan(GEN x, long prec).

3.3.14 atanh(x). Principal branch of tanh^{−1}(x) = log((1 + x)/(1 − x))/2. In particular the imaginary part of atanh(x) belongs to [−π/2, π/2]; if x ∈ R and |x| > 1 then atanh(x) is complex.

    The library syntax is GEN gatanh(GEN x, long prec).

3.3.15 bernfrac(x). Bernoulli number B_{x}, where B_{0} = 1, B_{1} = −1/2, B_{2} = 1/6,…, expressed as a rational number. The argument x should be of type integer.

The library syntax is GEN bernfrac(long x).

3.3.16 bernpol(n, {v =′ x}). Bernoulli polynomial B_{n} in variable v.

    ? bernpol(1)
    %1 = x - 1/2
    ? bernpol(3)
    %2 = x^3 - 3/2*x^2 + 1/2*x

The library syntax is GEN bernpol(long n, long v = -1), where v is a variable number.

3.3.17 bernreal(x). Bernoulli number B_{x}, as bernfrac, but B_{x} is returned as a real number (with the current precision).

    The library syntax is GEN bernreal(long x, long prec).

3.3.18 bernvec(x). Creates a vector containing, as rational numbers, the Bernoulli numbers B_{0}, B_{2},…, B_{2x}. This routine is obsolete. Use bernfrac instead each time you need a Bernoulli number in exact form.

Note. This routine is implemented using repeated independent calls to bernfrac, which is faster than the standard recursion in exact arithmetic. It is only kept for backward compatibility: it is not faster than individual calls to bernfrac, its output uses a lot of memory space, and coping with the index shift is awkward.

The library syntax is GEN bernvec(long x).

3.3.19 besselh1(nu, x). H^{1}-Bessel function of index nu and argument x.

    The library syntax is GEN hbessel1(GEN nu, GEN x, long prec).

3.3.20 besselh2(nu, x). H^{2}-Bessel function of index nu and argument x.

    The library syntax is GEN hbessel2(GEN nu, GEN x, long prec).

3.3.21 besseli(nu, x). I-Bessel function of index nu and argument x. If x converts to a power series, the initial factor (x/2)^{ν}/Γ(ν + 1) is omitted (since it cannot be represented in PARI when ν is not integral).

    The library syntax is GEN ibessel(GEN nu, GEN x, long prec).

3.3.22 besselj(nu, x). J-Bessel function of index nu and argument x. If x converts to a power series, the initial factor (x/2)^{ν}/Γ(ν + 1) is omitted (since it cannot be represented in PARI when ν is not integral).

    The library syntax is GEN jbessel(GEN nu, GEN x, long prec).

3.3.23 besseljh(n, x). J-Bessel function of half integral index. More precisely, besseljh(n, x) computes J_{n+1/2}(x) where n must be of type integer, and x is any element of C. In the present version 2.7.2, this function is not very accurate when x is small.

    The library syntax is GEN jbesselh(GEN n, GEN x, long prec).

3.3.24 besselk(nu, x). K-Bessel function of index nu and argument x.

    The library syntax is GEN kbessel(GEN nu, GEN x, long prec).

3.3.25 besseln(nu, x). N-Bessel function of index nu and argument x.

    The library syntax is GEN nbessel(GEN nu, GEN x, long prec).

3.3.26 cos(x). Cosine of x.

    The library syntax is GEN gcos(GEN x, long prec).

3.3.27 cosh(x). Hyperbolic cosine of x.

    The library syntax is GEN gcosh(GEN x, long prec).

3.3.28 cotan(x). Cotangent of x.

    The library syntax is GEN gcotan(GEN x, long prec).

3.3.29 dilog(x). Principal branch of the dilogarithm of x, i.e. analytic continuation of the power series log_{2}(x) = ∑_{n≥1} x^{n}/n^{2}.

    The library syntax is GEN dilog(GEN x, long prec).

3.3.30 eint1(x, \{n\}). Exponential integral ∫_{x}^{∞} \frac{e^{−t}}{t} dt = incgam(0, x), where the latter expression extends the function definition from real x > 0 to all complex x ≠ 0.

If n is present, we must have x > 0; the function returns the n-dimensional vector [eint1(x), …, eint1(nx)]. Contrary to other transcendental functions, and to the default case (n omitted), the values are correct up to a bounded absolute, rather than relative, error 10^{−}n, where n is precision(x) if x is a t_REAL and defaults to realprecision otherwise. (In the most important application, to the computation of L-functions via approximate functional equations, those values appear as weights in long sums and small individual relative errors are less useful than controlling the absolute error.) This is faster than repeatedly calling eint1(i * x), but less precise.

    The library syntax is GEN veceint1(GEN x, GEN n = NULL, long prec). Also available is
GEN eint1(GEN x, long prec).

3.3.31 erfc(x). Complementary error function, analytic continuation of (2/\sqrt{π}) ∫_{x}^{∞} e^{−t^{2}} dt = incgam(1/2, x^{2})/\sqrt{π}, where the latter expression extends the function definition from real x to all complex x ≠ 0.

The library syntax is GEN gerfc(GEN x, long prec).

3.3.32 eta(z, {flag = 0}). Variants of Dedekind’s η function. If flag = 0, return ∏_{n=1}^{∞}(1 − q^{n}), where q depends on x in the following way:

• q = e^{2iπx} if x is a complex number (which must then have positive imaginary part); notice that the factor q^{1/24} is missing!

• q = x if x is a t_PADIC, or can be converted to a power series (which must then have positive valuation).

If flag is non-zero, x is converted to a complex number and we return the true η function, q^{1/24} ∏_{n=1}^{∞}(1 − q^{n}), where q = e^{2iπx}.

The library syntax is GEN eta0(GEN z, long flag, long prec).
Also available is GEN trueeta(GEN x, long prec) (flag = 1).

3.3.33 exp(x). Exponential of x. p-adic arguments with positive valuation are accepted.

The library syntax is GEN gexp(GEN x, long prec). For a t_PADIC x, the function GEN Qp_exp(GEN x) is also available.

3.3.34 expm1(x). Return exp(x) − 1, computed in a way that is also accurate when the real part of x is near 0. Only accept real or complex arguments. A naive direct computation would suffer from catastrophic cancellation; PARI’s direct computation of exp(x) alleviates this well known problem at the expense of computing exp(x) to a higher accuracy when x is small. Using expm1 is recommended instead:

? default(realprecision, 10000); x = 1e-100;
? a = expm1(x);
time = 4 ms.
? b = exp(x)-1;
time = 28 ms.
? default(realprecision, 10040); x = 1e-100;
? c = expm1(x);  \\ reference point
? abs(a-c)/c  \\ relative error in expm1(x)
%7 = 0.E-10017
? abs(b-c)/c  \\ relative error in exp(x)-1
%8 = 1.7907031188259675794 E-9919

As the example above shows, when x is near 0, expm1 is both faster and more accurate than

exp(x)-1.
The library syntax is GEN gexpm1(GEN x, long prec).

3.3.35 gamma(s). For s a complex number, evaluates Euler’s gamma function

Γ(s) = ∫_{0}^{∞} t^{s−1} exp(−t) dt.

Error if s is a non-positive integer, where Γ has a pole.

For s a t_PADIC, evaluates the Morita gamma function at s, that is the unique continuous p-adic function on the p-adic integers extending Γ_{p}(k) = (−1)^{k} ∏′_{j<k} j, where the prime means that p does not divide j.

    ? gamma(1/4 + O(5^10))
    %1= 1 + 4*5 + 3*5^4 + 5^6 + 5^7 + 4*5^9 + O(5^10)
    ? algdep(%,4)
    %2 = x^4 + 4*x^2 + 5

The library syntax is GEN ggamma(GEN s, long prec). For a t_PADIC x, the function GEN Qp_gamma(GEN x) is also available.

3.3.36 gammah(x). Gamma function evaluated at the argument x + 1/2.

    The library syntax is GEN ggammah(GEN x, long prec).

3.3.37 hyperu(a, b, x). U-confluent hypergeometric function with parameters a and b. The parameters a and b can be complex but the present implementation requires x to be positive.

    The library syntax is GEN hyperu(GEN a, GEN b, GEN x, long prec).

3.3.38 incgam(s, x, {g}). Incomplete gamma function ∫_{x}^{∞} e^{−t}t^{s−1} dt, extended by analytic continuation to all complex x, s not both 0. The relative error is bounded in terms of the precision of s (the accuracy of x is ignored when determining the output precision). When g is given, assume that g = Γ(s). For small |x|, this will speed up the computation.

    The library syntax is GEN incgam0(GEN s, GEN x, GEN g = NULL, long prec). Also avail-
able is GEN incgam(GEN s, GEN x, long prec).

3.3.39 incgamc(s, x). Complementary incomplete gamma function. The arguments x and s are complex numbers such that s is not a pole of Γ and |x|/(|s|+1) is not much larger than 1 (otherwise the convergence is very slow). The result returned is ∫_{0}^{x} e^{−t}t^{s−1} dt.

    The library syntax is GEN incgamc(GEN s, GEN x, long prec).

3.3.40 lambertw(y). Lambert W function, solution of the implicit equation xe^{x} = y, for y > 0.

    The library syntax is GEN glambertW(GEN y, long prec).

3.3.41 lngamma(x). Principal branch of the logarithm of the gamma function of x. This function is analytic on the complex plane with non-positive integers removed, and can have much larger arguments than gamma itself.

For x a power series such that x(0) is not a pole of gamma, compute the Taylor expansion. (PARI only knows about regular power series and can’t include logarithmic terms.)

    ? lngamma(1+x+O(x^2))
    %1 = -0.57721566490153286060651209008240243104*x + O(x^2)
    ? lngamma(x+O(x^2))
     ***   at top-level: lngamma(x+O(x^2))
     ***                 ^-----------------
     *** lngamma: domain error in lngamma: valuation != 0
    ? lngamma(-1+x+O(x^2))
     *** lngamma: Warning: normalizing a series with 0 leading term.
     ***   at top-level: lngamma(-1+x+O(x^2))
     ***                 ^--------------------
     *** lngamma: domain error in intformal: residue(series, pole) != 0
    The library syntax is GEN glngamma(GEN x, long prec).

3.3.42 log(x). Principal branch of the natural logarithm of x ∈ C^{∗}, i.e. such that Im(log(x)) ∈ ] − π, π]. The branch cut lies along the negative real axis, continuous with quadrant 2, i.e. such that lim_{b→0^{+}} log(a + bi) = log a for a ∈ R^{∗}. The result is complex (with imaginary part equal to π) if x ∈ R and x < 0. In general, the algorithm uses the formula

log(x) ≈ \frac{π}{2agm(1, 4/s)} − m log 2,

if s = x2^{m} is large enough. (The result is exact to B bits provided s > 2^{B/2}.) At low accuracies, the series expansion near 1 is used.

p-adic arguments are also accepted for x, with the convention that log(p) = 0. Hence in particular exp(log(x))/x is not in general equal to 1 but to a (p − 1)-th root of unity (or ±1 if p = 2) times a power of p.

The library syntax is GEN glog(GEN x, long prec). For a t_PADIC x, the function GEN Qp_log(GEN x) is also available.

3.3.43 polylog(m, x, {flag = 0}). One of the different polylogarithms, depending on flag:

If flag = 0 or is omitted: m^{th} polylogarithm of x, i.e. analytic continuation of the power series Li_{m}(x) = ∑_{n≥1} x^{n}/n^{m} (x < 1). Uses the functional equation linking the values at x and 1/x to restrict to the case |x| ≤ 1, then the power series when |x|^{2} ≤ 1/2, and the power series expansion in log(x) otherwise.

Using flag, computes a modified m^{th} polylogarithm of x. We use Zagier’s notations; let ℜ_{m} denote ℜ or ℑ depending on whether m is odd or even:

If flag = 1: compute D̃_{m}(x), defined for |x| ≤ 1 by

ℜ_{m} ( ∑_{k=0}^{m−1} \frac{(− log |x|)^{k}}{k!} Li_{m−k}(x) + \frac{(− log |x|)^{m−1}}{m!} log |1 − x|) .

If flag = 2: compute D_{m}(x), defined for |x| ≤ 1 by

ℜ_{m} ( ∑_{k=0}^{m−1} \frac{(− log |x|)^{k}}{k!} Li_{m−k}(x) − \frac{1}{2} \frac{(− log |x|)^{m}}{m!} ) .

If flag = 3: compute P_{m}(x), defined for |x| ≤ 1 by

ℜ_{m} ( ∑_{k=0}^{m−1} \frac{2^{k}B_{k}}{k!} (log |x|)^{k}Li_{m−k}(x) − \frac{2^{m−1}B_{m}}{m!} (log |x|)^{m}) .

These three functions satisfy the functional equation f_{m}(1/x) = (−1)^{m−1}f_{m}(x).

The library syntax is GEN polylog0(long m, GEN x, long flag, long prec). Also avail-
able is GEN gpolylog(long m, GEN x, long prec) (flag= 0).

3.3.44 psi(x). The ψ-function of x, i.e. the logarithmic derivative Γ′(x)/Γ(x).

The library syntax is GEN gpsi(GEN x, long prec).

3.3.45 sin(x). Sine of x.

The library syntax is GEN gsin(GEN x, long prec).

3.3.46 sinh(x). Hyperbolic sine of x.

The library syntax is GEN gsinh(GEN x, long prec).

3.3.47 sqr(x). Square of x. This operation is not completely straightforward, i.e. identical to x∗x, since it can usually be computed more efficiently (roughly one-half of the elementary multiplications can be saved). Also, squaring a 2-adic number increases its precision. For example,

? (1 + O(2^4))^2
%1 = 1 + O(2^5)
? (1 + O(2^4)) * (1 + O(2^4))
%2 = 1 + O(2^4)

Note that this function is also called whenever one multiplies two objects which are known to be identical, e.g. they are the value of the same variable, or we are computing a power.

? x = (1 + O(2^4)); x * x
%3 = 1 + O(2^5)
? (1 + O(2^4))^4
%4 = 1 + O(2^6)

(note the difference between %2 and %3 above).

The library syntax is GEN gsqr(GEN x).

3.3.48 sqrt(x). Principal branch of the square root of x, defined as \sqrt{x} = exp(log x/2). In particular, we have Arg(sqrt(x)) ∈ ] − π/2, π/2], and if x ∈ R and x < 0, then the result is complex with positive imaginary part.

Intmod a prime p, t_PADIC and t_FFELT are allowed as arguments. In the first 2 cases (t_INTMOD, t_PADIC), the square root (if it exists) which is returned is the one whose first p-adic digit is in the interval [0, p/2]. For other arguments, the result is undefined.

The library syntax is GEN gsqrt(GEN x, long prec). For a t_PADIC x, the function GEN Qp_sqrt(GEN x) is also available.

3.3.49 sqrtn(x, n, {&z}). Principal branch of the nth root of x, i.e. such that Arg(sqrt(x)) ∈ ] − π/n, π/n]. Intmod a prime and p-adics are allowed as arguments.

If z is present, it is set to a suitable root of unity allowing to recover all the other roots. If it was not possible, z is set to zero. In the case this argument is present and no square root exist, 0 is returned instead or raising an error.

? sqrtn(Mod(2,7), 2)
%1 = Mod(4, 7)
? sqrtn(Mod(2,7), 2, &z); z
%2 = Mod(6, 7)
? sqrtn(Mod(2,7), 3)
  ***   at top-level: sqrtn(Mod(2,7),3)
  ***                 ^-----------------
  *** sqrtn: nth-root does not exist in gsqrtn.
? sqrtn(Mod(2,7), 3,  &z)
%2 = 0
? z
%3 = 0

The following script computes all roots in all possible cases:

sqrtnall(x,n)=
{ my(V,r,z,r2);
  r = sqrtn(x,n, &z);
  if (!z, error("Impossible case in sqrtn"));
  if (type(x) == "t_INTMOD" || type(x)=="t_PADIC",
    r2 = r*z; n = 1;
    while (r2!=r, r2*=z;n++));
  V = vector(n); V[1] = r;
  for(i=2, n, V[i] = V[i-1]*z);
  V
}
addhelp(sqrtnall,"sqrtnall(x,n):compute the vector of nth-roots of x");
The library syntax is GEN gsqrtn(GEN x, GEN n, GEN *z = NULL, long prec). If x is a
t_PADIC, the function GEN Qp_sqrt(GEN x, GEN n, GEN *z) is also available.

3.3.50 tan(x). Tangent of x.

The library syntax is GEN gtan(GEN x, long prec).

3.3.51 tanh(x). Hyperbolic tangent of x.

    The library syntax is GEN gtanh(GEN x, long prec).

3.3.52 teichmuller(x). Teichmüller character of the p-adic number x, i.e. the unique (p − 1)-th root of unity congruent to x/p^{v_{p}(x)} modulo p.

The library syntax is GEN teich(GEN x).

3.3.53 theta(q, z). Jacobi sine theta-function

θ_{1}(z, q) = 2q^{1/4} ∑_{n≥0}(−1)^{n}q^{n(n+1)} sin((2n + 1)z).

    The library syntax is GEN theta(GEN q, GEN z, long prec).

3.3.54 thetanullk(q, k). k-th derivative at z = 0 of theta(q, z).

    The library syntax is GEN thetanullk(GEN q, long k, long prec).

GEN vecthetanullk(GEN q, long k, long prec) returns the vector of all \frac{d^{i}θ}{dz^{i}} (q, 0) for all odd

i = 1, 3, . . . , 2k − 1. GEN vecthetanullk_tau(GEN tau, long k, long prec) returns vec-
thetanullk tau at q = exp(2iπtau).

3.3.55 weber(x, {flag = 0}). One of Weber’s three f functions. If flag = 0, returns

f(x) = exp(−iπ/24) · η((x + 1)/2) / η(x) such that j = (f^{24} − 16)^{3}/f^{24} ,

where j is the elliptic j-invariant (see the function ellj). If flag = 1, returns

f_{1}(x) = η(x/2) / η(x) such that j = (f_{1}^{24} + 16)^{3}/f_{1}^{24} .

Finally, if flag = 2, returns

f_{2}(x) = \sqrt{2}η(2x) / η(x) such that j = (f_{2}^{24} + 16)^{3}/f_{2}^{24}.

Note the identities f^{8} = f_{1}^{8} + f_{2}^{8} and ff_{1}f_{2} = \sqrt{2}.

    The library syntax is GEN weber0(GEN x, long flag, long prec). Also available are GEN
weberf(GEN x, long prec), GEN weberf1(GEN x, long prec) and GEN weberf2(GEN x, long
prec).

3.3.56 zeta(s). For s a complex number, Riemann’s zeta function ζ(s) = ∑_{n≥1} n^{−s}, computed using the Euler-Maclaurin summation formula, except when s is of type integer, in which case it is computed using Bernoulli numbers for s ≤ 0 or s > 0 and even, and using modular forms for s > 0 and odd.

For s a p-adic number, Kubota-Leopoldt zeta function at s, that is the unique continuous padic function on the p-adic integers that interpolates the values of (1−p^{−k})ζ(k) at negative integers k such that k ≡ 1 (mod p − 1) (resp. k is odd) if p is odd (resp. p = 2).

    The library syntax is GEN gzeta(GEN s, long prec).

3.4 Arithmetic functions.

These functions are by definition functions whose natural domain of definition is either Z (or Z_{>0}). The way these functions are used is completely different from transcendental functions in that there are no automatic type conversions: in general only integers are accepted as arguments. An integer argument N can be given in the following alternate formats:

• t_MAT: its factorization fa = factor(N),

• t_VEC: a pair [N, fa] giving both the integer and its factorization.

This allows to compute different arithmetic functions at a given N while factoring the latter only once.

      ? N = 10!; faN = factor(N);
      ? eulerphi(N)
      %2 = 829440
      ? eulerphi(faN)
      %3 = 829440
      ? eulerphi(S = [N, faN])
      %4 = 829440
      ? sigma(S)
      %5 = 15334088

3.4.1 Arithmetic functions and the factoring engine. All arithmetic functions in the narrow sense of the word — Euler’s totient function, the Moebius function, the sums over divisors or powers of divisors etc.— call, after trial division by small primes, the same versatile factoring machinery described under factorint. It includes Shanks SQUFOF, Pollard Rho, ECM and MPQS stages, and has an early exit option for the functions moebius and (the integer function underlying) issquarefree. This machinery relies on a fairly strong probabilistic primality test, see ispseudoprime, but you may also set

      default(factor_proven, 1)

to ensure that all tentative factorizations are fully proven. This should not slow down PARI too much, unless prime numbers with hundreds of decimal digits occur frequently in your application.

3.4.2 Orders in finite groups and Discrete Logarithm functions.

The following functions compute the order of an element in a finite group: ellorder (the rational points on an elliptic curve defined over a finite field), fforder (the multiplicative group of a finite field), znorder (the invertible elements in Z/nZ). The following functions compute discrete logarithms in the same groups (whenever this is meaningful) elllog, fflog, znlog.

All such functions allow an optional argument specifying an integer N, representing the order of the group. (The order functions also allows any non-zero multiple of the order, with a minor loss of efficiency.) That optional argument follows the same format as given above:

• t_INT: the integer N,

• t_MAT: the factorization fa = factor(N),

• t_VEC: this is the preferred format and provides both the integer N and its factorization in a two-component vector [N, fa].

When the group is fixed and many orders or discrete logarithms will be computed, it is much more efficient to initialize this data once and for all and pass it to the relevant functions, as in

? p = nextprime(10^40);
? v = [p-1, factor(p-1)]; \\ data for discrete log & order computations
? znorder(Mod(2,p), v)
%3 = 500000000000000000000000000028
? g = znprimroot(p);
? znlog(2, g, v)
%5 = 543038070904014908801878611374

3.4.3 addprimes({x = [ ]}). Adds the integers contained in the vector x (or the single integer x) to a special table of “user-defined primes”, and returns that table. Whenever factor is subsequently called, it will trial divide by the elements in this table. If x is empty or omitted, just returns the current list of extra primes.

The entries in x must be primes: there is no internal check, even if the factor_proven default is set. To remove primes from the list use removeprimes.

The library syntax is GEN addprimes(GEN x = NULL).

3.4.4 bestappr(x, {B}). Using variants of the extended Euclidean algorithm, returns a rational approximation a/b to x, whose denominator is limited by B, if present. If B is omitted, return the best approximation affordable given the input accuracy; if you are looking for true rational numbers, presumably approximated to sufficient accuracy, you should first try that option. Otherwise, B must be a positive real scalar (impose 0 < b ≤ B).

• If x is a t_REAL or a t_FRAC, this function uses continued fractions.

? bestappr(Pi, 100)
%1 = 22/7
? bestappr(0.1428571428571428571428571429)
%2 = 1/7
? bestappr([Pi, sqrt(2) + 'x], 10^3)
%3 = [355/113, x + 1393/985]

By definition, a/b is the best rational approximation to x if |bx − a| < |vx − u| for all integers (u, v) with 0 < v ≤ B. (Which implies that n/d is a convergent of the continued fraction of x.)

• If x is a t_INTMOD modulo N or a t_PADIC of precision N = p^{k}, this function performs rational modular reconstruction modulo N. The routine then returns the unique rational number a/b in coprime integers |a| < N/2B and b ≤ B which is congruent to x modulo N. Omitting B amounts to choosing it of the order of \sqrt{N/2}. If rational reconstruction is not possible (no suitable a/b exists), returns [].

? bestappr(Mod(18526731858, 11^10))
%1 = 1/7
? bestappr(Mod(18526731858, 11^20))
%2 = []
? bestappr(3 + 5 + 3*5^2 + 5^3 + 3*5^4 + 5^5 + 3*5^6 + O(5^7))
%2 = -1/3

In most concrete uses, B is a prime power and we performed Hensel lifting to obtain x.

The function applies recursively to components of complex objects (polynomials, vectors, …). If rational reconstruction fails for even a single entry, return [].

The library syntax is GEN bestappr(GEN x, GEN B = NULL).

3.4.5 bestapprPade(x, {B}). Using variants of the extended Euclidean algorithm, returns a rational function approximation a/b to x, whose denominator is limited by B, if present. If B is omitted, return the best approximation affordable given the input accuracy; if you are looking for true rational functions, presumably approximated to sufficient accuracy, you should first try that option. Otherwise, B must be a non-negative real (impose 0 ≤ degree(b) ≤ B).

• If x is a t_RFRAC or t_SER, this function uses continued fractions.

? bestapprPade((1-x^11)/(1-x)+O(x^11))
%1 = 1/(-x + 1)
? bestapprPade([1/(1+x+O(x^10)), (x^3-2)/(x^3+1)], 1)
%2 =  [1/(x + 1), -2]

• If x is a t_POLMOD modulo N or a t_SER of precision N = t^{k}, this function performs rational modular reconstruction modulo N. The routine then returns the unique rational function a/b in coprime polynomials, with degree(b) ≤ B which is congruent to x modulo N. Omitting B amounts to choosing it of the order of N/2. If rational reconstruction is not possible (no suitable a/b exists), returns [].

? bestapprPade(Mod(1+x+x^2+x^3+x^4, x^4-2))
%1 = (2*x - 1)/(x - 1)
? % * Mod(1,x^4-2)
%2 = Mod(x^3 + x^2 + x + 3, x^4 - 2)
? bestapprPade(Mod(1+x+x^2+x^3+x^5, x^9))
%2 = []
? bestapprPade(Mod(1+x+x^2+x^3+x^5, x^10))
%3 = (2*x^4 + x^3 - x - 1)/(-x^5 + x^3 + x^2 - 1)

The function applies recursively to components of complex objects (polynomials, vectors, …). If rational reconstruction fails for even a single entry, return [].

The library syntax is GEN bestapprPade(GEN x, long B).

3.4.6 bezout(x, y). Deprecated alias for gcdext

The library syntax is GEN gcdext0(GEN x, GEN y).

3.4.7 bigomega(x). Number of prime divisors of the integer |x| counted with multiplicity:

? factor(392)
%1 =
[2 3]
[7 2]
? bigomega(392)
%2 = 5;  \\ = 3+2
? omega(392)
%3 = 2;  \\ without multiplicity

The library syntax is long bigomega(GEN x).

3.4.8 binomial(x, y). binomial coefficient ( \begin{matrix}x \\ y \end{matrix} ). Here y must be an integer, but x can be any

PARI object.

    The library syntax is GEN binomial(GEN x, long y). The function GEN binomialuu(ulong

n, ulong k) is also available, and so is GEN vecbinome(long n), which returns a vector v with n + 1 components such that v[k + 1] = binomial(n, k) for k from 0 up to n.

3.4.9 chinese(x, {y}). If x and y are both intmods or both polmods, creates (with the same type) a z in the same residue class as x and in the same residue class as y, if it is possible.

    ? chinese(Mod(1,2), Mod(2,3))
    %1 = Mod(5, 6)
    ? chinese(Mod(x,x^2-1), Mod(x+1,x^2+1))
    %2 = Mod(-1/2*x^2 + x + 1/2, x^4 - 1)

This function also allows vector and matrix arguments, in which case the operation is recursively applied to each component of the vector or matrix.

    ? chinese([Mod(1,2),Mod(1,3)], [Mod(1,5),Mod(2,7)])
    %3 = [Mod(1, 10), Mod(16, 21)]

For polynomial arguments in the same variable, the function is applied to each coefficient; if the polynomials have different degrees, the high degree terms are copied verbatim in the result, as if the missing high degree terms in the polynomial of lowest degree had been Mod(0,1). Since the latter behavior is usually not the desired one, we propose to convert the polynomials to vectors of the same length first:

     ? P = x+1; Q = x^2+2*x+1;
     ? chinese(P*Mod(1,2), Q*Mod(1,3))
     %4 = Mod(1, 3)*x^2 + Mod(5, 6)*x + Mod(3, 6)
     ? chinese(Vec(P,3)*Mod(1,2), Vec(Q,3)*Mod(1,3))
     %5 = [Mod(1, 6), Mod(5, 6), Mod(4, 6)]
     ? Pol(%)
     %6 = Mod(1, 6)*x^2 + Mod(5, 6)*x + Mod(4, 6)

If y is omitted, and x is a vector, chinese is applied recursively to the components of x, yielding a residue belonging to the same class as all components of x.

Finally chinese(x, x) = x regardless of the type of x; this allows vector arguments to contain other data, so long as they are identical in both vectors.

    The library syntax is GEN chinese(GEN x, GEN y = NULL). GEN chinese1(GEN x) is also

available.

3.4.10 content(x). Computes the gcd of all the coefficients of x, when this gcd makes sense. This is the natural definition if x is a polynomial (and by extension a power series) or a vector/matrix. This is in general a weaker notion than the ideal generated by the coefficients:

    ? content(2*x+y)
    %1 = 1            \\ = gcd(2,y) over Q[y]

If x is a scalar, this simply returns the absolute value of x if x is rational (t_INT or t_FRAC), and either 1 (inexact input) or x (exact input) otherwise; the result should be identical to gcd(x,

0).

The content of a rational function is the ratio of the contents of the numerator and the denominator. In recursive structures, if a matrix or vector coefficient x appears, the gcd is taken not with x, but with its content:

    ? content([ [2], 4*matid(3) ])
    %1 = 2

The library syntax is GEN content(GEN x).

3.4.11 contfrac(x, {b}, {nmax}). Returns the row vector whose components are the partial quotients of the continued fraction expansion of x. In other words, a result [a_{0}, …, a_{n}] means that x ≈ a_{0} + 1/(a_{1} + … + 1/a_{n}). The output is normalized so that a_{n} ≠ 1 (unless we also have n = 0).

The number of partial quotients n + 1 is limited by nmax. If nmax is omitted, the expansion stops at the last significant partial quotient.

    ? \p19
      realprecision = 19 significant digits
    ? contfrac(Pi)
    %1 = [3, 7, 15, 1, 292, 1, 1, 1, 2, 1, 3, 1, 14, 2, 1, 1, 2, 2]
    ? contfrac(Pi,, 3)  \\ n = 2
    %2 = [3, 7, 15]

x can also be a rational function or a power series.

If a vector b is supplied, the numerators are equal to the coefficients of b, instead of all equal to 1 as above; more precisely, x ≈ (1/b_{0})(a_{0} +b_{1}/(a_{1} +…+b_{n}/a_{n})); for a numerical continued fraction (x real), the a_{i} are integers, as large as possible; if x is a rational function, they are polynomials with deg a_{i} = deg b_{i} + 1. The length of the result is then equal to the length of b, unless the next partial quotient cannot be reliably computed, in which case the expansion stops. This happens when a partial remainder is equal to zero (or too small compared to the available significant digits for x a t_REAL).

A direct implementation of the numerical continued fraction contfrac(x,b) described above would be

    \\ "greedy" generalized continued fraction
    cf(x, b) =
    { my( a= vector(#b), t );
      x *= b[1];
      for (i = 1, #b,
        a[i] = floor(x);
        t = x - a[i]; if (!t || i == #b, break);

    x = b[i+1] / t;
  ); a;
}

There is some degree of freedom when choosing the a_{i}; the program above can easily be modified to derive variants of the standard algorithm. In the same vein, although no builtin function implements the related Engel expansion (a special kind of Egyptian fraction decomposition: x = 1/a_{1} + 1/(a_{1}a_{2}) + …), it can be obtained as follows:

\\ n terms of the Engel expansion of x
engel(x, n = 10) =
{ my( u = x, a = vector(n) );
  for (k = 1, n,
    a[k] = ceil(1/u);
    u = u*a[k] - 1;
    if (!u, break);
  ); a
}

Obsolete hack. (don’t use this): If b is an integer, nmax is ignored and the command is understood

as contfrac(x, , b).
The library syntax is GEN contfrac0(GEN x, GEN b = NULL, long nmax). Also available
are GEN gboundcf(GEN x, long nmax), GEN gcf(GEN x) and GEN gcf2(GEN b, GEN x).

3.4.12 contfracpnqn(x, {n = −1}). When x is a vector or a one-row matrix, x is considered as the list of partial quotients [a_{0}, a_{1}, …, a_{n}] of a rational number, and the result is the 2 by 2 matrix [p_{n}, p_{n−1}; q_{n}, q_{n−1}] in the standard notation of continued fractions, so p_{n}/q_{n} = a_{0} + 1/(a_{1} + … + 1/a_{n}). If x is a matrix with two rows [b_{0}, b_{1}, …, b_{n}] and [a_{0}, a_{1}, …, a_{n}], this is then considered as a generalized continued fraction and we have similarly p_{n}/q_{n} = (1/b_{0})(a_{0} + b_{1}/(a_{1} + … + b_{n}/a_{n})). Note that in this case one usually has b_{0} = 1.

If n ≥ 0 is present, returns all convergents from p_{0}/q_{0} up to p_{n}/q_{n}. (All convergents if x is too small to compute the n + 1 requested convergents.)

? a=contfrac(Pi,20)
%1 = [3, 7, 15, 1, 292, 1, 1, 1, 2, 1, 3, 1, 14, 2, 1, 1, 2, 2, 2, 2]
? contfracpnqn(a,3)
%2 =
[3 22 333 355]
[1  7 106 113]
? contfracpnqn(a,7)
%3 =
[3 22 333 355 103993 104348 208341 312689]
[1  7 106 113  33102  33215  66317  99532]
The library syntax is GEN contfracpnqn(GEN x, long n). also available is GEN pnqn(GEN

x) for n = −1.

3.4.13 core(n, {flag = 0}). If n is an integer written as n = df^{2} with d squarefree, returns d. If flag is non-zero, returns the two-element row vector [d, f]. By convention, we write 0 = 0 × 1^{2}, so core(0, 1) returns [0, 1].

    The library syntax is GEN core0(GEN n, long flag). Also available are GEN core(GEN n)

(flag = 0) and GEN core2(GEN n) (flag = 1)

3.4.14 coredisc(n, {flag = 0}). A fundamental discriminant is an integer of the form t ≡ 1 mod 4 or 4t ≡ 8, 12 mod 16, with t squarefree (i.e. 1 or the discriminant of a quadratic number field). Given a non-zero integer n, this routine returns the (unique) fundamental discriminant d such that n = df^{2}, f a positive rational number. If flag is non-zero, returns the two-element row vector [d, f]. If n is congruent to 0 or 1 modulo 4, f is an integer, and a half-integer otherwise.

By convention, coredisc(0, 1)) returns [0, 1].

Note that quaddisc(n) returns the same value as coredisc(n), and also works with rational inputs n ∈ Q^{∗}.

    The library syntax is GEN coredisc0(GEN n, long flag). Also available are GEN core-
disc(GEN n) (flag = 0) and GEN coredisc2(GEN n) (flag = 1)

3.4.15 dirdiv(x, y). x and y being vectors of perhaps different lengths but with y[1] ≠ 0 considered as Dirichlet series, computes the quotient of x by y, again as a vector.

    The library syntax is GEN dirdiv(GEN x, GEN y).

3.4.16 direuler(p = a, b, expr, {c}). Computes the Dirichlet series associated to the Euler product of expression expr as p ranges through the primes from a to b. expr must be a polynomial or rational function in another variable than p (say X) and expr(X) is understood as the local factor expr(p^{−s}).

The series is output as a vector of coefficients. If c is present, output only the first c coefficients in the series. The following command computes the sigma function, associated to ζ(s)ζ(s − 1):

    ? direuler(p=2, 10, 1/((1-X)*(1-p*X)))
    %1 = [1, 3, 4, 7, 6, 12, 8, 15, 13, 18]
    The library syntax is direuler(void *E, GEN (*eval)(void*,GEN), GEN a, GEN b)

3.4.17 dirmul(x, y). x and y being vectors of perhaps different lengths representing the Dirichlet series ∑_{n} x_{n}n^{−s} and ∑_{n} y_{n}n^{−s}, computes the product of x by y, again as a vector.

    ? dirmul(vector(10,n,1), vector(10,n,moebius(n)))
    %1 = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0]

The product length is the minimum of #x∗v(y) and #y∗v(x), where v(x) is the index of the first non-zero coefficient.

    ? dirmul([0,1], [0,1]);
    %2 = [0, 0, 0, 1]
    The library syntax is GEN dirmul(GEN x, GEN y).

3.4.18 divisors(x). Creates a row vector whose components are the divisors of x. The factorization of x (as output by factor) can be used instead.

By definition, these divisors are the products of the irreducible factors of n, as produced by factor(n), raised to appropriate powers (no negative exponent may occur in the factorization). If n is an integer, they are the positive divisors, in increasing order.

The library syntax is GEN divisors(GEN x).

3.4.19 eulerphi(x). Euler’s ϕ (totient) function of the integer |x|, in other words |(Z/xZ)^{∗}|.

    ? eulerphi(40)
    %1 = 16

According to this definition we let ϕ(0) := 2, since Z^{∗} = {−1, 1}; this is consistent with znstar(0):

we have znstar(n).no = eulerphi(n) for all n ∈ Z.

The library syntax is GEN eulerphi(GEN x).

3.4.20 factor(x, {lim}). General factorization function, where x is a rational (including integers), a complex number with rational real and imaginary parts, or a rational function (including polynomials). The result is a two-column matrix: the first contains the irreducibles dividing x (rational or Gaussian primes, irreducible polynomials), and the second the exponents. By convention, 0 is factored as 0^{1}.

Q and Q(i). See factorint for more information about the algorithms used. The rational or Gaussian primes are in fact pseudoprimes (see ispseudoprime), a priori not rigorously proven primes. In fact, any factor which is ≤ 2^{64} (whose norm is ≤ 2^{64} for an irrational Gaussian prime) is a genuine prime. Use isprime to prove primality of other factors, as in

    ? fa = factor(2^2^7 + 1)
    %1 =
    [59649589127497217 1]
    [5704689200685129054721 1]
    ? isprime( fa[,1] )
    %2 = [1, 1]~   \\ both entries are proven primes

Another possibility is to set the global default factor_proven, which will perform a rigorous primality proof for each pseudoprime factor.

A t_INT argument lim can be added, meaning that we look only for prime factors p < lim. The limit lim must be non-negative. In this case, all but the last factor are proven primes, but the remaining factor may actually be a proven composite! If the remaining factor is less than lim^{2}, then it is prime.

    ? factor(2^2^7 +1, 10^5)
    %3 =
    [340282366920938463463374607431768211457 1]

Deprecated feature. Setting lim = 0 is the same as setting it to primelimit + 1. Don’t use this: it is unwise to rely on global variables when you can specify an explicit argument.

This routine uses trial division and perfect power tests, and should not be used for huge values of lim (at most 10^{9}, say): factorint(, 1 + 8) will in general be faster. The latter does not guarantee that all small prime factors are found, but it also finds larger factors, and in a much more efficient way.

? F = (2^2^7 + 1) * 1009 * 100003; factor(F, 10^5)  \\ fast, incomplete
time = 0 ms.
%4 =
[1009 1]
[34029257539194609161727850866999116450334371 1]
? factor(F, 10^9)    \\ very slow
time = 6,892 ms.
%6 =
[1009 1]
[100003 1]
[340282366920938463463374607431768211457 1]
? factorint(F, 1+8)  \\ much faster, all small primes were found
time = 12 ms.
%7 =
[1009 1]
[100003 1]
[340282366920938463463374607431768211457 1]
? factor(F)   \\ complete factorisation
time = 112 ms.
%8 =
[1009 1]
[100003 1]
[59649589127497217 1]
[5704689200685129054721 1]

Over Q, the prime factors are sorted in increasing order.

Rational functions. The polynomials or rational functions to be factored must have scalar coefficients. In particular PARI does not know how to factor multivariate polynomials. The following domains are currently supported: Q, R, C, Q_{p}, finite fields and number fields. See factormod and factorff for the algorithms used over finite fields, factornf for the algorithms over number fields. Over Q, van Hoeij’s method is used, which is able to cope with hundreds of modular factors.

The routine guesses a sensible ring over which to factor: the smallest ring containing all coefficients, taking into account quotient structures induced by t_INTMODs and t_POLMODs (e.g. if a coefficient in Z/nZ is known, all rational numbers encountered are first mapped to Z/nZ; different moduli will produce an error). Factoring modulo a non-prime number is not supported; to factor in Q_{p}, use t_PADIC coefficients not t_INTMOD modulo p^{n}.

? T = x^2+1;
? factor(T);                         \\ over Q
? factor(T*Mod(1,3))                 \\ over F_3
? factor(T*ffgen(ffinit(3,2,'t))^0)  \\ over F_{3^2}
? factor(T*Mod(Mod(1,3), t^2+t+2))   \\ over F_{3^2}, again
? factor(T*(1 + O(3^6))              \\ over Q_3, precision 6
? factor(T*1.)                       \\ over R, current precision
? factor(T*(1.+0.*I))                \\ over C
? factor(T*Mod(1, y^3-2))            \\ over Q(2^{1/3})

In most cases, it is clearer and simpler to call an explicit variant than to rely on the generic factor function and the above detection mechanism:

? factormod(T, 3)           \\ over F_3
? factorff(T, 3, t^2+t+2))  \\ over F_{3^2}
? factorpadic(T, 3,6)       \\ over Q_3, precision 6
? nffactor(y^3-2, T)        \\ over Q(2^{1/3})
? polroots(T)               \\ over C

Note that factorization of polynomials is done up to multiplication by a constant. In particular, the factors of rational polynomials will have integer coefficients, and the content of a polynomial or rational function is discarded and not included in the factorization. If needed, you can always ask for the content explicitly:

? factor(t^2 + 5/2*t + 1)
%1 =
[2*t + 1 1]
[t + 2 1]
? content(t^2 + 5/2*t + 1)
%2 = 1/2

The irreducible factors are sorted by increasing degree. See also nffactor.

The library syntax is GEN gp_factor0(GEN x, GEN lim = NULL). This function should only

be used by the gp interface. Use directly GEN factor(GEN x) or GEN boundfact(GEN x, ulong

lim). The obsolete function GEN factor0(GEN x, long lim) is kept for backward compatibility.

3.4.21 factorback(f, {e}). Gives back the factored object corresponding to a factorization. The integer 1 corresponds to the empty factorization.

If e is present, e and f must be vectors of the same length (e being integral), and the corresponding factorization is the product of the f[i]^{e[i]}.

If not, and f is vector, it is understood as in the preceding case with e a vector of 1s: we return the product of the f[i]. Finally, f can be a regular factorization, as produced with any factor command. A few examples:

? factor(12)
%1 =
[2 2]
[3 1]
? factorback(%)
%2 = 12
? factorback([2,3], [2,1])   \\ 2^3 * 3^1
%3 = 12
? factorback([5,2,3])
%4 = 30
The library syntax is GEN factorback2(GEN f, GEN e = NULL). Also available is GEN fac-
torback(GEN f) (case e = NULL).

3.4.22 factorcantor(x, p). Factors the polynomial x modulo the prime p, using distinct degree plus Cantor-Zassenhaus. The coefficients of x must be operation-compatible with Z/pZ. The result is a two-column matrix, the first column being the irreducible polynomials dividing x, and the second the exponents. If you want only the degrees of the irreducible polynomials (for example for computing an L-function), use factormod(x, p, 1). Note that the factormod algorithm is usually

faster than factorcantor.
The library syntax is GEN factcantor(GEN x, GEN p).

3.4.23 factorff(x, {p}, {a}). Factors the polynomial x in the field F_{q} defined by the irreducible polynomial a over F_{p}. The coefficients of x must be operation-compatible with Z/pZ. The result is a two-column matrix: the first column contains the irreducible factors of x, and the second their exponents. If all the coefficients of x are in F_{p}, a much faster algorithm is applied, using the computation of isomorphisms between finite fields.

Either a or p can omitted (in which case both are ignored) if x has t_FFELT coefficients; the function then becomes identical to factor:

? factorff(x^2 + 1, 5, y^2+3)  \\ over F_5[y]/(y^2+3) ~ F_25
%1 =
[Mod(Mod(1, 5), Mod(1, 5)*y^2 + Mod(3, 5))*x
 + Mod(Mod(2, 5), Mod(1, 5)*y^2 + Mod(3, 5)) 1]
[Mod(Mod(1, 5), Mod(1, 5)*y^2 + Mod(3, 5))*x
 + Mod(Mod(3, 5), Mod(1, 5)*y^2 + Mod(3, 5)) 1]
? t = ffgen(y^2 + Mod(3,5), 't); \\ a generator for F_25 as a t_FFELT
? factorff(x^2 + 1)   \\ not enough information to determine the base field
 ***   at top-level: factorff(x^2+1)

     ***                 ^---------------
     *** factorff: incorrect type in factorff.
    ? factorff(x^2 + t^0) \\ make sure a coeff. is a t_FFELT
    %3 =
    [x + 2 1]
    [x + 3 1]
    ? factorff(x^2 + t + 1)
    %11 =
    [x + (2*t + 1) 1]
    [x + (3*t + 4) 1]

Notice that the second syntax is easier to use and much more readable.

    The library syntax is GEN factorff(GEN x, GEN p = NULL, GEN a = NULL).

3.4.24 factorial(x). Factorial of x. The expression x! gives a result which is an integer, while factorial(x) gives a real number.

    The library syntax is GEN mpfactr(long x, long prec). GEN mpfact(long x) returns x! as
a t_INT.

3.4.25 factorint(x, {flag = 0}). Factors the integer n into a product of pseudoprimes (see ispseudoprime), using a combination of the Shanks SQUFOF and Pollard Rho method (with modifications due to Brent), Lenstra’s ECM (with modifications by Montgomery), and MPQS (the latter adapted from the LiDIA code with the kind permission of the LiDIA maintainers), as well as a search for pure powers. The output is a two-column matrix as for factor: the first column contains the “prime” divisors of n, the second one contains the (positive) exponents.

By convention 0 is factored as 0^{1}, and 1 as the empty factorization; also the divisors are by default not proven primes is they are larger than 2^{64}, they only failed the BPSW compositeness test (see ispseudoprime). Use isprime on the result if you want to guarantee primality or set the factor_proven default to 1. Entries of the private prime tables (see addprimes) are also included as is.

This gives direct access to the integer factoring engine called by most arithmetical functions. flag is optional; its binary digits mean 1: avoid MPQS, 2: skip first stage ECM (we may still fall back to it later), 4: avoid Rho and SQUFOF, 8: don’t run final ECM (as a result, a huge composite may be declared to be prime). Note that a (strong) probabilistic primality test is used; thus composites might not be detected, although no example is known.

You are invited to play with the flag settings and watch the internals at work by using gp’s debug default parameter (level 3 shows just the outline, 4 turns on time keeping, 5 and above show an increasing amount of internal details).

    The library syntax is GEN factorint(GEN x, long flag).

3.4.26 factormod(x, p, {flag = 0}). Factors the polynomial x modulo the prime integer p, using Berlekamp. The coefficients of x must be operation-compatible with Z/pZ. The result is a twocolumn matrix, the first column being the irreducible polynomials dividing x, and the second the exponents. If flag is non-zero, outputs only the degrees of the irreducible polynomials (for example, for computing an L-function). A different algorithm for computing the mod p factorization is factorcantor which is sometimes faster.

    The library syntax is GEN factormod0(GEN x, GEN p, long flag).

3.4.27 ffgen(q, {v}). Return a t_FFELT generator for the finite field with q elements; q = p^{f} must be a prime power. This functions computes an irreducible monic polynomial P ∈ F_{p}[X] of degree f (via ffinit) and returns g = X (mod P (X)). If v is given, the variable name is used to display g, else the variable x is used.

    ? g = ffgen(8, 't);
    ? g.mod
    %2 = t^3 + t^2 + 1
    ? g.p
    %3 = 2
    ? g.f
    %4 = 3
    ? ffgen(6)
     ***   at top-level: ffgen(6)
     ***                 ^--------
     *** ffgen: not a prime number in ffgen: 6.

Alternative syntax: instead of a prime power q, one may input directly the polynomial P (monic, irreducible, with t_INTMOD coefficients), and the function returns the generator g = X (mod P (X)), inferring p from the coefficients of P. If v is given, the variable name is used to display g, else the variable of the polynomial P is used. If P is not irreducible, we create an invalid object and behaviour of functions dealing with the resulting t_FFELT is undefined; in fact, it is much more costly to test P for irreducibility than it would be to produce it via ffinit.

The library syntax is GEN ffgen(GEN q, long v = -1), where v is a variable number.

To create a generator for a prime finite field, the function GEN p_to_GEN(GEN p, long v)

returns 1+ffgen(x*Mod(1,p),v).

3.4.28 ffinit(p, n, {v =′ x}). Computes a monic polynomial of degree n which is irreducible over F_{p}, where p is assumed to be prime. This function uses a fast variant of Adleman and Lenstra’s algorithm.

It is useful in conjunction with ffgen; for instance if P = ffinit(3,2), you can represent elements in F_{3^{2}} in term of g = ffgen(P,'t). This can be abbreviated as g = ffgen(3^2, 't), where the defining polynomial P can be later recovered as g.mod.

The library syntax is GEN ffinit(GEN p, long n, long v = -1), where v is a variable number.

3.4.29 fflog(x, g, {o}). Discrete logarithm of the finite field element x in base g, i.e. an e in Z such that g^{e} = o. If present, o represents the multiplicative order of g, see Section 3.4.2; the preferred format for this parameter is [ord, factor(ord)], where ord is the order of g. It may be set as a side effect of calling ffprimroot.

If no o is given, assume that g is a primitive root. The result is undefined if e does not exist. This function uses

• a combination of generic discrete log algorithms (see znlog)

• a cubic sieve index calculus algorithm for large fields of degree at least 5.

• Coppersmith’s algorithm for fields of characteristic at most 5.

    ? t = ffgen(ffinit(7,5));

? o = fforder(t)

%2 = 5602 \\ not a primitive root.

? fflog(t^10,t)
%3 = 10
? fflog(t^10,t, o)
%4 = 10
? g = ffprimroot(t, &o);
? o   \\ order is 16806, bundled with its factorization matrix
%6 = [16806, [2, 1; 3, 1; 2801, 1]]
? fforder(g, o)
%7 = 16806
? fflog(g^10000, g, o)
%8 = 10000
The library syntax is GEN fflog(GEN x, GEN g, GEN o = NULL).

3.4.30 ffnbirred(q, n{, fl = 0}). Computes the number of monic irreducible polynomials over F_{q} of degree exactly n, (flag = 0 or omitted) or at most n (flag = 1).

The library syntax is GEN ffnbirred0(GEN q, long n, long fl). Also available are GEN
ffnbirred(GEN q, long n) (for flag = 0) and GEN ffsumnbirred(GEN q, long n) (for flag =

1).

3.4.31 fforder(x, {o}). Multiplicative order of the finite field element x. If o is present, it represents a multiple of the order of the element, see Section 3.4.2; the preferred format for this parameter is [N, factor(N)], where N is the cardinality of the multiplicative group of the underlying finite field.

? t = ffgen(ffinit(nextprime(10^8), 5));
? g = ffprimroot(t, &o);  \\ o will be useful!
? fforder(g^1000000, o)
time = 0 ms.
%5 = 5000001750000245000017150000600250008403
? fforder(g^1000000)

time = 16 ms. \\ noticeably slower, same result of course

%6 = 5000001750000245000017150000600250008403
The library syntax is GEN fforder(GEN x, GEN o = NULL).

3.4.32 ffprimroot(x, {&o}). Return a primitive root of the multiplicative group of the definition field of the finite field element x (not necessarily the same as the field generated by x). If present, o is set to a vector [ord, fa], where ord is the order of the group and fa its factorisation factor(ord). This last parameter is useful in fflog and fforder, see Section 3.4.2.

? t = ffgen(ffinit(nextprime(10^7), 5));
? g = ffprimroot(t, &o);
? o[1]
%3 = 100000950003610006859006516052476098
? o[2]
%4 =
[2 1]

[7 2]
[31 1]
[41 1]
[67 1]
[1523 1]
[10498781 1]
[15992881 1]
[46858913131 1]
? fflog(g^1000000, g, o)
time = 1,312 ms.
%5 = 1000000
The library syntax is GEN ffprimroot(GEN x, GEN *o = NULL).

3.4.33 fibonacci(x). x^{th} Fibonacci number.

The library syntax is GEN fibo(long x).

3.4.34 gcd(x, {y}). Creates the greatest common divisor of x and y. If you also need the u and v such that x ∗ u + y ∗ v = gcd(x, y), use the bezout function. x and y can have rather quite general types, for instance both rational numbers. If y is omitted and x is a vector, returns the gcd of all components of x, i.e. this is equivalent to content(x).

When x and y are both given and one of them is a vector/matrix type, the GCD is again taken recursively on each component, but in a different way. If y is a vector, resp. matrix, then the result has the same type as y, and components equal to gcd(x, y[i]), resp. gcd(x, y[,i]). Else if x is a vector/matrix the result has the same type as x and an analogous definition. Note that for these types, gcd is not commutative.

The algorithm used is a naive Euclid except for the following inputs:

• integers: use modified right-shift binary (“plus-minus” variant).

• univariate polynomials with coefficients in the same number field (in particular rational): use modular gcd algorithm.

• general polynomials: use the subresultant algorithm if coefficient explosion is likely (non modular coefficients).

If u and v are polynomials in the same variable with inexact coefficients, their gcd is defined to be scalar, so that

? a = x + 0.0; gcd(a,a)
%1 = 1
? b = y*x + O(y); gcd(b,b)
%2 = y
? c = 4*x + O(2^3); gcd(c,c)
%3 = 4

A good quantitative check to decide whether such a gcd “should be” non-trivial, is to use polresultant: a value close to 0 means that a small deformation of the inputs has non-trivial gcd. You may also use gcdext, which does try to compute an approximate gcd d and provides u, v to check whether ux + vy is close to d.

The library syntax is GEN ggcd0(GEN x, GEN y = NULL). Also available are GEN ggcd(GEN
x, GEN y), if y is not NULL, and GEN content(GEN x), if y = NULL.

3.4.35 gcdext(x, y). Returns [u, v, d] such that d is the gcd of x, y, x ∗ u + y ∗ v = gcd(x, y), and u and v minimal in a natural sense. The arguments must be integers or polynomials.

? [u, v, d] = gcdext(32,102)
%1 = [16, -5, 2]
? d
%2 = 2
? gcdext(x^2-x, x^2+x-2)
%3 = [-1/2, 1/2, x - 1]

If x, y are polynomials in the same variable and inexact coefficients, then compute u, v, d such that x ∗ u + y ∗ v = d, where d approximately divides both and x and y; in particular, we do not obtain gcd(x,y) which is defined to be a scalar in this case:

? a = x + 0.0; gcd(a,a)
%1 = 1
? gcdext(a,a)
%2 = [0, 1, x + 0.E-28]
? gcdext(x-Pi, 6*x^2-zeta(2))
%3 = [-6*x - 18.8495559, 1, 57.5726923]

For inexact inputs, the output is thus not well defined mathematically, but you obtain explicit polynomials to check whether the approximation is close enough for your needs.

The library syntax is GEN gcdext0(GEN x, GEN y).

3.4.36 hilbert(x, y, {p}). Hilbert symbol of x and y modulo the prime p, p = 0 meaning the place at infinity (the result is undefined if p ≠ 0 is not prime).

It is possible to omit p, in which case we take p = 0 if both x and y are rational, or one of them is a real number. And take p = q if one of x, y is a t_INTMOD modulo q or a q-adic. (Incompatible types will raise an error.)

The library syntax is long hilbert(GEN x, GEN y, GEN p = NULL).

3.4.37 isfundamental(x). True (1) if x is equal to 1 or to the discriminant of a quadratic field, false (0) otherwise.

The library syntax is long isfundamental(GEN x).

3.4.38 ispolygonal(x, s, {&N}). True (1) if the integer x is an s-gonal number, false (0) if not. The parameter s > 2 must be a t_INT. If N is given, set it to n if x is the n-th s-gonal number.

? ispolygonal(36, 3, &N)
%1 = 1
? N
The library syntax is long ispolygonal(GEN x, GEN s, GEN *N = NULL).

3.4.39 ispower(x, {k}, {&n}). If k is given, returns true (1) if x is a k-th power, false (0) if not.

If k is omitted, only integers and fractions are allowed for x and the function returns the maximal k ≥ 2 such that x = n^{k} is a perfect power, or 0 if no such k exist; in particular ispower(-

1), ispower(0), and ispower(1) all return 0.

If a third argument &n is given and x is indeed a k-th power, sets n to a k-th root of x.

For a t_FFELT x, instead of omitting k (which is not allowed for this type), it may be natural to set

    k = (x.p ^poldegree(x.pol) - 1) / fforder(x)
    The library syntax is long ispower(GEN x, GEN k = NULL, GEN *n = NULL). Also available
is long gisanypower(GEN x, GEN *pty) (k omitted).

3.4.40 ispowerful(x). True (1) if x is a powerful integer, false (0) if not; an integer is powerful if and only if its valuation at all primes is greater than 1.

    ? ispowerful(50)
    %1 = 0
    ? ispowerful(100)
    %2 = 1
    ? ispowerful(5^3*(10^1000+1)^2)
    %3 = 1
    The library syntax is long ispowerful(GEN x).

3.4.41 isprime(x, {flag = 0}). True (1) if x is a prime number, false (0) otherwise. A prime number is a positive integer having exactly two distinct divisors among the natural numbers, namely 1 and itself.

This routine proves or disproves rigorously that a number is prime, which can be very slow when x is indeed prime and has more than 1000 digits, say. Use ispseudoprime to quickly check for compositeness. See also factor. It accepts vector/matrices arguments, and is then applied componentwise.

If flag = 0, use a combination of Baillie-PSW pseudo primality test (see ispseudoprime), Selfridge “p − 1” test if x − 1 is smooth enough, and Adleman-Pomerance-Rumely-Cohen-Lenstra (APRCL) for general x.

If flag = 1, use Selfridge-Pocklington-Lehmer “p − 1” test and output a primality certificate as follows: return

• 0 if x is composite,

• 1 if x is small enough that passing Baillie-PSW test guarantees its primality (currently x < 2^{64}, as checked by Jan Feitsma),

• 2 if x is a large prime whose primality could only sensibly be proven (given the algorithms implemented in PARI) using the APRCL test.

• Otherwise (x is large and x − 1 is smooth) output a three column matrix as a primality certificate. The first column contains prime divisors p of x − 1 (such that ∏ p^{v_{p}(x−1)} > x^{1/3}), the second the corresponding elements a_{p} as in Proposition 8.3.1 in GTM 138 , and the third the output of isprime(p,1).

The algorithm fails if one of the pseudo-prime factors is not prime, which is exceedingly unlikely and well worth a bug report. Note that if you monitor isprime at a high enough debug level, you may see warnings about untested integers being declared primes. This is normal: we ask for partial factorisations (sufficient to prove primality if the unfactored part is not too large), and factor warns us that the cofactor hasn’t been tested. It may or may not be tested later, and may or may not be prime. This does not affect the validity of the whole isprime procedure.

If flag = 2, use APRCL.

    The library syntax is GEN gisprime(GEN x, long flag).

3.4.42 isprimepower(x, {&n}). If x = p^{k} is a prime power (p prime, k > 0), return k, else return 0. If a second argument &n is given and x is indeed the k-th power of a prime p, sets n to p.

    The library syntax is long isprimepower(GEN x, GEN *n = NULL).

3.4.43 ispseudoprime(x, {flag}). True (1) if x is a strong pseudo prime (see below), false (0) otherwise. If this function returns false, x is not prime; if, on the other hand it returns true, it is only highly likely that x is a prime number. Use isprime (which is of course much slower) to prove that x is indeed prime. The function accepts vector/matrices arguments, and is then applied componentwise.

If flag = 0, checks whether x is a Baillie-Pomerance-Selfridge-Wagstaff pseudo prime (strong Rabin-Miller pseudo prime for base 2, followed by strong Lucas test for the sequence (P, −1), P smallest positive integer such that P^{2} − 4 is not a square mod x).

There are no known composite numbers passing this test, although it is expected that infinitely many such numbers exist. In particular, all composites ≤ 2^{64} are correctly detected (checked using

http://www.cecm.sfu.ca/Pseudoprimes/index-2-to-64.html).

If flag > 0, checks whether x is a strong Miller-Rabin pseudo prime for flag randomly chosen bases (with end-matching to catch square roots of −1).

    The library syntax is GEN gispseudoprime(GEN x, long flag).

3.4.44 issquare(x, {&n}). True (1) if x is a square, false (0) if not. What “being a square” means depends on the type of x: all t_COMPLEX are squares, as well as all non-negative t_REAL; for exact types such as t_INT, t_FRAC and t_INTMOD, squares are numbers of the form s^{2} with s in Z, Q and Z/NZ respectively.

    ? issquare(3)          \\ as an integer
    %1 = 0
    ? issquare(3.)         \\ as a real number
    %2 = 1
    ? issquare(Mod(7, 8))  \\ in Z/8Z
    %3 = 0
    ? issquare( 5 + O(13^4) )  \\ in Q_13
    %4 = 0

If n is given, a square root of x is put into n.

    ? issquare(4, &n)
    %1 = 1
    ? n

%2 = 2

For polynomials, either we detect that the characteristic is 2 (and check directly odd and even-power monomials) or we assume that 2 is invertible and check whether squaring the truncated power series for the square root yields the original input.

The library syntax is long issquareall(GEN x, GEN *n = NULL). Also available is long is-
square(GEN x). Deprecated GP-specific functions GEN gissquare(GEN x) and GEN gissquare-

all(GEN x, GEN *pt) return gen 0 and gen 1 instead of a boolean value.

3.4.45 issquarefree(x). True (1) if x is squarefree, false (0) if not. Here x can be an integer or a polynomial.

The library syntax is long issquarefree(GEN x).

3.4.46 istotient(x, {&N}). True (1) if x = ϕ(n) for some integer n, false (0) if not.

? istotient(14)
%1 = 0
? istotient(100)
%2 = 0

If N is given, set N = n as well.

? istotient(4, &n)
%1 = 1
? n
%2 = 10
The library syntax is long istotient(GEN x, GEN *N = NULL).

3.4.47 kronecker(x, y). Kronecker symbol (x|y), where x and y must be of type integer. By definition, this is the extension of Legendre symbol to Z × Z by total multiplicativity in both arguments with the following special rules for y = 0, −1 or 2:

• (x|0) = 1 if |x| = 1 and 0 otherwise.

• (x| − 1) = 1 if x ≥ 0 and −1 otherwise.

• (x|2) = 0 if x is even and 1 if x = 1, −1 mod 8 and −1 if x = 3, −3 mod 8.

The library syntax is long kronecker(GEN x, GEN y).

3.4.48 lcm(x, {y}). Least common multiple of x and y, i.e. such that lcm(x, y) ∗ gcd(x, y) = abs(x ∗ y). If y is omitted and x is a vector, returns the lcm of all components of x.

When x and y are both given and one of them is a vector/matrix type, the LCM is again taken recursively on each component, but in a different way. If y is a vector, resp. matrix, then the result has the same type as y, and components equal to lcm(x, y[i]), resp. lcm(x, y[,i]). Else if x is a vector/matrix the result has the same type as x and an analogous definition. Note that for these types, lcm is not commutative.

Note that lcm(v) is quite different from

l = v[1]; for (i = 1, #v, l = lcm(l, v[i]))

Indeed, lcm(v) is a scalar, but l may not be (if one of the v[i] is a vector/matrix). The computation uses a divide-conquer tree and should be much more efficient, especially when using the GMP multiprecision kernel (and more subquadratic algorithms become available):

? v = vector(10^4, i, random);
? lcm(v);
time = 323 ms.
? l = v[1]; for (i = 1, #v, l = lcm(l, v[i]))
time = 833 ms.
The library syntax is GEN glcm0(GEN x, GEN y = NULL).

3.4.49 logint(x, b, &z). Return the largest integer e so that b^{e} ≤ x, where the parameters b > 1 and x > 0 are both integers. If the parameter z is present, set it to b^{e}.

? logint(1000, 2)
%1 = 9
? 2^9
%2 = 512
? logint(1000, 2, &z)
%3 = 9
? z
%4 = 512

The number of digits used to write b in base x is 1 + logint(x,b):

? #digits(1000!, 10)
%5 = 2568
? logint(1000!, 10)
%6 = 2567

This function may conveniently replace

  floor( log(x) / log(b) )

which may not give the correct answer since PARI does not guarantee exact rounding.

The library syntax is long logint0(GEN x, GEN b, GEN *z = NULL).

3.4.50 moebius(x). Moebius μ-function of |x|. x must be of type integer.

The library syntax is long moebius(GEN x).

3.4.51 nextprime(x). Finds the smallest pseudoprime (see ispseudoprime) greater than or equal to x. x can be of any real type. Note that if x is a pseudoprime, this function returns x and not the smallest pseudoprime strictly larger than x. To rigorously prove that the result is prime, use

isprime.

The library syntax is GEN nextprime(GEN x).

3.4.52 numbpart(n). Gives the number of unrestricted partitions of n, usually called p(n) in the literature; in other words the number of nonnegative integer solutions to a + 2b + 3c + ⋯ = n. n must be of type integer and n < 10^{15} (with trivial values p(n) = 0 for n < 0 and p(0) = 1). The algorithm uses the Hardy-Ramanujan-Rademacher formula. To explicitly enumerate them, see

partitions.

The library syntax is GEN numbpart(GEN n).

3.4.53 numdiv(x). Number of divisors of |x|. x must be of type integer.

The library syntax is GEN numdiv(GEN x).

3.4.54 omega(x). Number of distinct prime divisors of |x|. x must be of type integer.

    ? factor(392)
    %1 =
    [2 3]
    [7 2]
    ? omega(392)
    %2 = 2;  \\ without multiplicity
    ? bigomega(392)
    %3 = 5;  \\ = 3+2, with multiplicity

The library syntax is long omega(GEN x).

3.4.55 partitions(k, {a = k}, {n = k})). Returns the vector of partitions of the integer k as a sum of positive integers (parts); for k < 0, it returns the empty set [], and for k = 0 the trivial partition (no parts). A partition is given by a t_VECSMALL, where parts are sorted in nondecreasing order:

    ? partitions(3)
    %1 = [Vecsmall([3]), Vecsmall([1, 2]), Vecsmall([1, 1, 1])]

correspond to 3, 1 + 2 and 1 + 1 + 1. The number of (unrestricted) partitions of k is given by

numbpart:
    ? #partitions(50)
    %1 = 204226
    ? numbpart(50)
    %2 = 204226

Optional parameters n and a are as follows:

• n = nmax (resp. n = [nmin, nmax]) restricts partitions to length less than nmax (resp. length between nmin and nmax), where the length is the number of nonzero entries.

• a = amax (resp. a = [amin, amax]) restricts the parts to integers less than amax (resp. between amin and amax).

? partitions(4, 2)  \\ parts bounded by 2
%1 = [Vecsmall([2, 2]), Vecsmall([1, 1, 2]), Vecsmall([1, 1, 1, 1])]
? partitions(4,, 2) \\ at most 2 parts
%2 = [Vecsmall([4]), Vecsmall([1, 3]), Vecsmall([2, 2])]
? partitions(4,[0,3], 2) \\ at most 2 parts
%3 = [Vecsmall([4]), Vecsmall([1, 3]), Vecsmall([2, 2])]

By default, parts are positive and we remove zero entries unless amin ≤ 0, in which case nmin is ignored and X is of constant length nmax:

? partitions(4, [0,3])  \\ parts between 0 and 3
%1 = [Vecsmall([0, 0, 1, 3]), Vecsmall([0, 0, 2, 2]),\
      Vecsmall([0, 1, 1, 2]), Vecsmall([1, 1, 1, 1])]
The library syntax is GEN partitions(long k, GEN a = NULL, GEN n) = NULL).

3.4.56 polrootsff(x, {p}, {a}). Returns the vector of distinct roots of the polynomial x in the field F_{q} defined by the irreducible polynomial a over F_{p}. The coefficients of x must be operationcompatible with Z/pZ. Either a or p can omitted (in which case both are ignored) if x has t_FFELT coefficients:

? polrootsff(x^2 + 1, 5, y^2+3)  \\ over F_5[y]/(y^2+3) ~ F_25
%1 = [Mod(Mod(3, 5), Mod(1, 5)*y^2 + Mod(3, 5)),
      Mod(Mod(2, 5), Mod(1, 5)*y^2 + Mod(3, 5))]
? t = ffgen(y^2 + Mod(3,5), 't); \\ a generator for F_25 as a t_FFELT
? polrootsff(x^2 + 1)   \\ not enough information to determine the base field
 ***   at top-level: polrootsff(x^2+1)
 ***                 ^-----------------
 *** polrootsff: incorrect type in factorff.
? polrootsff(x^2 + t^0) \\ make sure one coeff. is a t_FFELT
%3 = [3, 2]
? polrootsff(x^2 + t + 1)
%4 = [2*t + 1, 3*t + 4]

Notice that the second syntax is easier to use and much more readable.

The library syntax is GEN polrootsff(GEN x, GEN p = NULL, GEN a = NULL).

3.4.57 precprime(x). Finds the largest pseudoprime (see ispseudoprime) less than or equal to x. x can be of any real type. Returns 0 if x ≤ 1. Note that if x is a prime, this function returns x and not the largest prime strictly smaller than x. To rigorously prove that the result is prime, use

isprime.

The library syntax is GEN precprime(GEN x).

3.4.58 prime(n). The n^{th} prime number

? prime(10^9)
%1 = 22801763489

Uses checkpointing and a naive O(n) algorithm.

The library syntax is GEN prime(long n).

3.4.59 primepi(x). The prime counting function. Returns the number of primes p, p ≤ x.

    ? primepi(10)
    %1 = 4;
    ? primes(5)
    %2 = [2, 3, 5, 7, 11]
    ? primepi(10^11)
    %3 = 4118054813

Uses checkpointing and a naive O(x) algorithm.

The library syntax is GEN primepi(GEN x).

3.4.60 primes(n). Creates a row vector whose components are the first n prime numbers. (Returns the empty vector for n ≤ 0.) A t_VEC n = [a, b] is also allowed, in which case the primes in [a, b] are returned

    ? primes(10)     \\ the first 10 primes
    %1 = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29]
    ? primes([0,29])  \\ the primes up to 29
    %2 = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29]
    ? primes([15,30])
    %3 = [17, 19, 23, 29]

The library syntax is GEN primes0(GEN n).

3.4.61 qfbclassno(D, {flag = 0}). Ordinary class number of the quadratic order of discriminant D. In the present version 2.7.2, a O(D^{1/2}) algorithm is used for D > 0 (using Euler product and the functional equation) so D should not be too large, say D < 10^{8}, for the time to be reasonable. On the other hand, for D < 0 one can reasonably compute qfbclassno(D) for |D| < 10^{25}, since the routine uses Shanks’s method which is in O(|D|^{1/4}). For larger values of |D|, see quadclassunit.

If flag = 1, compute the class number using Euler products and the functional equation. However, it is in O(|D|^{1/2}).

Important warning. For D < 0, this function may give incorrect results when the class group has many cyclic factors, because implementing Shanks’s method in full generality slows it down immensely. It is therefore strongly recommended to double-check results using either the version with flag = 1 or the function quadclassunit.

Warning. Contrary to what its name implies, this routine does not compute the number of classes of binary primitive forms of discriminant D, which is equal to the narrow class number. The two notions are the same when D < 0 or the fundamental unit ε has negative norm; when D > 0 and Nε > 0, the number of classes of forms is twice the ordinary class number. This is a problem which we cannot fix for backward compatibility reasons. Use the following routine if you are only interested in the number of classes of forms:

    QFBclassno(D) =
    qfbclassno(D) * if (D < 0 || norm(quadunit(D)) < 0, 1, 2)

Here are a few examples:

    ? qfbclassno(400000028)
    time = 3,140 ms.
    %1 = 1
    ? quadclassunit(400000028).no
    time = 20 ms. \\ much faster
    %2 = 1
    ? qfbclassno(-400000028)
    time = 0 ms.

%3 = 7253 \\ correct, and fast enough

    ? quadclassunit(-400000028).no
    time = 0 ms.
    %4 = 7253
See also qfbhclassno.

The library syntax is GEN qfbclassno0(GEN D, long flag). The following functions are also available:

GEN classno(GEN D) (flag = 0)
GEN classno2(GEN D) (flag = 1).

Finally

GEN hclassno(GEN D) computes the class number of an imaginary quadratic field by counting reduced forms, an O(|D|) algorithm.

3.4.62 qfbcompraw(x, y). composition of the binary quadratic forms x and y, without reduction of the result. This is useful e.g. to compute a generating element of an ideal. The result is undefined if x and y do not have the same discriminant.

    The library syntax is GEN qfbcompraw(GEN x, GEN y).

3.4.63 qfbhclassno(x). Hurwitz class number of x, where x is non-negative and congruent to 0 or 3 modulo 4. For x > 5·10^{5}, we assume the GRH, and use quadclassunit with default parameters.

The library syntax is GEN hclassno(GEN x).

3.4.64 qfbnucomp(x, y, L). composition of the primitive positive definite binary quadratic forms x and y (type t_QFI) using the NUCOMP and NUDUPL algorithms of Shanks, à la Atkin. L is any positive constant, but for optimal speed, one should take L = |D|^{1/4}, where D is the common discriminant of x and y. When x and y do not have the same discriminant, the result is undefined.

The current implementation is straightforward and in general slower than the generic routine (since the latter takes advantage of asymptotically fast operations and careful optimizations).

    The library syntax is GEN nucomp(GEN x, GEN y, GEN L). Also available is GEN nudupl(GEN

x, GEN L) when x = y.

3.4.65 qfbnupow(x, n). n-th power of the primitive positive definite binary quadratic form x using Shanks’s NUCOMP and NUDUPL algorithms (see qfbnucomp, in particular the final warning).

    The library syntax is GEN nupow(GEN x, GEN n).

3.4.66 qfbpowraw(x, n). n-th power of the binary quadratic form x, computed without doing any reduction (i.e. using qfbcompraw). Here n must be non-negative and n < 2^{31}.

    The library syntax is GEN qfbpowraw(GEN x, long n).

3.4.67 qfbprimeform(x, p). Prime binary quadratic form of discriminant x whose first coefficient is p, where |p| is a prime number. By abuse of notation, p = ±1 is also valid and returns the unit form. Returns an error if x is not a quadratic residue mod p, or if x < 0 and p < 0. (Negative definite t_QFI are not implemented.) In the case where x > 0, the “distance” component of the form is set equal to zero according to the current precision.

    The library syntax is GEN primeform(GEN x, GEN p, long prec).

3.4.68 qfbred(x, {flag = 0}, {d}, {isd}, {sd}). Reduces the binary quadratic form x (updating Shanks’s distance function if x is indefinite). The binary digits of flag are toggles meaning

1: perform a single reduction step

2: don’t update Shanks’s distance

The arguments d, isd, sd, if present, supply the values of the discriminant, ⌊\sqrt{d}⌋, and \sqrt{d} respectively (no checking is done of these facts). If d < 0 these values are useless, and all references to Shanks’s distance are irrelevant.

    The library syntax is GEN qfbred0(GEN x, long flag, GEN d = NULL, GEN isd = NULL,

GEN sd = NULL). Also available are

GEN redimag(GEN x) (for definite x),

and for indefinite forms:

GEN redreal(GEN x)
GEN rhoreal(GEN x) (= qfbred(x,1)),
GEN redrealnod(GEN x, GEN isd) (= qfbred(x,2,,isd)),
GEN rhorealnod(GEN x, GEN isd) (= qfbred(x,3,,isd)).

3.4.69 qfbsolve(Q, p). Solve the equation Q(x, y) = p over the integers, where Q is a binary quadratic form and p a prime number.

Return [x, y] as a two-components vector, or zero if there is no solution. Note that this function returns only one solution and not all the solutions.

Let D = discQ. The algorithm used runs in probabilistic polynomial time in p (through the computation of a square root of D modulo p); it is polynomial time in D if Q is imaginary, but exponential time if Q is real (through the computation of a full cycle of reduced forms). In the latter case, note that bnfisprincipal provides a solution in heuristic subexponential time in D assuming the GRH.

    The library syntax is GEN qfbsolve(GEN Q, GEN p).

3.4.70 quadclassunit(D, {flag = 0}, {tech = [ ]}). Buchmann-McCurley’s sub-exponential algorithm for computing the class group of a quadratic order of discriminant D.

This function should be used instead of qfbclassno or quadregula when D < −10^{25}, D > 10^{10}, or when the structure is wanted. It is a special case of bnfinit, which is slower, but more robust.

The result is a vector v whose components should be accessed using member functions:

• v.no: the class number

• v.cyc: a vector giving the structure of the class group as a product of cyclic groups;

• v.gen: a vector giving generators of those cyclic groups (as binary quadratic forms).

• v.reg: the regulator, computed to an accuracy which is the maximum of an internal accuracy determined by the program and the current default (note that once the regulator is known to a small accuracy it is trivial to compute it to very high accuracy, see the tutorial).

The flag is obsolete and should be left alone. In older versions, it supposedly computed the narrow class group when D > 0, but this did not work at all; use the general function bnfnarrow.

Optional parameter tech is a row vector of the form [c_{1}, c_{2}], where c_{1} ≤ c_{2} are non-negative real numbers which control the execution time and the stack size, see 3.6.7. The parameter is used as a threshold to balance the relation finding phase against the final linear algebra. Increasing the default c_{1} means that relations are easier to find, but more relations are needed and the linear algebra will be harder. The default value for c_{1} is 0 and means that it is taken equal to c_{2}. The parameter c_{2} is mostly obsolete and should not be changed, but we still document it for completeness: we compute a tentative class group by generators and relations using a factorbase of prime ideals ≤ c_{1}(log |D|)^{2}, then prove that ideals of norm ≤ c_{2}(log |D|)^{2} do not generate a larger group. By default an optimal c_{2} is chosen, so that the result is provably correct under the GRH — a famous result of Bach states that c_{2} = 6 is fine, but it is possible to improve on this algorithmically. You may provide a smaller c_{2}, it will be ignored (we use the provably correct one); you may provide a larger c_{2} than the default value, which results in longer computing times for equally correct outputs (under GRH).

    The library syntax is GEN quadclassunit0(GEN D, long flag, GEN tech = NULL, long

prec). If you really need to experiment with the tech parameter, it is usually more convenient to

use GEN Buchquad(GEN D, double c1, double c2, long prec)

3.4.71 quaddisc(x). Discriminant of the quadratic field Q(\sqrt{x}), where x ∈ Q.

The library syntax is GEN quaddisc(GEN x).

3.4.72 quadgen(D). Creates the quadratic number ω = (a + \sqrt{D})/2 where a = 0 if D ≡ 0 mod 4, a = 1 if D ≡ 1 mod 4, so that (1, ω) is an integral basis for the quadratic order of discriminant D. D must be an integer congruent to 0 or 1 modulo 4, which is not a square.

The library syntax is GEN quadgen(GEN D).

3.4.73 quadhilbert(D). Relative equation defining the Hilbert class field of the quadratic field of discriminant D.

If D < 0, uses complex multiplication (Schertz’s variant).

If D > 0 Stark units are used and (in rare cases) a vector of extensions may be returned whose compositum is the requested class field. See bnrstark for details.

    The library syntax is GEN quadhilbert(GEN D, long prec).

3.4.74 quadpoly(D, {v =′ x}). Creates the “canonical” quadratic polynomial (in the variable v) corresponding to the discriminant D, i.e. the minimal polynomial of quadgen(D). D must be an integer congruent to 0 or 1 modulo 4, which is not a square.

The library syntax is GEN quadpoly0(GEN D, long v = -1), where v is a variable number.

3.4.75 quadray(D, f). Relative equation for the ray class field of conductor f for the quadratic field of discriminant D using analytic methods. A bnf for x^{2} − D is also accepted in place of D.

For D < 0, uses the σ function and Schertz’s method.

For D > 0, uses Stark’s conjecture, and a vector of relative equations may be returned. See bnrstark for more details.

    The library syntax is GEN quadray(GEN D, GEN f, long prec).

3.4.76 quadregulator(x). Regulator of the quadratic field of positive discriminant x. Returns an error if x is not a discriminant (fundamental or not) or if x is a square. See also quadclassunit if x is large.

    The library syntax is GEN quadregulator(GEN x, long prec).

3.4.77 quadunit(D). Fundamental unit of the real quadratic field Q(\sqrt{D}) where D is the positive discriminant of the field. If D is not a fundamental discriminant, this probably gives the fundamental unit of the corresponding order. D must be an integer congruent to 0 or 1 modulo 4, which is not a square; the result is a quadratic number (see Section 3.4.72).

The library syntax is GEN quadunit(GEN D).

3.4.78 randomprime({N = 2^{31}}). Returns a strong pseudo prime (see ispseudoprime) in [2, N − 1]. A t_VEC N = [a, b] is also allowed, with a ≤ b in which case a pseudo prime a ≤ p ≤ b is returned; if no prime exists in the interval, the function will run into an infinite loop. If the upper bound is less than 2^{64} the pseudo prime returned is a proven prime.

    The library syntax is GEN randomprime(GEN N = NULL).

3.4.79 removeprimes({x = [ ]}). Removes the primes listed in x from the prime number table. In particular removeprimes(addprimes()) empties the extra prime table. x can also be a single integer. List the current extra primes if x is omitted.

    The library syntax is GEN removeprimes(GEN x = NULL).

3.4.80 sigma(x, {k = 1}). Sum of the k^{th} powers of the positive divisors of |x|. x and k must be of type integer.

    The library syntax is GEN sumdivk(GEN x, long k). Also available is GEN sumdiv(GEN n),

for k = 1.

3.4.81 sqrtint(x). Returns the integer square root of x, i.e. the largest integer y such that y^{2} ≤ x, where x a non-negative integer.

    ? N = 120938191237; sqrtint(N)
    %1 = 347761
    ? sqrt(N)
    %2 = 347761.68741970412747602130964414095216

The library syntax is GEN sqrtint(GEN x).

3.4.82 sqrtnint(x, n). Returns the integer n-th root of x, i.e. the largest integer y such that y^{n} ≤ x, where x is a non-negative integer.

    ? N = 120938191237; sqrtnint(N, 5)
    %1 = 164
    ? N^(1/5)
    %2 = 164.63140849829660842958614676939677391

The special case n = 2 is sqrtint

    The library syntax is GEN sqrtnint(GEN x, long n).

3.4.83 stirling(n, k, {flag = 1}). Stirling number of the first kind s(n, k) (flag = 1, default) or of the second kind S(n, k) (flag=2), where n, k are non-negative integers. The former is (−1)^{n−k} times the number of permutations of n symbols with exactly k cycles; the latter is the number of ways of partitioning a set of n elements into k non-empty subsets. Note that if all s(n, k) are needed, it is much faster to compute

∑_{k} s(n, k)x^{k} = x(x − 1) … (x − n + 1).

Similarly, if a large number of S(n, k) are needed for the same k, one should use

∑_{n} S(n, k)x^{n} = \frac{x^{k}}{(1 − x) … (1 − kx)}.

(Should be implemented using a divide and conquer product.) Here are simple variants for n fixed:

    /* list of s(n,k), k = 1..n */
    vecstirling(n) = Vec( factorback(vector(n-1,i,1-i*'x)) )
    /* list of S(n,k), k = 1..n */

vecstirling2(n) =
{ my(Q = x^(n-1), t);
  vector(n, i, t = divrem(Q, x-i); Q=t[1]; t[2]);
}
The library syntax is GEN stirling(long n, long k, long flag). Also available are GEN
stirling1(ulong n, ulong k) (flag = 1) and GEN stirling2(ulong n, ulong k) (flag = 2).

3.4.84 sumdedekind(h, k). Returns the Dedekind sum associated to the integers h and k, corresponding to a fast implementation of

  s(h,k) = sum(n = 1, k-1, (n/k)*(frac(h*n/k) - 1/2))

The library syntax is GEN sumdedekind(GEN h, GEN k).

3.4.85 sumdigits(n). Sum of (decimal) digits in the integer n.

? sumdigits(123456789)
%1 = 45

Other bases that 10 are not supported. Note that the sum of bits in n is returned by hammingweight.

The library syntax is GEN sumdigits(GEN n).

3.4.86 zncoppersmith(P, N, X, {B = N}). N being an integer and P ∈ Z[X], finds all integers x with |x| ≤ X such that

gcd(N, P (x)) ≥ B,

using Coppersmith’s algorithm (a famous application of the LLL algorithm). X must be smaller than exp(log^{2} B/(deg(P) log N)): for B = N, this means X < N^{1/ deg(P)}. Some x larger than X may be returned if you are very lucky. The smaller B (or the larger X), the slower the routine will be. The strength of Coppersmith method is the ability to find roots modulo a general composite N: if N is a prime or a prime power, polrootsmod or polrootspadic will be much faster.

We shall now present two simple applications. The first one is finding non-trivial factors of N, given some partial information on the factors; in that case B must obviously be smaller than the largest non-trivial divisor of N.

setrand(1); \\ to make the example reproducible
p = nextprime(random(10^30));
q = nextprime(random(10^30)); N = p*q;
p0 = p % 10^20; \\ assume we know 1) p > 10^29, 2) the last 19 digits of p
p1 = zncoppersmith(10^19*x + p0, N, 10^12, 10^29)
\\ result in 10ms.
%1 = [35023733690]
? gcd(p1[1] * 10^19 + p0, N) == p
%2 = 1

and we recovered p, faster than by trying all possibilities < 10^{12}.

The second application is an attack on RSA with low exponent, when the message x is short and the padding P is known to the attacker. We use the same RSA modulus N as in the first example:

setrand(1);
P = random(N);    \\ known padding
e = 3;            \\ small public encryption exponent
X = floor(N^0.3); \\ N^(1/e - epsilon)
x0 = random(X);   \\ unknown short message
C = lift( (Mod(x0,N) + P)^e ); \\ known ciphertext, with padding P
zncoppersmith((P + x)^3 - C, N, X)
\\ result in 244ms.
%3 = [265174753892462432]
? %[1] == x0
%4 = 1

We guessed an integer of the order of 10^{18}, almost instantly.

The library syntax is GEN zncoppersmith(GEN P, GEN N, GEN X, GEN B = NULL).

3.4.87 znlog(x, g, {o}). Discrete logarithm of x in (Z/NZ)^{∗} in base g. The result is [] when x is not a power of g. If present, o represents the multiplicative order of g, see Section 3.4.2; the preferred format for this parameter is [ord, factor(ord)], where ord is the order of g. This provides a definite speedup when the discrete log problem is simple:

? p = nextprime(10^4); g = znprimroot(p); o = [p-1, factor(p-1)];
? for(i=1,10^4, znlog(i, g, o))
time = 205 ms.
? for(i=1,10^4, znlog(i, g))
time = 244 ms. \\ a little slower

The result is undefined if g is not invertible mod N or if the supplied order is incorrect.

This function uses

• a combination of generic discrete log algorithms (see below).

• in (Z/NZ)^{∗} when N is prime: a linear sieve index calculus method, suitable for N < 10^{50}, say, is used for large prime divisors of the order.

The generic discrete log algorithms are:

• Pohlig-Hellman algorithm, to reduce to groups of prime order q, where q|p − 1 and p is an odd prime divisor of N,

• Shanks baby-step/giant-step (q < 2^{32} is small),

• Pollard rho method (q > 2^{32}).

The latter two algorithms require O(\sqrt{q}) operations in the group on average, hence will not be able to treat cases where q > 10^{30}, say. In addition, Pollard rho is not able to handle the case where there are no solutions: it will enter an infinite loop.

? g = znprimroot(101)
%1 = Mod(2,101)
? znlog(5, g)
%2 = 24
? g^24
%3 = Mod(5, 101)

? G = znprimroot(2 * 101^10)
%4 = Mod(110462212541120451003, 220924425082240902002)
? znlog(5, G)
%5 = 76210072736547066624
? G^% == 5
%6 = 1
? N = 2^4*3^2*5^3*7^4*11; g = Mod(13, N); znlog(g^110, g)
%7 = 110
? znlog(6, Mod(2,3))  \\ no solution
%8 = []

For convenience, g is also allowed to be a p-adic number:

? g = 3+O(5^10); znlog(2, g)
%1 = 1015243
? g^%
%2 = 2 + O(5^10)
The library syntax is GEN znlog(GEN x, GEN g, GEN o = NULL).

3.4.88 znorder(x, {o}). x must be an integer mod n, and the result is the order of x in the multiplicative group (Z/nZ)^{∗}. Returns an error if x is not invertible. The parameter o, if present, represents a non-zero multiple of the order of x, see Section 3.4.2; the preferred format for this parameter is [ord, factor(ord)], where ord = eulerphi(n) is the cardinality of the group.

The library syntax is GEN znorder(GEN x, GEN o = NULL). Also available is GEN order(GEN
x).

3.4.89 znprimroot(n). Returns a primitive root (generator) of (Z/nZ)^{∗}, whenever this latter group is cyclic (n = 4 or n = 2p^{k} or n = p^{k}, where p is an odd prime and k ≥ 0). If the group is not cyclic, the result is undefined. If n is a prime power, then the smallest positive primitive root is returned. This may not be true for n = 2p^{k}, p odd.

Note that this function requires factoring p − 1 for p as above, in order to determine the exact order of elements in (Z/nZ)^{∗}: this is likely to be costly if p is large.

The library syntax is GEN znprimroot(GEN n).

3.4.90 znstar(n). Gives the structure of the multiplicative group (Z/nZ)^{∗} as a 3-component row vector v, where v[1] = ϕ(n) is the order of that group, v[2] is a k-component row-vector d of integers d[i] such that d[i] > 1 and d[i] | d[i − 1] for i ≥ 2 and (Z/nZ)^{∗} ≃ ∏_{i=1}^{k}(Z/d[i]Z), and v[3] is a k-component row vector giving generators of the image of the cyclic groups Z/d[i]Z.

? G = znstar(40)
%1 = [16, [4, 2, 2], [Mod(17, 40), Mod(21, 40), Mod(11, 40)]]
? G.no   \\ eulerphi(40)
%2 = 16
? G.cyc  \\ cycle structure
%3 = [4, 2, 2]
? G.gen  \\ generators for the cyclic components
%4 = [Mod(17, 40), Mod(21, 40), Mod(11, 40)]
? apply(znorder, G.gen)

    %5 = [4, 2, 2]

According to the above definitions, znstar(0) is [2, [2], [-1]], corresponding to Z^{∗}.

The library syntax is GEN znstar(GEN n).

3.5 Functions related to elliptic curves.

3.5.1 Elliptic curve structures. An elliptic curve is given by a Weierstrass model

y^{2} + a_{1}xy + a_{3}y = x^{3} + a_{2}x^{2} + a_{4}x + a_{6},

whose discriminant is non-zero. Affine points on E are represented as two-component vectors [x,y]; the point at infinity, i.e. the identity element of the group law, is represented by the one-component vector [0].

Given a vector of coefficients [a_{1}, a_{2}, a_{3}, a_{4}, a_{6}], the function ellinit initializes and returns an ell structure. (An additional optional argument allows to specify the base field in case it cannot be inferred from the curve coefficients.) This structure contains data needed by elliptic curve related functions, and is generally passed as a first argument. Expensive data are skipped on initialization: they will be dynamically computed when (and if) needed, and then inserted in the structure. The precise layout of the ell structure is left undefined and should never be used directly. The following member functions are available, depending on the underlying domain.

3.5.1.1 All domains.

• a1, a2, a3, a4, a6: coefficients of the elliptic curve.

• b2, b4, b6, b8: b-invariants of the curve; in characteristic ≠ 2, for Y = 2y + a_{1}x + a3, the curve equation becomes

Y^{2} = 4x^{3} + b_{2}x^{2} + 2b_{4}x + b_{6} =: g(x).

• c4, c6: c-invariants of the curve; in characteristic ≠ 2, 3, for X = x + b_{2}/12 and Y = 2y + a_{1}x + a3, the curve equation becomes

Y^{2} = 4X^{3} − (c_{4}/12)X − (c_{6}/216).

• disc: discriminant of the curve. This is only required to be non-zero, not necessarily a unit.

• j: j-invariant of the curve.

These are used as follows:

    ? E = ellinit([0,0,0, a4,a6]);
    ? E.b4
    %2 = 2*a4
    ? E.disc
    %3 = -64*a4^3 - 432*a6^2

3.5.1.2 Curves over R.

This in particular includes curves defined over Q. All member functions in this section return data, as it is currently stored in the structure, if present; and otherwise compute it to the default accuracy, that was fixed at the time of ellinit (via a t_REAL D domain argument, or realprecision by default). The function ellperiods allows to recompute (and cache) the following data to current

realprecision.

• area: volume of the complex lattice defining E.

• roots is a vector whose three components contain the complex roots of the right hand side g(x) of the associated b-model Y^{2} = g(x). If the roots are all real, they are ordered by decreasing value. If only one is real, it is the first component.

• omega: [ω_{1}, ω_{2}], periods forming a basis of the complex lattice defining E. The first component ω_{1} is the (positive) real period, in other words the integral of dx/(2y + a_{1}x + a_{3}) over the connected component of the identity component of E(R). The second component ω_{2} is a complex period, such that τ = \frac{ω_{1}}{ω_{2}} belongs to Poincaré’s half-plane (positive imaginary part); not necessarily to the standard fundamental domain.

• eta is a row vector containing the quasi-periods η_{1} and η_{2} such that η_{i} = 2ζ(ω_{i}/2), where ζ is the Weierstrass zeta function associated to the period lattice; see ellzeta. In particular, the Legendre relation holds: η_{2}ω_{1} − η_{1}ω_{2} = 2iπ.

Warning. As for the orientation of the basis of the period lattice, beware that many sources use the inverse convention where ω_{2}/ω_{1} has positive imaginary part and our ω_{2} is the negative of theirs. Our convention τ = ω_{1}/ω_{2} ensures that the action of PSL_{2} is the natural one:

[a, b; c, d] · τ = (aτ + b)/(cτ + d) = (aω_{1} + bω_{2})/(cω_{1} + dω_{2}),

instead of a twisted one. (Our tau is −1/τ in the above inverse convention.)

3.5.1.3 Curves over Q_{p}.

We advise to input a model defined over Q for such curves. In any case, if you input an approximate model with t_PADIC coefficients, it will be replaced by a lift to Q (an exact model “close” to the one that was input) and all quantities will then be computed in terms of this lifted model.

For the time being only curves with multiplicative reduction (split or non-split), i.e. v_{p}(j) < 0, are supported by non-trivial functions. In this case the curve is analytically isomorphic to Q̄_{p}^{∗}/q^{Z} := E_{q}(Q̄_{p}), for some p-adic integer q (the Tate period). In particular, we have j(q) = j(E).

• p is the residual characteristic

• roots is a vector with a single component, equal to the p-adic root e_{1} of the right hand side g(x) of the associated b-model Y^{2} = g(x). The point (e_{1}, 0) corresponds to −1 ∈ Q̄_{p}^{∗}/q^{Z} under the Tate parametrization.

• tate returns [u^{2}, u, q, [a, b]] in the notation of Henniart-Mestre (CRAS t. 308, p. 391–395, 1989): q is as above, u ∈ Q_{p}(\sqrt{−c_{6}}) is such that ϕ^{∗}dx/(2y + a_{1}x + a3) = udt/t, where ϕ : E_{q} → E is an isomorphism (well defined up to sign) and dt/t is the canonical invariant differential on the Tate curve; u^{2} ∈ Q_{p} does not depend on ϕ. (Technicality: if u ∉ Q_{p}, it is stored as a quadratic t_POLMOD.) Finally, [a, b] satisfy 4u^{2}b · agm(\sqrt{a/b}, 1)^{2} = 1 as in Theorem 2 (loc. cit.).

3.5.1.4 Curves over F_{q}.

• p is the characteristic of F_{q}.

• no is #E(F_{q}).

• cyc gives the cycle structure of E(F_{q}).

• gen returns the generators of E(F_{q}).

• group returns [no, cyc, gen], i.e. E(F_{q}) as an abelian group structure.

3.5.1.5 Curves over Q.

All functions should return a correct result, whether the model is minimal or not, but it is a good idea to stick to minimal models whenever gcd(c_{4}, c_{6}) is easy to factor (minor speed-up). The construction

  E = ellminimalmodel(E0, &v)

replaces the original model E_{0} by a minimal model E, and the variable change v allows to go between the two models:

  ellchangepoint(P0, v)
  ellchangepointinv(P, v)

respectively map the point P_{0} on E_{0} to its image on E, and the point P on E to its pre-image on E_{0}.

A few routines — namely ellgenerators, ellidentify, ellsearch, forell — require the

optional package elldata (John Cremona’s database) to be installed. In that case, the function ellinit will allow alternative inputs, e.g. ellinit("11a1"). Functions using this package need to load chunks of a large database in memory and require at least 2MB stack to avoid stack overflows.

• gen returns the generators of E(Q), if known (from John Cremona’s database)

3.5.2 ellL1(e, r). Returns the value at s = 1 of the derivative of order r of the L-function of the elliptic curve e assuming that r is at most the order of vanishing of the L-function at s = 1. (The result is wrong if r is strictly larger than the order of vanishing at 1.)

? e = ellinit("11a1"); \\ order of vanishing is 0
? ellL1(e, 0)
%2 = 0.2538418608559106843377589233
? e = ellinit("389a1");  \\ order of vanishing is 2
? ellL1(e, 0)
%4 = -5.384067311837218089235032414 E-29
? ellL1(e, 1)
%5 = 0
? ellL1(e, 2)
%6 = 1.518633000576853540460385214

The main use of this function, after computing at low accuracy the order of vanishing using ellanalyticrank, is to compute the leading term at high accuracy to check (or use) the Birch and Swinnerton-Dyer conjecture:

? \p18
  realprecision = 18 significant digits

? ellanalyticrank(ellinit([0, 0, 1, -7, 6]))
time = 32 ms.
%1 = [3, 10.3910994007158041]
? \p200
  realprecision = 202 significant digits (200 digits displayed)
? ellL1(e, 3)
time = 23,113 ms.
%3 = 10.3910994007158041387518505103609170697263563756570092797[. . .]
The library syntax is GEN ellL1(GEN e, long r, long prec).

3.5.3 elladd(E, z1 , z2 ). Sum of the points z1 and z2 on the elliptic curve corresponding to E.

The library syntax is GEN elladd(GEN E, GEN z1, GEN z2).

3.5.4 ellak(E, n). Computes the coefficient a_{n} of the L-function of the elliptic curve E/Q, i.e. coefficients of a newform of weight 2 by the modularity theorem (Taniyama-Shimura-Weil conjecture). E must be an ell structure over Q as output by ellinit. E must be given by an integral model, not necessarily minimal, although a minimal model will make the function faster.

? E = ellinit([0,1]);
? ellak(E, 10)
%2 = 0
? e = ellinit([5^4,5^6]); \\ not minimal at 5
? ellak(e, 5) \\ wasteful but works
%3 = -3
? E = ellminimalmodel(e); \\ now minimal
? ellak(E, 5)
%5 = -3

If the model is not minimal at a number of bad primes, then the function will be slower on those n divisible by the bad primes. The speed should be comparable for other n:

? for(i=1,10^6, ellak(E,5))
time = 820 ms.
? for(i=1,10^6, ellak(e,5)) \\ 5 is bad, markedly slower
time = 1,249 ms.
? for(i=1,10^5,ellak(E,5*i))
time = 977 ms.
? for(i=1,10^5,ellak(e,5*i)) \\ still slower but not so much on average
time = 1,008 ms.
The library syntax is GEN akell(GEN E, GEN n).

3.5.5 ellan(E, n). Computes the vector of the first n Fourier coefficients a_{k} corresponding to the elliptic curve E. The curve must be given by an integral model, not necessarily minimal, although a minimal model will make the function faster.

The library syntax is GEN anell(GEN E, long n). Also available is GEN anellsmall(GEN e,

long n), which returns a t_VECSMALL instead of a t_VEC, saving on memory.

3.5.6 ellanalyticrank(e, {eps}). Returns the order of vanishing at s = 1 of the L-function of the elliptic curve e and the value of the first non-zero derivative. To determine this order, it is assumed that any value less than eps is zero. If no value of eps is given, a value of half the current precision is used.

? e = ellinit("11a1"); \\ rank 0
? ellanalyticrank(e)
%2 = [0, 0.2538418608559106843377589233]
? e = ellinit("37a1"); \\ rank 1
? ellanalyticrank(e)
%4 = [1, 0.3059997738340523018204836835]
? e = ellinit("389a1"); \\ rank 2
? ellanalyticrank(e)
%6 = [2, 1.518633000576853540460385214]
? e = ellinit("5077a1"); \\ rank 3
? ellanalyticrank(e)
%8 = [3, 10.39109940071580413875185035]
The library syntax is GEN ellanalyticrank(GEN e, GEN eps = NULL, long prec).

3.5.7 ellap(E, {p}). Let E be an ell structure as output by ellinit, defined over Q or a finite field F_{q}. The argument p is best left omitted if the curve is defined over a finite field, and must be a prime number otherwise. This function computes the trace of Frobenius t for the elliptic curve E, defined by the equation #E(F_{q}) = q + 1 − t.

If the curve is defined over Q, p must be explicitly given and the function computes the trace of the reduction over F_{p}. The trace of Frobenius is also the a_{p} coefficient in the curve L-series L(E, s) = ∑_{n} a_{n}n^{−s}, whence the function name. The equation must be integral at p but need not be minimal at p; of course, a minimal model will be more efficient.

? E = ellinit([0,1]);  \\ y^2 = x^3 + 0.x + 1, defined over Q
? ellap(E, 7) \\ 7 necessary here
%2 = -4       \\ #E(F_7) = 7+1-(-4) = 12
? ellcard(E, 7)
%3 = 12       \\ OK
? E = ellinit([0,1], 11);  \\ defined over F_11
? ellap(E)       \\ no need to repeat 11
%4 = 0
? ellap(E, 11)   \\ ... but it also works
%5 = 0
? ellgroup(E, 13) \\ ouch, inconsistent input!
   ***   at top-level: ellap(E,13)
   ***                 ^-----------
   *** ellap: inconsistent moduli in Rg_to_Fp:
     11
     13
? Fq = ffgen(ffinit(11,3), 'a); \\ defines F_q := F_{11^3}
? E = ellinit([a+1,a], Fq);  \\ y^2 = x^3 + (a+1)x + a, defined over F_q
? ellap(E)
%8 = -3

Algorithms used. If E/F_{q} has CM by a principal imaginary quadratic order we use a fast explicit formula (involving essentially Kronecker symbols and Cornacchia’s algorithm), in O(log q)^{2}. Otherwise, we use Shanks-Mestre’s baby-step/giant-step method, which runs in time q(p^{1/4}) using O(q^{1/4}) storage, hence becomes unreasonable when q has about 30 digits. If the seadata package is installed, the SEA algorithm becomes available, heuristically in Õ(log q)^{4}, and primes of the order of 200 digits become feasible. In very small characteristic (2,3,5,7 or 13), we use Harley’s algorithm.

    The library syntax is GEN ellap(GEN E, GEN p = NULL).

3.5.8 ellbil(E, z1 , z2 ). If z1 and z2 are points on the elliptic curve E this function computes the value of the canonical bilinear form on z1, z2:

(h(E, z1+z2) − h(E, z1) − h(E, z2))/2

where + denotes of course addition on E. In addition, z1 or z2 (but not both) can be vectors or matrices.

    The library syntax is GEN bilhell(GEN E, GEN z1, GEN z2, long prec).

3.5.9 ellcard(E, {p}). Let E be an ell structure as output by ellinit, defined over Q or a finite field F_{q}. The argument p is best left omitted if the curve is defined over a finite field, and must be a prime number otherwise. This function computes the order of the group E(F_{q}) (as would be computed by ellgroup).

If the curve is defined over Q, p must be explicitly given and the function computes the cardinal of the reduction over F_{p}; the equation need not be minimal at p, but a minimal model will be more efficient. The reduction is allowed to be singular, and we return the order of the group of non-singular points in this case.

The library syntax is GEN ellcard(GEN E, GEN p = NULL). Also available is GEN ell-

card(GEN E, GEN p) where p is not NULL.

3.5.10 ellchangecurve(E, v). Changes the data for the elliptic curve E by changing the coordinates using the vector v=[u,r,s,t], i.e. if x′ and y′ are the new coordinates, then x = u^{2}x′ + r, y = u^{3}y′ + su^{2}x′ + t. E must be an ell structure as output by ellinit. The special case v = 1 is also used instead of [1, 0, 0, 0] to denote the trivial coordinate change.

    The library syntax is GEN ellchangecurve(GEN E, GEN v).

3.5.11 ellchangepoint(x, v). Changes the coordinates of the point or vector of points x using the vector v=[u,r,s,t], i.e. if x′ and y′ are the new coordinates, then x = u^{2}x′ +r, y = u^{3}y′ +su^{2}x′ +t

(see also ellchangecurve).
    ? E0 = ellinit([1,1]); P0 = [0,1]; v = [1,2,3,4];
    ? E = ellchangecurve(E0, v);
    ? P = ellchangepoint(P0,v)
    %3 = [-2, 3]
    ? ellisoncurve(E, P)
    %4 = 1
    ? ellchangepointinv(P,v)
    %5 = [0, 1]

The library syntax is GEN ellchangepoint(GEN x, GEN v). The reciprocal function GEN

ellchangepointinv(GEN x, GEN ch) inverts the coordinate change.

3.5.12 ellchangepointinv(x, v). Changes the coordinates of the point or vector of points x using the inverse of the isomorphism associated to v=[u,r,s,t], i.e. if x′ and y′ are the old coordinates, then x = u^{2}x′ + r, y = u^{3}y′ + su^{2}x′ + t (inverse of ellchangepoint).

? E0 = ellinit([1,1]); P0 = [0,1]; v = [1,2,3,4];
? E = ellchangecurve(E0, v);
? P = ellchangepoint(P0,v)
%3 = [-2, 3]
? ellisoncurve(E, P)
%4 = 1
? ellchangepointinv(P,v)
%5 = [0, 1]  \\ we get back P0
The library syntax is GEN ellchangepointinv(GEN x, GEN v).

3.5.13 ellconvertname(name). Converts an elliptic curve name, as found in the elldata database, from a string to a triplet [conductor, isogeny class, index]. It will also convert a triplet back to a curve name. Examples:

? ellconvertname("123b1")
%1 = [123, 1, 1]
? ellconvertname(%)
%2 = "123b1"
The library syntax is GEN ellconvertname(GEN name).

3.5.14 elldivpol(E, n, {v =′ x}). n-division polynomial f_{n} for the curve E in the variable v. In standard notation, for any affine point P = (X, Y) on the curve, we have

[n]P = (ϕ_{n}(P)ψ_{n}(P) : ω_{n}(P) : ψ_{n}(P)^{3})

for some polynomials ϕ_{n}, ω_{n}, ψ_{n} in Z[a_{1}, a_{2}, a_{3}, a_{4}, a_{6}][X, Y]. We have f_{n}(X) = ψ_{n}(X) for n odd, and f_{n}(X) = ψ_{n}(X, Y)(2Y + a_{1}X + a_{3}) for n even. We have

f_{1} = 1, f_{2} = 4X^{3} + b_{2}X^{2} + 2b_{4}X + b_{6}, f_{3} = 3X^{4} + b_{2}X^{3} + 3b_{4}X^{2} + 3b_{6}X + b8,

f_{4} = f_{2}(2X^{6} + b_{2}X^{5} + 5b_{4}X^{4} + 10b_{6}X^{3} + 10b_{8}X^{2} + (b_{2}b_{8} − b_{4}b_{6})X + (b_{8}b_{4} − b_{6}^{2})), …

For n ≥ 2, the roots of f_{n} are the X-coordinates of points in E[n].

The library syntax is GEN elldivpol(GEN E, long n, long v = -1), where v is a variable number.

3.5.15 elleisnum(w, k, {flag = 0}). k being an even positive integer, computes the numerical value of the Eisenstein series of weight k at the lattice w, as given by ellperiods, namely

(2iπ/ω_{2})^{k}(1 + 2/ζ(1 − k) ∑_{n≥0} n^{k−1}q^{n}/(1 − q^{n})),

where q = exp(2iπτ) and τ := ω_{1}/ω_{2} belongs to the complex upper half-plane. It is also possible to directly input w = [ω_{1}, ω_{2}], or an elliptic curve E as given by ellinit.

    ? w = ellperiods([1,I]);
    ? elleisnum(w, 4)
    %2 = 2268.8726415508062275167367584190557607
    ? elleisnum(w, 6)
    %3 = -3.977978632282564763 E-33
    ? E = ellinit([1, 0]);
    ? elleisnum(E, 4, 1)
    %5 = -47.999999999999999999999999999999999998

When flag is non-zero and k = 4 or 6, returns the elliptic invariants g_{2} or g_{3}, such that

y^{2} = 4x^{3} − g_{2}x − g_{3}

is a Weierstrass equation for E.

    The library syntax is GEN elleisnum(GEN w, long k, long flag, long prec).

3.5.16 elleta(w). Returns the quasi-periods [η_{1}, η_{2}] associated to the lattice basis w = [ω_{1}, ω_{2}]. Alternatively, w can be an elliptic curve E as output by ellinit, in which case, the quasi periods associated to the period lattice basis E.omega (namely, E.eta) are returned.

    ? elleta([1, I])
    %1 = [3.141592653589793238462643383, 9.424777960769379715387930149*I]
    The library syntax is GEN elleta(GEN w, long prec).

3.5.17 ellfromj(j). Returns the coefficients [a_{1}, a_{2}, a_{3}, a_{4}, a_{6}] of a fixed elliptic curve with jinvariant j.

The library syntax is GEN ellfromj(GEN j).

3.5.18 ellgenerators(E). If E is an elliptic curve over the rationals, return a Z-basis of the free part of the Mordell-Weil group associated to E. This relies on the elldata database being installed and referencing the curve, and so is only available for curves over Z of small conductors. If E is an elliptic curve over a finite field F_{q} as output by ellinit, return a minimal set of generators for the group E(F_{q}).

    The library syntax is GEN ellgenerators(GEN E).

3.5.19 ellglobalred(E). Calculates the arithmetic conductor, the global minimal model of E and the global Tamagawa number c. E must be an ell structure as output by ellinit, defined over Q. The result is a vector [N, v, c, F, L], where

• N is the arithmetic conductor of the curve,

• v gives the coordinate change for E over Q to the minimal integral model (see ellmini-

malmodel),

• c is the product of the local Tamagawa numbers c_{p}, a quantity which enters in the Birch and Swinnerton-Dyer conjecture,

• F is the factorization of N over Z.

• L is a vector, whose i-th entry contains the local data at the i-th prime divisor of N, i.e. L[i] = elllocalred(E,F[i,1]), where the local coordinate change has been deleted, and replaced by a 0.

    The library syntax is GEN ellglobalred(GEN E).

3.5.20 ellgroup(E, {p}, {flag}). Let E be an ell structure as output by ellinit, defined over Q or a finite field F_{q}. The argument p is best left omitted if the curve is defined over a finite field, and must be a prime number otherwise. This function computes the structure of the group E(F_{q}) ∼ Z/d_{1}Z × Z/d_{2}Z, with d_{2} | d_{1}.

If the curve is defined over Q, p must be explicitly given and the function computes the structure of the reduction over F_{p}; the equation need not be minimal at p, but a minimal model will be more efficient. The reduction is allowed to be singular, and we return the structure of the (cyclic) group of non-singular points in this case.

If the flag is 0 (default), return [d_{1}] or [d_{1}, d_{2}], if d_{2} > 1. If the flag is 1, return a triple [h, cyc, gen], where h is the curve cardinality, cyc gives the group structure as a product of cyclic groups (as per flag = 0). More precisely, if d_{2} > 1, the output is [d_{1}d_{2}, [d_{1}, d_{2}], [P, Q]] where P is of order d_{1} and [P, Q] generates the curve.

Caution. It is not guaranteed that Q has order d_{2}, which in the worst case requires an expensive discrete log computation. Only that ellweilpairing(E, P, Q, d1) has order d_{2}.

    ? E = ellinit([0,1]);  \\ y^2 = x^3 + 0.x + 1, defined over Q
    ? ellgroup(E, 7)
    %2 = [6, 2] \\ Z/6 x Z/2, non-cyclic
    ? E = ellinit([0,1] * Mod(1,11));  \\ defined over F_11
    ? ellgroup(E)   \\ no need to repeat 11
    %4 = [12]
    ? ellgroup(E, 11)   \\ ... but it also works
    %5 = [12]
    ? ellgroup(E, 13) \\ ouch, inconsistent input!
       ***   at top-level: ellgroup(E,13)
       ***                 ^--------------
       *** ellgroup: inconsistent moduli in Rg_to_Fp:
         11
         13
    ? ellgroup(E, 7, 1)
    %6 = [12, [6, 2], [[Mod(2, 7), Mod(4, 7)], [Mod(4, 7), Mod(4, 7)]]]

If E is defined over Q, we allow singular reduction and in this case we return the structure of the group of non-singular points, satisfying #E_{ns}(F_{p}) = p − a_{p}.

? E = ellinit([0,5]);
? ellgroup(E, 5, 1)
%2 = [5, [5], [[Mod(4, 5), Mod(2, 5)]]]
? ellap(E, 5)
%3 = 0 \\ additive reduction at 5
? E = ellinit([0,-1,0,35,0]);
? ellgroup(E, 5, 1)
%5 = [4, [4], [[Mod(2, 5), Mod(2, 5)]]]
? ellap(E, 5)
%6 = 1 \\ split multiplicative reduction at 5
? ellgroup(E, 7, 1)
%7 = [8, [8], [[Mod(3, 7), Mod(5, 7)]]]
? ellap(E, 7)
%8 = -1 \\ non-split multiplicative reduction at 7
The library syntax is GEN ellgroup0(GEN E, GEN p = NULL, long flag). Also available is

GEN ellgroup(GEN E, GEN p), corresponding to flag= 0.

3.5.21 ellheegner(E). Let E be an elliptic curve over the rationals, assumed to be of (analytic) rank 1. This returns a non-torsion rational point on the curve, whose canonical height is equal to the product of the elliptic regulator by the analytic Sha.

This uses the Heegner point method, described in Cohen GTM 239; the complexity is proportional to the product of the square root of the conductor and the height of the point (thus, it is preferable to apply it to strong Weil curves).

? E = ellinit([-157^2,0]);
? u = ellheegner(E); print(u[1], "\n", u[2])
69648970982596494254458225/166136231668185267540804
538962435089604615078004307258785218335/67716816556077455999228495435742408
? ellheegner(ellinit([0,1]))         \\ E has rank 0 !
 ***   at top-level: ellheegner(E=ellinit
 ***                 ^--------------------
 *** ellheegner: The curve has even analytic rank.
The library syntax is GEN ellheegner(GEN E).

3.5.22 ellheight(E, x, {flag = 2}). Global Néron-Tate height of the point z on the elliptic curve E (defined over Q), using the normalization in Cremona’s Algorithms for modular elliptic curves. E must be an ell as output by ellinit; it needs not be given by a minimal model although the computation will be faster if it is. flag selects the algorithm used to compute the Archimedean local height. If flag = 0, we use sigma and theta-functions and Silverman’s trick (Computing heights on elliptic curves, Math. Comp. 51; note that our height is twice Silverman’s height). If flag = 1, use Tate’s 4^{n} algorithm. If flag = 2, use Mestre’s AGM algorithm. The latter converges quadratically and is much faster than the other two.

The library syntax is GEN ellheight0(GEN E, GEN x, long flag, long prec). Also avail-
able is GEN ghell(GEN E, GEN x, long prec) (flag = 2).

3.5.23 ellheightmatrix(E, x). x being a vector of points, this function outputs the Gram matrix of x with respect to the Néron-Tate height, in other words, the (i, j) component of the matrix is equal to ellbil(E,x[i],x[j]). The rank of this matrix, at least in some approximate sense, gives the rank of the set of points, and if x is a basis of the Mordell-Weil group of E, its determinant is equal to the regulator of E. Note our height normalization follows Cremona’s Algorithms for modular elliptic curves: this matrix should be divided by 2 to be in accordance with, e.g., Silverman’s normalizations.

    The library syntax is GEN mathell(GEN E, GEN x, long prec).

3.5.24 ellidentify(E). Look up the elliptic curve E, defined by an arbitrary model over Q, in the elldata database. Return [[N, M, G], C] where N is the curve name in Cremona’s elliptic curve database, M is the minimal model, G is a Z-basis of the free part of the Mordell-Weil group E(Q) and C is the change of coordinates change, suitable for ellchangecurve.

    The library syntax is GEN ellidentify(GEN E).

3.5.25 ellinit(x, {D = 1}). Initialize an ell structure, associated to the elliptic curve E. E is either

• a 5-component vector [a_{1}, a_{2}, a_{3}, a_{4}, a_{6}] defining the elliptic curve with Weierstrass equation

Y^{2} + a_{1}XY + a_{3}Y = X^{3} + a_{2}X^{2} + a_{4}X + a_{6},

• a 2-component vector [a_{4}, a_{6}] defining the elliptic curve with short Weierstrass equation

Y^{2} = X^{3} + a_{4}X + a_{6},

• a character string in Cremona’s notation, e.g. "11a1", in which case the curve is retrieved from the elldata database if available.

The optional argument D describes the domain over which the curve is defined:

• the t_INT 1 (default): the field of rational numbers Q.

• a t_INT p, where p is a prime number: the prime finite field F_{p}.

• an t_INTMOD Mod(a, p), where p is a prime number: the prime finite field F_{p}.

• a t_FFELT, as returned by ffgen: the corresponding finite field F_{q}.

• a t_PADIC, O(p^{n}): the field Q_{p}, where p-adic quantities will be computed to a relative accuracy of n digits. We advise to input a model defined over Q for such curves. In any case, if you input an approximate model with t_PADIC coefficients, it will be replaced by a lift to Q (an exact model “close” to the one that was input) and all quantities will then be computed in terms of this lifted model, at the given accuracy.

• a t_REAL x: the field C of complex numbers, where floating point quantities are by default computed to a relative accuracy of precision(x). If no such argument is given, the value of realprecision at the time ellinit is called will be used.

This argument D is indicative: the curve coefficients are checked for compatibility, possibly changing D; for instance if D = 1 and an t_INTMOD is found. If inconsistencies are detected, an error is raised:

? ellinit([1 + O(5), 1], O(7));
 ***   at top-level: ellinit([1+O(5),1],O
 ***                 ^--------------------
 *** ellinit: inconsistent moduli in ellinit: 7 != 5

If the curve coefficients are too general to fit any of the above domain categories, only basic operations, such as point addition, will be supported later.

If the curve (seen over the domain D) is singular, fail and return an empty vector [].

? E = ellinit([0,0,0,0,1]); \\ y^2 = x^3 + 1, over Q
? E = ellinit([0,1]);       \\ the same curve, short form
? E = ellinit("36a1");      \\ sill the same curve, Cremona's notations
? E = ellinit([0,1], 2)     \\ over F2: singular curve
%4 = []
? E = ellinit(['a4,'a6] * Mod(1,5));  \\ over F_5[a4,a6], basic support !

The result of ellinit is an ell structure. It contains at least the following information in its components:

a_{1}, a_{2}, a_{3}, a_{4}, a_{6}, b_{2}, b_{4}, b_{6}, b_{8}, c_{4}, c_{6}, Δ, j.

All are accessible via member functions. In particular, the discriminant is E.disc, and the jinvariant is E.j.

? E = ellinit([a4, a6]);
? E.disc
%2 = -64*a4^3 - 432*a6^2
? E.j
%3 = -6912*a4^3/(-4*a4^3 - 27*a6^2)

Further components contain domain-specific data, which are in general dynamic: only computed when needed, and then cached in the structure.

? E = ellinit([2,3], 10^60+7);  \\ E over F_p, p large
? ellap(E)
time = 4,440 ms.
%2 = -1376268269510579884904540406082
? ellcard(E);  \\ now instantaneous !
time = 0 ms.
? ellgenerators(E);
time = 5,965 ms.
? ellgenerators(E); \\ second time instantaneous
time = 0 ms.

See the description of member functions related to elliptic curves at the beginning of this section.

The library syntax is GEN ellinit(GEN x, GEN D = NULL, long prec).

3.5.26 ellisoncurve(E, z). Gives 1 (i.e. true) if the point z is on the elliptic curve E, 0 otherwise. If E or z have imprecise coefficients, an attempt is made to take this into account, i.e. an imprecise equality is checked, not a precise one. It is allowed for z to be a vector of points in which case a vector (of the same type) is returned.

    The library syntax is GEN ellisoncurve(GEN E, GEN z). Also available is int oncurve(GEN

E, GEN z) which does not accept vectors of points.

3.5.27 ellj(x). Elliptic j-invariant. x must be a complex number with positive imaginary part, or convertible into a power series or a p-adic number with positive valuation.

    The library syntax is GEN jell(GEN x, long prec).

3.5.28 elllocalred(E, p). Calculates the Kodaira type of the local fiber of the elliptic curve E at the prime p. E must be an ell structure as output by ellinit, and is assumed to have all its coefficients a_{i} in Z. The result is a 4-component vector [f, kod, v, c]. Here f is the exponent of p in the arithmetic conductor of E, and kod is the Kodaira type which is coded as follows:

1 means good reduction (type I_{0}), 2, 3 and 4 mean types II, III and IV respectively, 4 + ν with ν > 0 means type I_{ν}; finally the opposite values −1, −2, etc. refer to the starred types I_{0}^{∗}, II^{∗}, etc. The third component v is itself a vector [u, r, s, t] giving the coordinate changes done during the local reduction; u = 1 if and only if the given equation was already minimal at p. Finally, the last component c is the local Tamagawa number c_{p}.

    The library syntax is GEN elllocalred(GEN E, GEN p).

3.5.29 elllog(E, P, G, {o}). Given two points P and G on the elliptic curve E/F_{q}, returns the discrete logarithm of P in base G, i.e. the smallest non-negative integer n such that P = [n]G. See znlog for the limitations of the underlying discrete log algorithms. If present, o represents the order of G, see Section 3.4.2; the preferred format for this parameter is [N, factor(N)], where N is the order of G.

If no o is given, assume that G generates the curve. The function also assumes that P is a multiple of G.

    ? a = ffgen(ffinit(2,8),'a);
    ? E = ellinit([a,1,0,0,1]);  \\ over F_{2^8}
    ? x = a^3; y = ellordinate(E,x)[1];
    ? P = [x,y]; G = ellmul(E, P, 113);
    ? ord = [242, factor(242)]; \\ P generates a group of order 242. Initialize.
    ? ellorder(E, G, ord)
    %4 = 242
    ? e = elllog(E, P, G, ord)
    %5 = 15
    ? ellmul(E,G,e) == P
    %6 = 1
    The library syntax is GEN elllog(GEN E, GEN P, GEN G, GEN o = NULL).

3.5.30 elllseries(E, s, {A = 1}). E being an elliptic curve, given by an arbitrary model over Q as output by ellinit, this function computes the value of the L-series of E at the (complex) point s. This function uses an O(N^{1/2}) algorithm, where N is the conductor.

The optional parameter A fixes a cutoff point for the integral and is best left omitted; the result must be independent of A, up to realprecision, so this allows to check the function’s accuracy.

    The library syntax is GEN elllseries(GEN E, GEN s, GEN A = NULL, long prec).

3.5.31 ellminimalmodel(E, {&v}). Return the standard minimal integral model of the rational elliptic curve E. If present, sets v to the corresponding change of variables, which is a vector [u, r, s, t] with rational components. The return value is identical to that of ellchangecurve(E,

v).

The resulting model has integral coefficients, is everywhere minimal, a_{1} is 0 or 1, a_{2} is 0, 1 or −1 and a_{3} is 0 or 1. Such a model is unique, and the vector v is unique if we specify that u is positive, which we do.

    The library syntax is GEN ellminimalmodel(GEN E, GEN *v = NULL).

3.5.32 ellmodulareqn(N, {x}, {y}). Return a vector [eqn,t] where eqn is a modular equation of level N, i.e. a bivariate polynomial with integer coefficients; t indicates the type of this equation: either canonical (t = 0) or Atkin (t = 1). This function currently requires the package seadata to be installed and is limited to N < 500, N prime.

Let j be the j-invariant function. The polynomial eqn satisfies the following functional equation, which allows to compute the values of the classical modular polynomial Φ_{N} of prime level N, such that Φ_{N}(j(τ), j(Nτ)) = 0, while being much smaller than the latter:

• for canonical type: P (f(τ), j(τ)) = P (N^{s}/f(τ), j(Nτ)) = 0, where s = 12/gcd(12, N − 1);

• for Atkin type: P (f(τ), j(τ)) = P (f(τ), j(Nτ)) = 0.

In both cases, f is a suitable modular function (see below).

The following GP function returns values of the classical modular polynomial by eliminating f(τ) in the above two equations, for N ≤ 31 or N ∈ {41, 47, 59, 71}.

    classicaleqn(N, X='X, Y='Y)=
    {
      my(E=ellmodulareqn(N), P=E[1], t=E[2], Q, d);
      if(poldegree(P,'y)>2,error("level unavailable in classicaleqn"));
      if (t == 0,
        my(s = 12/gcd(12,N-1));
        Q = 'x^(N+1) * substvec(P,['x,'y],[N^s/'x,Y]);
        d = N^(s*(2*N+1)) * (-1)^(N+1);
      ,
        Q = subst(P,'y,Y);
        d = (X-Y)^(N+1));
      polresultant(subst(P,'y,X), Q) / d;
    }

More precisely, let W_{N}(τ) = \frac{−1}{Nτ} be the Atkin-Lehner involution; we have j(W_{N}(τ)) = j(Nτ) and the function f also satisfies:

• for canonical type: f(W_{N}(τ)) = N^{s}/f(τ);

• for Atkin type: f(W_{N}(τ)) = f(τ).

Furthermore, for an equation of canonical type, f is the standard η-quotient

f(τ) = N^{s}(η(Nτ)/η(τ))^{2s},

where η is Dedekind’s eta function, which is invariant under Γ_{0}(N).

The library syntax is GEN ellmodulareqn(long N, long x = -1, long y = -1), where x,

y are variable numbers.

3.5.33 ellmul(E, z, n). Computes [n]z, where z is a point on the elliptic curve E. The exponent n is in Z, or may be a complex quadratic integer if the curve E has complex multiplication by n (if not, an error message is issued).

? Ei = ellinit([1,0]); z = [0,0];
? ellmul(Ei, z, 10)
%2 = [0]     \\ unsurprising: z has order 2
? ellmul(Ei, z, I)
%3 = [0, 0]  \\ Ei has complex multiplication by Z[i]
? ellmul(Ei, z, quadgen(-4))
%4 = [0, 0]  \\ an alternative syntax for the same query
? Ej  = ellinit([0,1]); z = [-1,0];
? ellmul(Ej, z, I)
  ***   at top-level: ellmul(Ej,z,I)
  ***                 ^--------------
  *** ellmul: not a complex multiplication in ellmul.
? ellmul(Ej, z, 1+quadgen(-3))
%6 = [1 - w, 0]

The simple-minded algorithm for the CM case assumes that we are in characteristic 0, and that the quadratic order to which n belongs has small discriminant.

The library syntax is GEN ellmul(GEN E, GEN z, GEN n).

3.5.34 ellneg(E, z). Opposite of the point z on elliptic curve E.

The library syntax is GEN ellneg(GEN E, GEN z).

3.5.35 ellorder(E, z, {o}). Gives the order of the point z on the elliptic curve E, defined over Q or a finite field. If the curve is defined over Q, return (the impossible value) zero if the point has infinite order.

? E = ellinit([-157^2,0]);  \\ the "157-is-congruent" curve
? P = [2,2]; ellorder(E, P)
%2 = 2
? P = ellheegner(E); ellorder(E, P) \\ infinite order
%3 = 0
? E = ellinit(ellfromj(ffgen(5^10)));
? ellcard(E)
%5 = 9762580

? P = random(E); ellorder(E, P)
%6 = 4881290
? p = 2^160+7; E = ellinit([1,2], p);
? N = ellcard(E)
%8 = 1461501637330902918203686560289225285992592471152
? o = [N, factor(N)];
? for(i=1,100, ellorder(E,random(E)))
time = 260 ms.

The parameter o, is now mostly useless, and kept for backward compatibility. If present, it represents a non-zero multiple of the order of z, see Section 3.4.2; the preferred format for this parameter is [ord, factor(ord)], where ord is the cardinality of the curve. It is no longer needed since PARI is now able to compute it over large finite fields (was restricted to small prime fields at the time this feature was introduced), and caches the result in E so that it is computed and factored only once. Modifying the last example, we see that including this extra parameter provides no improvement:

? o = [N, factor(N)];
? for(i=1,100, ellorder(E,random(E),o))
time = 260 ms.
The library syntax is GEN ellorder(GEN E, GEN z, GEN o = NULL). The obsolete form GEN

orderell(GEN e, GEN z) should no longer be used.

3.5.36 ellordinate(E, x). Gives a 0, 1 or 2-component vector containing the y-coordinates of the points of the curve E having x as x-coordinate.

The library syntax is GEN ellordinate(GEN E, GEN x, long prec).

3.5.37 ellperiods(w, {flag = 0}). Let w describe a complex period lattice (w = [w_{1}, w_{2}] or an ellinit structure). Returns normalized periods [W_{1}, W_{2}] generating the same lattice such that τ := W_{1}/W_{2} has positive imaginary part and lies in the standard fundamental domain for SL_{2}(Z).

If flag = 1, the function returns [[W_{1}, W_{2}], [η_{1}, η_{2}]], where η_{1} and η_{2} are the quasi-periods associated to [W_{1}, W_{2}], satisfying η_{1}W_{2} − η_{2}W_{1} = 2iπ.

The output of this function is meant to be used as the first argument given to ellwp, ellzeta, ellsigma or elleisnum. Quasi-periods are needed by ellzeta and ellsigma only.

The library syntax is GEN ellperiods(GEN w, long flag , long prec).

3.5.38 ellpointtoz(E, P). If E/C ≃ C/Λ is a complex elliptic curve (Λ = E.omega), computes a complex number z, well-defined modulo the lattice Λ, corresponding to the point P; i.e. such that P = [℘_{Λ}(z), ℘′_{Λ}(z)] satisfies the equation

y^{2} = 4x^{3} − g_{2}x − g_{3},

where g_{2}, g_{3} are the elliptic invariants.

If E is defined over R and P ∈ E(R), we have more precisely, 0 ≤ ℜ(t) < w1 and 0 ≤ ℑ(t) < ℑ(w2), where (w1, w2) are the real and complex periods of E.

? E = ellinit([0,1]); P = [2,3];
? z = ellpointtoz(E, P)

%2 = 3.5054552633136356529375476976257353387
? ellwp(E, z)
%3 = 2.0000000000000000000000000000000000000
? ellztopoint(E, z) - P
%4 = [6.372367644529809109 E-58, 7.646841173435770930 E-57]
? ellpointtoz(E, [0]) \\ the point at infinity
%5 = 0

If E/Q_{p} has multiplicative reduction, then E/Q̄_{p} is analytically isomorphic to Q̄_{p}^{∗}/q^{Z} (Tate curve) for some p-adic integer q. The behaviour is then as follows:

• If the reduction is split (E.tate[2] is a t_PADIC), we have an isomorphism ϕ : E(Q_{p}) ≃ Q_{p}^{∗}/q^{Z} and the function returns ϕ(P) ∈ Q_{p}.

• If the reduction is not split (E.tate[2] is a t_POLMOD), we only have an isomorphism ϕ : E(K) ≃ K^{∗}/q^{Z} over the unramified quadratic extension K/Q_{p}. In this case, the output ϕ(P) ∈ K

is a t_POLMOD.
? E = ellinit([0,-1,1,0,0], O(11^5)); P = [0,0];
? [u2,u,q] = E.tate; type(u) \\ split multiplicative reduction
%2 = "t_PADIC"
? ellmul(E, P, 5)  \\ P has order 5
%3 = [0]
? z = ellpointtoz(E, [0,0])
%4 = 3 + 11^2 + 2*11^3 + 3*11^4 + O(11^5)
? z^5
%5 = 1 + O(11^5)
? E = ellinit(ellfromj(1/4), O(2^6)); x=1/2; y=ellordinate(E,x)[1];
? z = ellpointtoz(E,[x,y]); \\ t_POLMOD of t_POL with t_PADIC coeffs
? liftint(z) \\ lift all p-adics
%8 = Mod(8*u + 7, u^2 + 437)
The library syntax is GEN zell(GEN E, GEN P, long prec).

3.5.39 ellpow(E, z, n). Deprecated alias for ellmul.

The library syntax is GEN ellmul(GEN E, GEN z, GEN n).

3.5.40 ellrootno(E, {p}). E being an ell structure over Q as output by ellinit, this function computes the local root number of its L-series at the place p (at the infinite place if p = 0). If p is omitted, return the global root number. Note that the global root number is the sign of the functional equation and conjecturally is the parity of the rank of the Mordell-Weil group. The equation for E needs not be minimal at p, but if the model is already minimal the function will run faster.

The library syntax is long ellrootno(GEN E, GEN p = NULL).

3.5.41 ellsearch(N). This function finds all curves in the elldata database satisfying the constraint defined by the argument N:

• if N is a character string, it selects a given curve, e.g. "11a1", or curves in the given isogeny class, e.g. "11a", or curves with given conductor, e.g. "11";

• if N is a vector of integers, it encodes the same constraints as the character string above, according to the ellconvertname correspondance, e.g. [11,0,1] for "11a1", [11,0] for "11a"

and [11] for "11";

• if N is an integer, curves with conductor N are selected.

If N is a full curve name, e.g. "11a1" or [11,0,1], the output format is [N, [a_{1}, a_{2}, a_{3}, a_{4}, a_{6}], G] where [a_{1}, a_{2}, a_{3}, a_{4}, a_{6}] are the coefficients of the Weierstrass equation of the curve and G is a Z-basis of the free part of the Mordell-Weil group associated to the curve.

? ellsearch("11a3")
%1 = ["11a3", [0, -1, 1, 0, 0], []]
? ellsearch([11,0,3])
%2 = ["11a3", [0, -1, 1, 0, 0], []]

If N is not a full curve name, then the output is a vector of all matching curves in the above format:

? ellsearch("11a")
%1 = [["11a1", [0, -1, 1, -10, -20], []],
      ["11a2", [0, -1, 1, -7820, -263580], []],
      ["11a3", [0, -1, 1, 0, 0], []]]
? ellsearch("11b")
%2 = []
The library syntax is GEN ellsearch(GEN N). Also available is GEN ellsearchcurve(GEN N)

that only accepts complete curve names (as t_STR).

3.5.42 ellsigma(L, {z =′ x}, {flag = 0}). Computes the value at z of the Weierstrass σ function attached to the lattice L as given by ellperiods(, 1): including quasi-periods is useful, otherwise there are recomputed from scratch for each new z.

σ(z, L) = z ∏_{ω∈L^{∗}} (1 − \frac{z}{ω} ) e^{\frac{z}{ω} + \frac{z^{2}}{2ω^{2}}} .

It is also possible to directly input L = [ω_{1}, ω_{2}], or an elliptic curve E as given by ellinit

(L = E.omega).
? w = ellperiods([1,I], 1);
? ellsigma(w, 1/2)
%2 = 0.47494937998792065033250463632798296855
? E = ellinit([1,0]);
? ellsigma(E) \\ at 'x, implicitly at default seriesprecision
%4 = x + 1/60*x^5 - 1/10080*x^9 - 23/259459200*x^13 + O(x^17)

If flag = 1, computes an arbitrary determination of log(σ(z)).

The library syntax is GEN ellsigma(GEN L, GEN z = NULL, long flag, long prec).

3.5.43 ellsub(E, z1 , z2 ). Difference of the points z1 and z2 on the elliptic curve corresponding to E.

    The library syntax is GEN ellsub(GEN E, GEN z1, GEN z2).

3.5.44 elltaniyama(E, {d = seriesprecision}). Computes the modular parametrization of the elliptic curve E/Q, where E is an ell structure as output by ellinit. This returns a two-component vector [u, v] of power series, given to d significant terms (seriesprecision by default), characterized by the following two properties. First the point (u, v) satisfies the equation of the elliptic curve. Second, let N be the conductor of E and Φ : X_{0}(N) → E be a modular parametrization; the pullback by Φ of the Néron differential du/(2v + a_{1}u + a_{3}) is equal to 2iπf(z)dz, a holomorphic differential form. The variable used in the power series for u and v is x, which is implicitly understood to be equal to exp(2iπz).

The algorithm assumes that E is a strong Weil curve and that the Manin constant is equal to 1: in fact, f(x) = ∑_{n>0} ellan(E, n)x^{n}.

    The library syntax is GEN elltaniyama(GEN E, long precdl).

3.5.45 elltatepairing(E, P, Q, m). Computes the Tate pairing of the two points P and Q on the elliptic curve E. The point P must be of m-torsion.

    The library syntax is GEN elltatepairing(GEN E, GEN P, GEN Q, GEN m).

3.5.46 elltors(E, {flag = 0}). If E is an elliptic curve defined over Q, outputs the torsion subgroup of E as a 3-component vector [t,v1,v2], where t is the order of the torsion group, v1 gives the structure of the torsion group as a product of cyclic groups (sorted by decreasing order), and v2 gives generators for these cyclic groups. E must be an ell structure as output by ellinit, defined over Q.

    ?  E = ellinit([-1,0]);
    ?  elltors(E)
    %1 = [4, [2, 2], [[0, 0], [1, 0]]]

Here, the torsion subgroup is isomorphic to Z/2Z × Z/2Z, with generators [0, 0] and [1, 0].

If flag = 0, find rational roots of division polynomials.

If flag = 1, use Lutz-Nagell (much slower).

If flag = 2, use Doud’s algorithm: bound torsion by computing #E(F_{p}) for small primes of good reduction, then look for torsion points using Weierstrass ℘ function (and Mazur’s classification). For this variant, E must be an ell.

    The library syntax is GEN elltors0(GEN E, long flag). Also available is GEN elltors(GEN
E) for elltors(E, 0).

3.5.47 ellweilpairing(E, P, Q, m). Computes the Weil pairing of the two points of m-torsion P and Q on the elliptic curve E.

    The library syntax is GEN ellweilpairing(GEN E, GEN P, GEN Q, GEN m).

3.5.48 ellwp(w, {z =′ x}, {flag = 0}). Computes the value at z of the Weierstrass ℘ function attached to the lattice w as given by ellperiods. It is also possible to directly input w = [ω_{1}, ω_{2}], or an elliptic curve E as given by ellinit (w = E.omega).

? w = ellperiods([1,I]);
? ellwp(w, 1/2)
%2 = 6.8751858180203728274900957798105571978
? E = ellinit([1,1]);
? ellwp(E, 1/2)
%4 = 3.9413112427016474646048282462709151389

One can also compute the series expansion around z = 0:

? E = ellinit([1,0]);
? ellwp(E)              \\ 'x implicitly at default seriesprecision
%5 = x^-2 - 1/5*x^2 + 1/75*x^6 - 2/4875*x^10 + O(x^14)
? ellwp(E, x + O(x^12)) \\ explicit precision
%6 = x^-2 - 1/5*x^2 + 1/75*x^6 + O(x^9)

Optional flag means 0 (default): compute only ℘(z), 1: compute [℘(z), ℘′(z)].

The library syntax is GEN ellwp0(GEN w, GEN z = NULL, long flag, long prec). For
flag = 0, we also have GEN ellwp(GEN w, GEN z, long prec), and GEN ellwpseries(GEN E,

long v, long precdl) for the power series in variable v.

3.5.49 ellzeta(w, {z =′ x}). Computes the value at z of the Weierstrass ζ function attached to the lattice w as given by ellperiods(, 1): including quasi-periods is useful, otherwise there are recomputed from scratch for each new z.

ζ(z, L) = \frac{1}{z} + z^{2} ∑_{ω∈L^{∗}} \frac{1}{ω^{2}(z − ω)}.

It is also possible to directly input w = [ω_{1}, ω_{2}], or an elliptic curve E as given by ellinit (w = E.omega). The quasi-periods of ζ, such that

ζ(z + aω_{1} + bω_{2}) = ζ(z) + aη_{1} + bη_{2}

for integers a and b are obtained as η_{i} = 2ζ(ω_{i}/2). Or using directly elleta.

? w = ellperiods([1,I],1);
? ellzeta(w, 1/2)
%2 = 1.5707963267948966192313216916397514421
? E = ellinit([1,0]);
? ellzeta(E, E.omega[1]/2)
%4 = 0.84721308479397908660649912348219163647

One can also compute the series expansion around z = 0 (the quasi-periods are useless in this case):

? E = ellinit([0,1]);
? ellzeta(E) \\ at 'x, implicitly at default seriesprecision
%4 = x^-1 + 1/35*x^5 - 1/7007*x^11 + O(x^15)
? ellzeta(E, x + O(x^20)) \\ explicit precision
%5 = x^-1 + 1/35*x^5 - 1/7007*x^11 + 1/1440257*x^17 + O(x^18)
The library syntax is GEN ellzeta(GEN w, GEN z = NULL, long prec).

3.5.50 ellztopoint(E, z). E being an ell as output by ellinit, computes the coordinates [x, y] on the curve E corresponding to the complex number z. Hence this is the inverse function of ellpointtoz. In other words, if the curve is put in Weierstrass form y^{2} = 4x^{3} − g_{2}x − g_{3}, [x, y] represents the Weierstrass ℘-function and its derivative. More precisely, we have

x = ℘(z) − b_{2}/12, y = ℘′(z) − (a_{1}x + a_{3})/2.

If z is in the lattice defining E over C, the result is the point at infinity [0].

    The library syntax is GEN pointell(GEN E, GEN z, long prec).

3.5.51 genus2red(Q, P, {p}). Let Q, P be polynomials with integer coefficients. Determines the reduction at p > 2 of the (proper, smooth) genus 2 curve C/Q, defined by the hyperelliptic equation y^{2} + Qy = P. (The special fiber X_{p} of the minimal regular model X of C over Z.) If p is omitted, determines the reduction type for all (odd) prime divisors of the discriminant.

This function rewritten from an implementation of Liu’s algorithm by Cohen and Liu (1994),

genus2reduction-0.3, see http://www.math.u-bordeaux1.fr/~liu/G2R/.

CAVEAT. The function interface may change: for the time being, it returns [N, FaN , T, V] where N is either the local conductor at p or the global conductor, FaN is its factorization, y^{2} = T defines a minimal model over Z[1/2] and V describes the reduction type at the various considered p. Unfortunately, the program is not complete for p = 2, and we may return the odd part of the conductor only: this is the case if the factorization includes the (impossible) term 2^{−1}; if the factorization contains another power of 2, then this is the exact local conductor at 2 and N is the global conductor.

    ? default(debuglevel, 1);
    ? genus2red(0,x^6 + 3*x^3 + 63, 3)
    (potential) stable reduction: [1, []]
    reduction at p: [III{9}] page 184, [3, 3], f = 10
    %1 = [59049, Mat([3, 10]), x^6 + 3*x^3 + 63, [3, [1, []],
           ["[III{9}] page 184", [3, 3]]]]
    ? [N, FaN, T, V] = genus2red(x^3-x^2-1, x^2-x);  \\ X_1(13), global reduction
    p = 13
    (potential) stable reduction: [5, [Mod(0, 13), Mod(0, 13)]]
    reduction at p: [I{0}-II-0] page 159, [], f = 2
    ? N
    %3 = 169
    ? FaN
    %4 = Mat([13, 2])   \\ in particular, good reduction at 2 !
    ? T
    %5 = x^6 + 58*x^5 + 1401*x^4 + 18038*x^3 + 130546*x^2 + 503516*x + 808561
    ? V
    %6 = [[13, [5, [Mod(0, 13), Mod(0, 13)]], ["[I{0}-II-0] page 159", []]]]

We now first describe the format of the vector V = V_{p} in the case where p was specified (local reduction at p): it is a triple [p, stable, red]. The component stable = [type, vecj ] contains information about the stable reduction after a field extension; depending on types, the stable reduction is

• 1: smooth (i.e. the curve has potentially good reduction). The Jacobian J(C) has potentially good reduction.

• 2: an elliptic curve E with an ordinary double point; vecj contains j mod p, the modular invariant of E. The (potential) semi-abelian reduction of J(C) is the extension of an elliptic curve (with modular invariant j mod p) by a torus.

• 3: a projective line with two ordinary double points. The Jacobian J(C) has potentially multiplicative reduction.

• 4: the union of two projective lines crossing transversally at three points. The Jacobian J(C) has potentially multiplicative reduction.

• 5: the union of two elliptic curves E_{1} and E_{2} intersecting transversally at one point; vecj contains their modular invariants j_{1} and j_{2}, which may live in a quadratic extension of F_{p} are need not be distinct. The Jacobian J(C) has potentially good reduction, isomorphic to the product of the reductions of E_{1} and E_{2}.

• 6: the union of an elliptic curve E and a projective line which has an ordinary double point, and these two components intersect transversally at one point; vecj contains j mod p, the modular invariant of E. The (potential) semi-abelian reduction of J(C) is the extension of an elliptic curve (with modular invariant j mod p) by a torus.

• 7: as in type 6, but the two components are both singular. The Jacobian J(C) has potentially multiplicative reduction.

The component red = [NUtype, neron] contains two data concerning the reduction at p without any ramified field extension.

The NUtype is a t_STR describing the reduction at p of C, following Namikawa-Ueno, The complete classification of fibers in pencils of curves of genus two, Manuscripta Math., vol. 9, (1973), pages 143-186. The reduction symbol is followed by the corresponding page number in this article.

The second datum neron is the group of connected components (over an algebraic closure of F_{p}) of the Néron model of J(C), given as a finite abelian group (vector of elementary divisors).

If p = 2, the red component may be omitted altogether (and replaced by [], in the case where the program could not compute it. When p was not specified, V is the vector of all V_{p}, for all considered p.

Notes about Namikawa-Ueno types.

• A lower index is denoted between braces: for instance, [I{2}-II-5] means [I 2-II-5].

• If K and K′ are Kodaira symbols for singular fibers of elliptic curves, [K-K′-m] and [K′- K-m] are the same.

• [K-K′-−1] is [K′-K-α] in the notation of Namikawa-Ueno.

• The figure [2I 0-m] in Namikawa-Ueno, page 159, must be denoted by [2I 0-(m+1)].

    The library syntax is GEN genus2red(GEN Q, GEN P, GEN p = NULL).

3.6 Functions related to general number fields.

In this section can be found functions which are used almost exclusively for working in general number fields. Other less specific functions can be found in the next section on polynomials. Functions related to quadratic number fields are found in section Section 3.4 (Arithmetic functions).

3.6.1 Number field structures.

Let K = Q[X]/(T) a number field, Z_{K} its ring of integers, T ∈ Z[X] is monic. Three basic number field structures can be associated to K in GP:

• nf denotes a number field, i.e. a data structure output by nfinit. This contains the basic arithmetic data associated to the number field: signature, maximal order (given by a basis nf.zk), discriminant, defining polynomial T, etc.

• bnf denotes a “Buchmann’s number field”, i.e. a data structure output by bnfinit. This contains nf and the deeper invariants of the field: units U(K), class group Cl(K), as well as technical data required to solve the two associated discrete logarithm problems.

• bnr denotes a “ray number field”, i.e. a data structure output by bnrinit, corresponding to the ray class group structure of the field, for some modulus f. It contains a bnf , the modulus f, the ray class group Cl_{f}(K) and data associated to the discrete logarithm problem therein.

3.6.2 Algebraic numbers and ideals.

An algebraic number belonging to K = Q[X]/(T) is given as

• a t_INT, t_FRAC or t_POL (implicitly modulo T), or

• a t_POLMOD (modulo T), or

• a t_COL v of dimension N = [K : Q], representing the element in terms of the computed integral basis, as sum(i = 1, N, v[i] * nf.zk[i]). Note that a t_VEC will not be recognized.

An ideal is given in any of the following ways:

• an algebraic number in one of the above forms, defining a principal ideal.

• a prime ideal, i.e. a 5-component vector in the format output by idealprimedec or ideal-

factor.

• a t_MAT, square and in Hermite Normal Form (or at least upper triangular with non-negative coefficients), whose columns represent a Z-basis of the ideal.

One may use idealhnf to convert any ideal to the last (preferred) format.

• an extended ideal is a 2-component vector [I, t], where I is an ideal as above and t is an algebraic number, representing the ideal (t)I. This is useful whenever idealred is involved, implicitly working in the ideal class group, while keeping track of principal ideals. Ideal operations suitably update the principal part when it makes sense (in a multiplicative context), e.g. using idealmul on [I, t], [J, u], we obtain [IJ, tu]. When it does not make sense, the extended part is silently discarded, e.g. using idealadd with the above input produces I + J.

The “principal part” t in an extended ideal may be represented in any of the above forms, and also as a factorization matrix (in terms of number field elements, not ideals!), possibly the empty matrix [;] representing 1. In the latter case, elements stay in factored form, or famat for factorization matrix, which is a convenient way to avoid coefficient explosion. To recover the conventional expanded form, try nffactorback; but many functions already accept famats as input, for instance ideallog, so expanding huge elements should never be necessary.

3.6.3 Finite abelian groups.

A finite abelian group G in user-readable format is given by its Smith Normal Form as a pair [h, d] or triple [h, d, g]. Here h is the cardinality of G, (d_{i}) is the vector of elementary divisors, and (g_{i}) is a vector of generators. In short, G = ⊕_{i≤n}(Z/d_{i}Z)g_{i}, with d_{n} | … | d_{2} | d_{1} and ∏ d_{i} = h. This information can also be retrieved as G.no, G.cyc and G.gen.

• a character on the abelian group ⊕(Z/d_{i}Z)g_{i} is given by a row vector χ = [a_{1}, …, a_{n}] such that χ(∏ g_{i}^{n_{i}}) = exp(2iπ ∑ a_{i}n_{i}/d_{i}).

• given such a structure, a subgroup H is input as a square matrix in HNF, whose columns express generators of H on the given generators g_{i}. Note that the determinant of that matrix is equal to the index (G : H).

3.6.4 Relative extensions.

We now have a look at data structures associated to relative extensions of number fields L/K, and to projective Z_{K}-modules. When defining a relative extension L/K, the nf associated to the base field K must be defined by a variable having a lower priority (see Section 2.5.3) than the variable defining the extension. For example, you may use the variable name y to define the base field K, and x to define the relative extension L/K.

3.6.4.1 Basic definitions.

• rnf denotes a relative number field, i.e. a data structure output by rnfinit, associated to the extension L/K. The nf associated to be base field K is rnf.nf.

• A relative matrix is an m × n matrix whose entries are elements of K, in any form. Its m columns A_{j} represent elements in K^{n}.

• An ideal list is a row vector of fractional ideals of the number field nf .

• A pseudo-matrix is a 2-component row vector (A, I) where A is a relative m × n matrix and I an ideal list of length n. If I = {a_{1}, …, a_{n}} and the columns of A are (A_{1}, …, A_{n}), this data defines the torsion-free (projective) Z_{K}-module a_{1}A_{1} ⊕ a_{n}A_{n}.

• An integral pseudo-matrix is a 3-component row vector w(A, I, J) where A = (a_{i,j}) is an m × n relative matrix and I = (b_{1}, …, b_{m}), J = (a_{1}, …, a_{n}) are ideal lists, such that a_{i,j} ∈ b_{i}a_{j}^{−1} for all i, j. This data defines two abstract projective Z_{K}-modules N = a_{1}ω_{1} ⊕ ⋯ ⊕ a_{n}ω_{n} in K^{n}, P = b_{1}η_{1} ⊕ ⋯ ⊕ b_{m}η_{m} in K^{m}, and a Z_{K}-linear map f : N → P given by

f(∑ α_{j}ω_{j}) = ∑_{i} (a_{i,j}α_{j})η_{i}.

This data defines the Z_{K}-module M = P/f(N).

• Any projective Z_{K}-module M of finite type in K^{m} can be given by a pseudo matrix (A, I).

• An arbitrary Z_{K} modules of finite type in K^{m}, with non-trivial torsion, is given by an integral pseudo-matrix (A, I, J)

3.6.4.2 Pseudo-bases, determinant.

• The pair (A, I) is a pseudo-basis of the module it generates if the a_{j} are non-zero, and the A_{j} are K-linearly independent. We call n the size of the pseudo-basis. If A is a relative matrix, the latter condition means it is square with non-zero determinant; we say that it is in Hermite Normal Form (HNF) if it is upper triangular and all the elements of the diagonal are equal to 1.

• For instance, the relative integer basis rnf.zk is a pseudo-basis (A, I) of Z_{L}, where A = rnf.zk[1] is a vector of elements of L, which are K-linearly independent. Most rnf routines return and handle Z_{K}-modules contained in L (e.g. Z_{L}-ideals) via a pseudo-basis (A′, I′), where A′ is a relative matrix representing a vector of elements of L in terms of the fixed basis rnf.zk[1]

• The determinant of a pseudo-basis (A, I) is the ideal equal to the product of the determinant of A by all the ideals of I. The determinant of a pseudo-matrix is the determinant of any pseudobasis of the module it generates.

3.6.5 Class field theory.

A modulus, in the sense of class field theory, is a divisor supported on the non-complex places of K. In PARI terms, this means either an ordinary ideal I as above (no Archimedean component), or a pair [I, a], where a is a vector with r_{1} {0, 1}-components, corresponding to the infinite part of the divisor. More precisely, the i-th component of a corresponds to the real embedding associated to the i-th real root of K.roots. (That ordering is not canonical, but well defined once a defining polynomial for K is chosen.) For instance, [1, [1,1]] is a modulus for a real quadratic field, allowing ramification at any of the two places at infinity, and nowhere else.

A bid or “big ideal” is a structure output by idealstar needed to compute in (Z_{K}/I)^{∗}, where I is a modulus in the above sense. It is a finite abelian group as described above, supplemented by technical data needed to solve discrete log problems.

Finally we explain how to input ray number fields (or bnr), using class field theory. These are defined by a triple A, B, C, where the defining set [A, B, C] can have any of the following forms: [bnr], [bnr, subgroup], [bnf , mod], [bnf , mod, subgroup]. The last two forms are kept for backward compatibility, but no longer serve any real purpose (see example below); no newly written function will accept them.

• bnf is as output by bnfinit, where units are mandatory unless the modulus is trivial; bnr is as output by bnrinit. This is the ground field K.

• mod is a modulus 𝔣, as described above.

• subgroup a subgroup of the ray class group modulo 𝔣 of K. As described above, this is input as a square matrix expressing generators of a subgroup of the ray class group bnr.clgp on the given generators.

The corresponding bnr is the subfield of the ray class field of K modulo 𝔣, fixed by the given subgroup.

      ? K = bnfinit(y^2+1);
      ? bnr = bnrinit(K, 13)
      ? %.clgp
      %3 = [36, [12, 3]]
      ? bnrdisc(bnr); \\ discriminant of the full ray class field
      ? bnrdisc(bnr, [3,1;0,1]); \\ discriminant of cyclic cubic extension of K

We could have written directly

      ? bnrdisc(K, 13);
      ? bnrdisc(K, 13, [3,1;0,1]);

avoiding one bnrinit, but this would actually be slower since the bnrinit is called internally anyway. And now twice!

3.6.6 General use.

All the functions which are specific to relative extensions, number fields, Buchmann’s number fields, Buchmann’s number rays, share the prefix rnf, nf, bnf, bnr respectively. They take as first argument a number field of that precise type, respectively output by rnfinit, nfinit, bnfinit,

and bnrinit.

However, and even though it may not be specified in the descriptions of the functions below, it is permissible, if the function expects a nf , to use a bnf instead, which contains much more information. On the other hand, if the function requires a bnf, it will not launch bnfinit for you, which is a costly operation. Instead, it will give you a specific error message. In short, the types

                                  nf ≤ bnf ≤ bnr

are ordered, each function requires a minimal type to work properly, but you may always substitute a larger type.

The data types corresponding to the structures described above are rather complicated. Thus, as we already have seen it with elliptic curves, GP provides “member functions” to retrieve data from these structures (once they have been initialized of course). The relevant types of number fields are indicated between parentheses:

bid  (bnr  ) : bid ideal structure.
bnf  (bnr, bnf  ) : Buchmann’s number field.
clgp (bnr, bnf  ) : classgroup. This one admits the following three subclasses:
cyc  : cyclic decomposition (SNF).
gen  : generators.
no  : number of elements.
diff (bnr, bnf , nf ) : the different ideal.
codiff (bnr, bnf , nf ) : the codifferent (inverse of the different in the ideal group).
disc (bnr, bnf , nf ) : discriminant.
fu (bnr, bnf ) : fundamental units.
index (bnr, bnf , nf ) : index of the power order in the ring of integers.
mod (bnr ) : modulus.
nf  (bnr, bnf , nf ) : number field.
pol  (bnr, bnf , nf ) : defining polynomial.
r1  (bnr, bnf , nf ) : the number of real embeddings.
r2  (bnr, bnf , nf ) : the number of pairs of complex embeddings.
reg (bnr, bnf ) : regulator.
roots (bnr, bnf , nf ) : roots of the polynomial generating the field.
sign (bnr, bnf , nf ) : signature [r1, r2].
t2  (bnr, bnf , nf ) : the T_{2} matrix (see nfinit).
tu (bnr, bnf ) : a generator for the torsion units.
zk  (bnr, bnf , nf ) : integral basis, i.e. a Z-basis of the maximal order.
zkst (bnr  ) : structure of (Z_{K}/m)^{∗}.

Deprecated. The following member functions are still available, but deprecated and should not be used in new scripts :

futu (bnr, bnf , ) : [u_{1}, ..., u_{r}, w], (u_{i}) is a vector of fundamental units,

w generates the torsion units.

tufu (bnr, bnf , ) : [w, u_{1}, ..., u_{r}], (u_{i}) is a vector of fundamental units,

w generates the torsion units.

For instance, assume that bnf = bnfinit(pol), for some polynomial. Then bnf .clgp retrieves the class group, and bnf .clgp.no the class number. If we had set bnf = nfinit(pol), both would have output an error message. All these functions are completely recursive, thus for instance bnr.bnf.nf.zk will yield the maximal order of bnr, which you could get directly with a simple bnr.zk.

3.6.7 Class group, units, and the GRH.

Some of the functions starting with bnf are implementations of the sub-exponential algorithms for finding class and unit groups under GRH, due to Hafner-McCurley, Buchmann and Cohen-Diaz-Olivier. The general call to the functions concerning class groups of general number fields (i.e. excluding quadclassunit) involves a polynomial P and a technical vector

tech = [c_{1}, c_{2}, nrpid],

where the parameters are to be understood as follows:

P is the defining polynomial for the number field, which must be in Z[X], irreducible and monic. In fact, if you supply a non-monic polynomial at this point, gp issues a warning, then transforms your polynomial so that it becomes monic. The nfinit routine will return a different result in this case: instead of res, you get a vector [res,Mod(a,Q)], where Mod(a,Q) = Mod(X,P) gives the change of variables. In all other routines, the variable change is simply lost.

The tech interface is obsolete and you should not tamper with these parameters. Indeed, from version 2.4.0 on,

• the results are always rigorous under GRH (before that version, they relied on a heuristic strengthening, hence the need for overrides).

• the influence of these parameters on execution time and stack size is marginal. They can be useful to fine-tune and experiment with the bnfinit code, but you will be better off modifying all tuning parameters in the C code (there are many more than just those three). We nevertheless describe it for completeness.

The numbers c_{1} ≤ c_{2} are non-negative real numbers. By default they are chosen so that the result is correct under GRH. For i = 1, 2, let B_{i} = c_{i}(log |d_{K}|)^{2}, and denote by S(B) the set of maximal ideals of K whose norm is less than B. We want S(B_{1}) to generate Cl(K) and hope that S(B_{2}) can be proven to generate Cl(K).

More precisely, S(B_{1}) is a factorbase used to compute a tentative Cl(K) by generators and relations. We then check explicitly, using essentially bnfisprincipal, that the elements of S(B_{2}) belong to the span of S(B_{1}). Under the assumption that S(B_{2}) generates Cl(K), we are done. User-supplied c_{i} are only used to compute initial guesses for the bounds B_{i}, and the algorithm increases them until one can prove under GRH that S(B_{2}) generates Cl(K). A uniform result of Bach says that c_{2} = 12 is always suitable, but this bound is very pessimistic and a direct algorithm due to Belabas-Diaz-Friedman is used to check the condition, assuming GRH. The default values are c_{1} = c_{2} = 0. When c_{1} is equal to 0 the algorithm takes it equal to c_{2}.

nrpid is the maximal number of small norm relations associated to each ideal in the factor base. Set it to 0 to disable the search for small norm relations. Otherwise, reasonable values are between 4 and 20. The default is 4.

Warning. Make sure you understand the above! By default, most of the bnf routines depend on the correctness of the GRH. In particular, any of the class number, class group structure, class group generators, regulator and fundamental units may be wrong, independently of each other. Any result computed from such a bnf may be wrong. The only guarantee is that the units given generate a subgroup of finite index in the full unit group. You must use bnfcertify to certify the computations unconditionally.

Remarks.

You do not need to supply the technical parameters (under the library you still need to send at least an empty vector, coded as NULL). However, should you choose to set some of them, they must be given in the requested order. For example, if you want to specify a given value of nrpid, you must give some values as well for c_{1} and c_{2}, and provide a vector [c_{1}, c_{2}, nrpid].

Note also that you can use an nf instead of P, which avoids recomputing the integral basis and analogous quantities.

3.6.8 bnfcertify(bnf , {flag = 0}). bnf being as output by bnfinit, checks whether the result is correct, i.e. whether it is possible to remove the assumption of the Generalized Riemann Hypothesis. It is correct if and only if the answer is 1. If it is incorrect, the program may output some error message, or loop indefinitely. You can check its progress by increasing the debug level. The bnf structure must contain the fundamental units:

    ? K = bnfinit(x^3+2^2^3+1); bnfcertify(K)
      ***   at top-level: K=bnfinit(x^3+2^2^3+1);bnfcertify(K)
      ***                                        ^-------------
      *** bnfcertify: missing units in bnf.
    ? K = bnfinit(x^3+2^2^3+1, 1); \\ include units
    ? bnfcertify(K)
    %3 = 1

If flag is present, only certify that the class group is a quotient of the one computed in bnf (much simpler in general); likewise, the computed units may form a subgroup of the full unit group. In this variant, the units are no longer needed:

    ? K = bnfinit(x^3+2^2^3+1); bnfcertify(K, 1)
    %4 = 1
    The library syntax is long bnfcertify0(GEN bnf, long flag ). Also available is GEN bn-
fcertify(GEN bnf) (flag = 0).

3.6.9 bnfcompress(bnf ). Computes a compressed version of bnf (from bnfinit), a “small Buchmann’s number field” (or sbnf for short) which contains enough information to recover a full bnf vector very rapidly, but which is much smaller and hence easy to store and print. Calling bnfinit on the result recovers a true bnf, in general different from the original. Note that an snbf is useless for almost all purposes besides storage, and must be converted back to bnf form before use; for instance, no nf*, bnf* or member function accepts them.

An sbnf is a 12 component vector v, as follows. Let bnf be the result of a full bnfinit, complete with units. Then v[1] is bnf.pol, v[2] is the number of real embeddings bnf.sign[1], v[3] is bnf.disc, v[4] is bnf.zk, v[5] is the list of roots bnf.roots, v[7] is the matrix W = bnf[1], v[8] is the matrix matalpha = bnf[2], v[9] is the prime ideal factor base bnf[5] coded in a compact way, and ordered according to the permutation bnf[6], v[10] is the 2-component vector giving the number of roots of unity and a generator, expressed on the integral basis, v[11] is the list of fundamental units, expressed on the integral basis, v[12] is a vector containing the algebraic numbers alpha corresponding to the columns of the matrix matalpha, expressed on the integral basis.

All the components are exact (integral or rational), except for the roots in v[5].

    The library syntax is GEN bnfcompress(GEN bnf).

3.6.10 bnfdecodemodule(nf , m). If m is a module as output in the first component of an extension given by bnrdisclist, outputs the true module.

    ? K = bnfinit(x^2+23); L = bnrdisclist(K, 10); s = L[1][2]
    %1 = [[Mat([8, 1]), [[0, 0, 0]]], [Mat([9, 1]), [[0, 0, 0]]]]
    ? bnfdecodemodule(K, s[1][1])
    %2 =
    [2 0]
    [0 1]
    The library syntax is GEN decodemodule(GEN nf, GEN m).

3.6.11 bnfinit(P, {flag = 0}, {tech = [ ]}). Initializes a bnf structure. Used in programs such as bnfisprincipal, bnfisunit or bnfnarrow. By default, the results are conditional on the GRH, see 3.6.7. The result is a 10-component vector bnf .

This implements Buchmann’s sub-exponential algorithm for computing the class group, the regulator and a system of fundamental units of the general algebraic number field K defined by the irreducible polynomial P with integer coefficients.

If the precision becomes insufficient, gp does not strive to compute the units by default (flag = 0).

When flag = 1, we insist on finding the fundamental units exactly. Be warned that this can take a very long time when the coefficients of the fundamental units on the integral basis are very large. If the fundamental units are simply too large to be represented in this form, an error message is issued. They could be obtained using the so-called compact representation of algebraic numbers as a formal product of algebraic integers. The latter is implemented internally but not publicly accessible yet.

tech is a technical vector (empty by default, see 3.6.7). Careful use of this parameter may speed up your computations, but it is mostly obsolete and you should leave it alone.

The components of a bnf or sbnf are technical and never used by the casual user. In fact: never access a component directly, always use a proper member function. However, for the sake of completeness and internal documentation, their description is as follows. We use the notations explained in the book by H. Cohen, A Course in Computational Algebraic Number Theory, Graduate Texts in Maths 138, Springer-Verlag, 1993, Section 6.5, and subsection 6.5.5 in particular.

bnf [1] contains the matrix W, i.e. the matrix in Hermite normal form giving relations for the class group on prime ideal generators (𝔭_{i})_{1≤i≤r}.

bnf [2] contains the matrix B, i.e. the matrix containing the expressions of the prime ideal factorbase in terms of the 𝔭_{i}. It is an r × c matrix.

bnf [3] contains the complex logarithmic embeddings of the system of fundamental units which has been found. It is an (r_{1} + r_{2}) × (r_{1} + r_{2} − 1) matrix.

bnf [4] contains the matrix M′′_{C} of Archimedean components of the relations of the matrix (W |B).

bnf [5] contains the prime factor base, i.e. the list of prime ideals used in finding the relations.

bnf [6] used to contain a permutation of the prime factor base, but has been obsoleted. It contains a dummy 0.

bnf [7] or bnf .nf is equal to the number field data nf as would be given by nfinit.

bnf [8] is a vector containing the classgroup bnf .clgp as a finite abelian group, the regulator bnf .reg, a 1 (used to contain an obsolete “check number”), the number of roots of unity and a generator bnf .tu, the fundamental units bnf .fu.

bnf [9] is a 3-element row vector used in bnfisprincipal only and obtained as follows. Let D = UW V obtained by applying the Smith normal form algorithm to the matrix W (= bnf [1]) and let U_{r} be the reduction of U modulo D. The first elements of the factorbase are given (in terms of bnf.gen) by the columns of U_{r}, with Archimedean component g_{a}; let also GD_{a} be the Archimedean components of the generators of the (principal) ideals defined by the bnf.gen[i]^ bnf.cyc[i]. Then bnf [9] = [U_{r}, g_{a}, GD_{a}].

bnf [10] is by default unused and set equal to 0. This field is used to store further information about the field as it becomes available, which is rarely needed, hence would be too expensive to compute during the initial bnfinit call. For instance, the generators of the principal ideals bnf.gen[i]^bnf.cyc[i] (during a call to bnrisprincipal), or those corresponding to the relations in W and B (when the bnf internal precision needs to be increased).

    The library syntax is GEN bnfinit0(GEN P, long flag, GEN tech = NULL, long prec).
    Also available is GEN Buchall(GEN P, long flag, long prec), corresponding to tech =

NULL, where flag is either 0 (default) or nf_FORCE (insist on finding fundamental units). The

function GEN Buchall_param(GEN P, double c1, double c2, long nrpid, long flag, long

prec) gives direct access to the technical parameters.

3.6.12 bnfisintnorm(bnf , x). Computes a complete system of solutions (modulo units of positive norm) of the absolute norm equation Norm(a) = x, where a is an integer in bnf . If bnf has not been certified, the correctness of the result depends on the validity of GRH.

    See also bnfisnorm.
    The library syntax is GEN bnfisintnorm(GEN bnf, GEN x). The function GEN bnfisint-

normabs(GEN bnf, GEN a) returns a complete system of solutions modulo units of the absolute norm equation |Norm(x)| = |a|. As fast as bnfisintnorm, but solves the two equations Norm(x) = ±a simultaneously.

3.6.13 bnfisnorm(bnf , x, {flag = 1}). Tries to tell whether the rational number x is the norm of some element y in bnf . Returns a vector [a, b] where x = Norm(a) ∗ b. Looks for a solution which is an S-unit, with S a certain set of prime ideals containing (among others) all primes dividing x. If bnf is known to be Galois, set flag = 0 (in this case, x is a norm iff b = 1). If flag is non zero the program adds to S the following prime ideals, depending on the sign of flag. If flag > 0, the ideals of norm less than flag. And if flag < 0 the ideals dividing flag.

Assuming GRH, the answer is guaranteed (i.e. x is a norm iff b = 1), if S contains all primes less than 12 log(disc(Bnf ))^{2}, where Bnf is the Galois closure of bnf .

    See also bnfisintnorm.
    The library syntax is GEN bnfisnorm(GEN bnf, GEN x, long flag).

3.6.14 bnfisprincipal(bnf , x, {flag = 1}). bnf being the number field data output by bnfinit, and x being an ideal, this function tests whether the ideal is principal or not. The result is more complete than a simple true/false answer and solves general discrete logarithm problem. Assume the class group is ⊕(Z/d_{i}Z)g_{i} (where the generators g_{i} and their orders d_{i} are respectively given by bnf.gen and bnf.cyc). The routine returns a row vector [e, t], where e is a vector of exponents 0 ≤ e_{i} < d_{i}, and t is a number field element such that

x = (t) ∏_{i} g_{i}^{e_{i}}.

For given g_{i} (i.e. for a given bnf), the e_{i} are unique, and t is unique modulo units.

In particular, x is principal if and only if e is the zero vector. Note that the empty vector, which is returned when the class number is 1, is considered to be a zero vector (of dimension 0).

    ? K = bnfinit(y^2+23);
    ? K.cyc
    %2 = [3]
    ? K.gen
    %3 = [[2, 0; 0, 1]]          \\ a prime ideal above 2
    ? P = idealprimedec(K,3)[1]; \\ a prime ideal above 3
    ? v = bnfisprincipal(K, P)
    %5 = [[2]~, [3/4, 1/4]~]
    ? idealmul(K, v[2], idealfactorback(K, K.gen, v[1]))
    %6 =
    [3 0]
    [0 1]

    ? % == idealhnf(K, P)
    %7 = 1

The binary digits of flagmean:

• 1: If set, outputs [e, t] as explained above, otherwise returns only e, which is much easier to compute. The following idiom only tests whether an ideal is principal:

      is_principal(bnf, x) = !bnfisprincipal(bnf,x,0);

• 2: It may not be possible to recover t, given the initial accuracy to which bnf was computed. In that case, a warning is printed and t is set equal to the empty vector []~. If this bit is set, increase the precision and recompute needed quantities until t can be computed. Warning: setting this may induce very lengthy computations.

    The library syntax is GEN bnfisprincipal0(GEN bnf, GEN x, long flag). Instead of the

above hardcoded numerical flags, one should rather use an or-ed combination of the symbolic flags nf_GEN (include generators, possibly a place holder if too difficult) and nf_FORCE (insist on finding the generators).

3.6.15 bnfissunit(bnf , sfu, x). bnf being output by bnfinit, sfu by bnfsunit, gives the column vector of exponents of x on the fundamental S-units and the roots of unity. If x is not a unit, outputs an empty vector.

    The library syntax is GEN bnfissunit(GEN bnf, GEN sfu, GEN x).

3.6.16 bnfisunit(bnf , x). bnf being the number field data output by bnfinit and x being an algebraic number (type integer, rational or polmod), this outputs the decomposition of x on the fundamental units and the roots of unity if x is a unit, the empty vector otherwise. More precisely, if u_{1},…,u_{r} are the fundamental units, and ζ is the generator of the group of roots of unity (bnf.tu), the output is a vector [x_{1}, …, x_{r}, x_{r+1}] such that x = u_{1}^{x_{1}} ⋯ u_{r}^{x_{r}} · ζ^{x_{r+1}}. The x_{i} are integers for i ≤ r and is an integer modulo the order of ζ for i = r + 1.

Note that bnf need not contain the fundamental unit explicitly:

    ? setrand(1); bnf = bnfinit(x^2-x-100000);
    ? bnf.fu
      ***   at top-level: bnf.fu
      ***                     ^--
      *** _.fu: missing units in .fu.
    ? u = [119836165644250789990462835950022871665178127611316131167, \
           379554884019013781006303254896369154068336082609238336]~;
    ? bnfisunit(bnf, u)
    %3 = [-1, Mod(0, 2)]~

The given u is the inverse of the fundamental unit implicitly stored in bnf . In this case, the fundamental unit was not computed and stored in algebraic form since the default accuracy was too low. (Re-run the command at “g1 or higher to see such diagnostics.)

    The library syntax is GEN bnfisunit(GEN bnf, GEN x).

3.6.17 bnfnarrow(bnf ). bnf being as output by bnfinit, computes the narrow class group of bnf . The output is a 3-component row vector v analogous to the corresponding class group component bnf .clgp (bnf [8][1]): the first component is the narrow class number v.no, the second component is a vector containing the SNF cyclic components v.cyc of the narrow class group, and the third is a vector giving the generators of the corresponding v.gen cyclic groups. Note that this function is a special case of bnrinit.

The library syntax is GEN buchnarrow(GEN bnf).

3.6.18 bnfsignunit(bnf ). bnf being as output by bnfinit, this computes an r_{1} × (r_{1} + r_{2} − 1) matrix having ±1 components, giving the signs of the real embeddings of the fundamental units. The following functions compute generators for the totally positive units:

/* exponents of totally positive units generators on bnf.tufu */
tpuexpo(bnf)=
{ my(S,d,K);
  S = bnfsignunit(bnf); d = matsize(S);
  S = matrix(d[1],d[2], i,j, if (S[i,j] < 0, 1,0));
  S = concat(vectorv(d[1],i,1), S);   \\ add sign(-1)
  K = lift(matker(S * Mod(1,2)));
  if (K, mathnfmodid(K, 2), 2*matid(d[1]))
}
/* totally positive units */
tpu(bnf)=
{ my(vu = bnf.tufu, ex = tpuexpo(bnf));
  vector(#ex-1, i, factorback(vu, ex[,i+1]))  \\ ex[,1] is 1
}
The library syntax is GEN signunits(GEN bnf).

3.6.19 bnfsunit(bnf , S). Computes the fundamental S-units of the number field bnf (output by bnfinit), where S is a list of prime ideals (output by idealprimedec). The output is a vector v with 6 components.

v[1] gives a minimal system of (integral) generators of the S-unit group modulo the unit group.

v[2] contains technical data needed by bnfissunit.

v[3] is an empty vector (used to give the logarithmic embeddings of the generators in v[1] in version 2.0.16).

v[4] is the S-regulator (this is the product of the regulator, the determinant of v[2] and the natural logarithms of the norms of the ideals in S).

v[5] gives the S-class group structure, in the usual format (a row vector whose three components give in order the S-class number, the cyclic components and the generators).

v[6] is a copy of S.

The library syntax is GEN bnfsunit(GEN bnf, GEN S, long prec).

3.6.20 bnrL1(bnr, {H}, {flag = 0}). Let bnr be the number field data output by bnrinit(,,1) and H be a square matrix defining a congruence subgroup of the ray class group corresponding to bnr (the trivial congruence subgroup if omitted). This function returns, for each character χ of the ray class group which is trivial on H, the value at s = 1 (or s = 0) of the abelian L-function associated to χ. For the value at s = 0, the function returns in fact for each χ a vector [r_{χ}, c_{χ}] where

L(s, χ) = c · s^{r} + O(s^{r+1})

near 0.

The argument flag is optional, its binary digits mean 1: compute at s = 0 if unset or s = 1 if set, 2: compute the primitive L-function associated to χ if unset or the L-function with Euler factors at prime ideals dividing the modulus of bnr removed if set (that is L_{S}(s, χ), where S is the set of infinite places of the number field together with the finite prime ideals dividing the modulus of bnr), 3: return also the character if set.

    K = bnfinit(x^2-229);
    bnr = bnrinit(K,1,1);
    bnrL1(bnr)

returns the order and the first non-zero term of L(s, χ) at s = 0 where χ runs through the characters of the class group of K = Q(\sqrt{229}). Then

    bnr2 = bnrinit(K,2,1);
    bnrL1(bnr2,,2)

returns the order and the first non-zero terms of L_{S}(s, χ) at s = 0 where χ runs through the characters of the class group of K and S is the set of infinite places of K together with the finite prime 2. Note that the ray class group modulo 2 is in fact the class group, so bnrL1(bnr2,0) returns the same answer as bnrL1(bnr,0).

This function will fail with the message

     *** bnrL1: overflow in zeta_get_N0 [need too many primes].

if the approximate functional equation requires us to sum too many terms (if the discriminant of K is too large).

    The library syntax is GEN bnrL1(GEN bnr, GEN H = NULL, long flag, long prec).

3.6.21 bnrclassno(A, {B}, {C}). Let A, B, C define a class field L over a ground field K (of type [bnr], [bnr, subgroup], or [bnf , modulus], or [bnf , modulus,subgroup], Section 3.6.5); this function returns the relative degree [L : K].

In particular if A is a bnf (with units), and B a modulus, this function returns the corresponding ray class number modulo B. One can input the associated bid (with generators if the subgroup C is non trivial) for B instead of the module itself, saving some time.

This function is faster than bnrinit and should be used if only the ray class number is desired. See bnrclassnolist if you need ray class numbers for all moduli less than some bound.

    The library syntax is GEN bnrclassno0(GEN A, GEN B = NULL, GEN C = NULL). Also avail-

able is GEN bnrclassno(GEN bnf,GEN f) to compute the ray class number modulo f.

3.6.22 bnrclassnolist(bnf , list). bnf being as output by bnfinit, and list being a list of moduli (with units) as output by ideallist or ideallistarch, outputs the list of the class numbers of the corresponding ray class groups. To compute a single class number, bnrclassno is more efficient.

? bnf = bnfinit(x^2 - 2);
? L = ideallist(bnf, 100, 2);
? H = bnrclassnolist(bnf, L);
? H[98]
%4 = [1, 3, 1]
? l = L[1][98]; ids = vector(#l, i, l[i].mod[1])
%5 = [[98, 88; 0, 1], [14, 0; 0, 7], [98, 10; 0, 1]]

The weird l[i].mod[1], is the first component of l[i].mod, i.e. the finite part of the conductor. (This is cosmetic: since by construction the Archimedean part is trivial, I do not want to see it). This tells us that the ray class groups modulo the ideals of norm 98 (printed as %5) have respectively order 1, 3 and 1. Indeed, we may check directly:

? bnrclassno(bnf, ids[2])
%6 = 3
The library syntax is GEN bnrclassnolist(GEN bnf, GEN list).

3.6.23 bnrconductor(A, {B}, {C}, {flag = 0}). Conductor f of the subfield of a ray class field as defined by [A, B, C] (of type [bnr], [bnr, subgroup], [bnf , modulus] or [bnf , modulus, subgroup], Section 3.6.5)

If flag = 0, returns f.

If flag = 1, returns [f, Cl_{f}, H], where Cl_{f} is the ray class group modulo f, as a finite abelian group; finally H is the subgroup of Cl_{f} defining the extension.

If flag = 2, returns [f, bnr(f), H], as above except Cl_{f} is replaced by a bnr structure, as output by bnrinit(, f, 1).

The library syntax is GEN bnrconductor0(GEN A, GEN B = NULL, GEN C = NULL, long
flag).
Also available is GEN bnrconductor(GEN bnr, GEN H, long flag)

3.6.24 bnrconductorofchar(bnr, chi). bnr being a big ray number field as output by bnrinit, and chi being a row vector representing a character as expressed on the generators of the ray class group, gives the conductor of this character as a modulus.

The library syntax is GEN bnrconductorofchar(GEN bnr, GEN chi).

3.6.25 bnrdisc(A, {B}, {C}, {flag = 0}). A, B, C defining a class field L over a ground field K (of type [bnr], [bnr, subgroup], [bnf , modulus] or [bnf , modulus, subgroup], Section 3.6.5), outputs data [N, r_{1}, D] giving the discriminant and signature of L, depending on the binary digits of flag:

• 1: if this bit is unset, output absolute data related to L/Q: N is the absolute degree [L : Q], r_{1} the number of real places of L, and D the discriminant of L/Q. Otherwise, output relative data for L/K: N is the relative degree [L : K], r_{1} is the number of real places of K unramified in L (so that the number of real places of L is equal to r_{1} times N), and D is the relative discriminant ideal of L/K.

• 2: if this bit is set and if the modulus is not the conductor of L, only return 0.

    The library syntax is GEN bnrdisc0(GEN A, GEN B = NULL, GEN C = NULL, long flag).

3.6.26 bnrdisclist(bnf , bound, {arch}). bnf being as output by bnfinit (with units), computes a list of discriminants of Abelian extensions of the number field by increasing modulus norm up to bound bound. The ramified Archimedean places are given by arch; all possible values are taken if arch is omitted.

The alternative syntax bnrdisclist(bnf , list) is supported, where list is as output by ideallist or ideallistarch (with units), in which case arch is disregarded.

The output v is a vector of vectors, where v[i][j] is understood to be in fact V [2^{15}(i − 1) + j] of a unique big vector V. (This awkward scheme allows for larger vectors than could be otherwise represented.)

V [k] is itself a vector W, whose length is the number of ideals of norm k. We consider first the case where arch was specified. Each component of W corresponds to an ideal m of norm k, and gives invariants associated to the ray class field L of bnf of conductor [m, arch]. Namely, each contains a vector [m, d, r, D] with the following meaning: m is the prime ideal factorization of the modulus, d = [L : Q] is the absolute degree of L, r is the number of real places of L, and D is the factorization of its absolute discriminant. We set d = r = D = 0 if m is not the finite part of a conductor.

If arch was omitted, all t = 2^{r_{1}} possible values are taken and a component of W has the form [m, [[d_{1}, r_{1}, D_{1}], …, [d_{t}, r_{t}, D_{t}]]], where m is the finite part of the conductor as above, and [d_{i}, r_{i}, D_{i}] are the invariants of the ray class field of conductor [m, v_{i}], where v_{i} is the i-th Archimedean component, ordered by inverse lexicographic order; so v_{1} = [0, …, 0], v_{2} = [1, 0 …, 0], etc. Again, we set d_{i} = r_{i} = D_{i} = 0 if [m, v_{i}] is not a conductor.

Finally, each prime ideal pr = [p, α, e, f, β] in the prime factorization m is coded as the integer p · n^{2} + (f − 1) · n + (j − 1), where n is the degree of the base field and j is such that

    pr = idealprimedec(nf ,p)[j].

m can be decoded using bnfdecodemodule.

Note that to compute such data for a single field, either bnrclassno or bnrdisc is more efficient.

    The library syntax is GEN bnrdisclist0(GEN bnf, GEN bound, GEN arch = NULL).

3.6.27 bnrinit(bnf , f, {flag = 0}). bnf is as output by bnfinit, f is a modulus, initializes data linked to the ray class group structure corresponding to this module, a so-called bnr structure. One can input the associated bid with generators for f instead of the module itself, saving some time. (As in idealstar, the finite part of the conductor may be given by a factorization into prime ideals, as produced by idealfactor.)

The following member functions are available on the result: .bnf is the underlying bnf , .mod the modulus, .bid the bid structure associated to the modulus; finally, .clgp, .no, .cyc, .gen refer to the ray class group (as a finite abelian group), its cardinality, its elementary divisors, its generators (only computed if flag = 1).

The last group of functions are different from the members of the underlying bnf , which refer to the class group; use bnr.bnf.xxx to access these, e.g. bnr.bnf.cyc to get the cyclic decomposition of the class group.

They are also different from the members of the underlying bid, which refer to (Z_{K}/f)^{∗}; use bnr.bid.xxx to access these, e.g. bnr.bid.no to get ϕ(f).

If flag = 0 (default), the generators of the ray class group are not computed, which saves time. Hence bnr.gen would produce an error.

If flag = 1, as the default, except that generators are computed.

The library syntax is GEN bnrinit0(GEN bnf, GEN f, long flag). Instead the above hard-

coded numerical flags, one should rather use GEN Buchray(GEN bnf, GEN module, long flag)

where flag is an or-ed combination of nf GEN (include generators) and nf INIT (if omitted, return just the cardinal of the ray class group and its structure), possibly 0.

3.6.28 bnrisconductor(A, {B}, {C}). A, B, C represent an extension of the base field, given by class field theory (see Section 3.6.5). Outputs 1 if this modulus is the conductor, and 0 otherwise. This is slightly faster than bnrconductor.

    The library syntax is long bnrisconductor0(GEN A, GEN B = NULL, GEN C = NULL).

3.6.29 bnrisprincipal(bnr, x, {flag = 1}). bnr being the number field data which is output by bnrinit(, , 1) and x being an ideal in any form, outputs the components of x on the ray class group generators in a way similar to bnfisprincipal. That is a 2-component vector v where v[1] is the vector of components of x on the ray class group generators, v[2] gives on the integral basis an element α such that x = α ∏_{i} g_{i}^{x_{i}}.

If flag = 0, outputs only v_{1}. In that case, bnr need not contain the ray class group generators, i.e. it may be created with bnrinit(, , 0) If x is not coprime to the modulus of bnr the result is undefined.

    The library syntax is GEN bnrisprincipal(GEN bnr, GEN x, long flag). Instead of hard-

coded numerical flags, one should rather use GEN isprincipalray(GEN bnr, GEN x) for flag = 0, and if you want generators:

      bnrisprincipal(bnr, x, nf_GEN)

3.6.30 bnrrootnumber(bnr, chi, {flag = 0}). If χ = chi is a character over bnr, not necessarily primitive, let L(s, χ) = ∑_{id} χ(id)N(id)^{−s} be the associated Artin L-function. Returns the so-called Artin root number, i.e. the complex number W (χ) of modulus 1 such that

Λ(1 − s, χ) = W (χ)Λ(s, \overline{χ})

where Λ(s, χ) = A(χ)^{s/2}γ_{χ}(s)L(s, χ) is the enlarged L-function associated to L.

The generators of the ray class group are needed, and you can set flag = 1 if the character is known to be primitive. Example:

    bnf = bnfinit(x^2 - x - 57);
    bnr = bnrinit(bnf, [7,[1,1]], 1);
    bnrrootnumber(bnr, [2,1])

returns the root number of the character χ of Cl_{7∞_{1}∞_{2}}(Q(\sqrt{229})) defined by χ(g_{1}^{a}g_{2}^{b}) = ζ_{1}^{2a}ζ_{2}^{b}. Here g_{1}, g_{2} are the generators of the ray-class group given by bnr.gen and ζ_{1} = e^{2iπ/N_{1}}, ζ_{2} = e^{2iπ/N_{2}} where N_{1}, N_{2} are the orders of g_{1} and g_{2} respectively (N_{1} = 6 and N_{2} = 3 as bnr.cyc readily tells us).

    The library syntax is GEN bnrrootnumber(GEN bnr, GEN chi, long flag, long prec).

3.6.31 bnrstark(bnr, {subgroup}). bnr being as output by bnrinit(,,1), finds a relative equation for the class field corresponding to the modulus in bnr and the given congruence subgroup (as usual, omit subgroup if you want the whole ray class group).

The main variable of bnr must not be x, and the ground field and the class field must be totally real. When the base field is Q, the vastly simpler galoissubcyclo is used instead. Here is an example:

    bnf = bnfinit(y^2 - 3);
    bnr = bnrinit(bnf, 5, 1);
    bnrstark(bnr)

returns the ray class field of Q(\sqrt{3}) modulo 5. Usually, one wants to apply to the result one of

rnfpolredabs(bnf, pol, 16) \\ compute a reduced relative polynomial rnfpolredabs(bnf, pol, 16 + 2) \\ compute a reduced absolute polynomial

The routine uses Stark units and needs to find a suitable auxiliary conductor, which may not exist when the class field is not cyclic over the base. In this case bnrstark is allowed to return a vector of polynomials defining independent relative extensions, whose compositum is the requested class field. It was decided that it was more useful to keep the extra information thus made available, hence the user has to take the compositum herself.

Even if it exists, the auxiliary conductor may be so large that later computations become unfeasible. (And of course, Stark’s conjecture may simply be wrong.) In case of difficulties, try

rnfkummer:
    ? bnr = bnrinit(bnfinit(y^8-12*y^6+36*y^4-36*y^2+9,1), 2, 1);
    ? bnrstark(bnr)
      ***   at top-level: bnrstark(bnr)
      ***                 ^-------------
      *** bnrstark: need 3919350809720744 coefficients in initzeta.
      *** Computation impossible.

? lift( rnfkummer(bnr) )
time = 24 ms.
%2 = x^2 + (1/3*y^6 - 11/3*y^4 + 8*y^2 - 5)
The library syntax is GEN bnrstark(GEN bnr, GEN subgroup = NULL, long prec).

3.6.32 dirzetak(nf , b). Gives as a vector the first b coefficients of the Dedekind zeta function of the number field nf considered as a Dirichlet series.

The library syntax is GEN dirzetak(GEN nf, GEN b).

3.6.33 factornf(x, t). Factorization of the univariate polynomial x over the number field defined by the (univariate) polynomial t. x may have coefficients in Q or in the number field. The algorithm reduces to factorization over Q (Trager’s trick). The direct approach of nffactor, which uses van Hoeij’s method in a relative setting, is in general faster.

The main variable of t must be of lower priority than that of x (see Section 2.5.3). However if non-rational number field elements occur (as polmods or polynomials) as coefficients of x, the variable of these polmods must be the same as the main variable of t. For example

? factornf(x^2 + Mod(y, y^2+1), y^2+1);
? factornf(x^2 + y, y^2+1); \\ these two are OK
? factornf(x^2 + Mod(z,z^2+1), y^2+1)
  ***   at top-level: factornf(x^2+Mod(z,z
  ***                 ^--------------------
  *** factornf: inconsistent data in rnf function.
? factornf(x^2 + z, y^2+1)
  ***   at top-level: factornf(x^2+z,y^2+1
  ***                 ^--------------------
  *** factornf: incorrect variable in rnf function.
The library syntax is GEN polfnf(GEN x, GEN t).

3.6.34 galoisexport(gal, {flag}). gal being be a Galois group as output by galoisinit, export the underlying permutation group as a string suitable for (no flags or flag = 0) GAP or (flag = 1) Magma. The following example compute the index of the underlying abstract group in the GAP library:

? G = galoisinit(x^6+108);
? s = galoisexport(G)
%2 = "Group((1, 2, 3)(4, 5, 6), (1, 4)(2, 6)(3, 5))"
? extern("echo \"IdGroup("s");\" | gap -q")
%3 = [6, 1]
? galoisidentify(G)
%4 = [6, 1]

This command also accepts subgroups returned by galoissubgroups.

To import a GAP permutation into gp (for galoissubfields for instance), the following GAP function may be useful:

PermToGP := function(p, n)
  return Permuted([1..n],p);
end;

gap> p:= (1,26)(2,5)(3,17)(4,32)(6,9)(7,11)(8,24)(10,13)(12,15)(14,27)
  (16,22)(18,28)(19,20)(21,29)(23,31)(25,30)
gap> PermToGP(p,32);
[ 26, 5, 17, 32, 2, 9, 11, 24, 6, 13, 7, 15, 10, 27, 12, 22, 3, 28, 20, 19,
  29, 16, 31, 8, 30, 1, 14, 18, 21, 25, 23, 4 ]
The library syntax is GEN galoisexport(GEN gal, long flag).

3.6.35 galoisfixedfield(gal, perm, {flag}, {v = y}). gal being be a Galois group as output by galoisinit and perm an element of gal.group, a vector of such elements or a subgroup of gal as returned by galoissubgroups, computes the fixed field of gal by the automorphism defined by the permutations perm of the roots gal.roots. P is guaranteed to be squarefree modulo gal.p.

If no flags or flag = 0, output format is the same as for nfsubfield, returning [P, x] such that P is a polynomial defining the fixed field, and x is a root of P expressed as a polmod in gal.pol.

If flag = 1 return only the polynomial P.

If flag = 2 return [P, x, F] where P and x are as above and F is the factorization of gal.pol over the field defined by P, where variable v (y by default) stands for a root of P. The priority of v must be less than the priority of the variable of gal.pol (see Section 2.5.3). Example:

? G = galoisinit(x^4+1);
? galoisfixedfield(G,G.group[2],2)
%2 = [x^2 + 2, Mod(x^3 + x, x^4 + 1), [x^2 - y*x - 1, x^2 + y*x - 1]]

computes the factorization x^{4} + 1 = (x^{2} − \sqrt{−2}x − 1)(x^{2} + \sqrt{−2}x − 1)

The library syntax is GEN galoisfixedfield(GEN gal, GEN perm, long flag, long v =

-1), where v is a variable number.

3.6.36 galoisgetpol(a, {b}, {s}). Query the galpol package for a polynomial with Galois group isomorphic to GAP4(a,b), totally real if s = 1 (default) and totally complex if s = 2. The output is a vector [pol, den] where

• pol is the polynomial of degree a

• den is the denominator of nfgaloisconj(pol). Pass it as an optional argument to ga-

loisinit or nfgaloisconj to speed them up:
? [pol,den] = galoisgetpol(64,4,1);
? G = galoisinit(pol);
time = 352ms
? galoisinit(pol, den);  \\ passing 'den' speeds up the computation
time = 264ms
? % == %`
%4 = 1  \\ same answer

If b and s are omitted, return the number of isomorphism classes of groups of order a.

The library syntax is GEN galoisgetpol(long a, long b, long s). Also available is GEN

galoisnbpol(long a) when b and s are omitted.

3.6.37 galoisidentify(gal). gal being be a Galois group as output by galoisinit, output the isomorphism class of the underlying abstract group as a two-components vector [o, i], where o is the group order, and i is the group index in the GAP4 Small Group library, by Hans Ulrich Besche, Bettina Eick and Eamonn O’Brien.

This command also accepts subgroups returned by galoissubgroups.

The current implementation is limited to degree less or equal to 127. Some larger “easy” orders are also supported.

The output is similar to the output of the function IdGroup in GAP4. Note that GAP4 IdGroup handles all groups of order less than 2000 except 1024, so you can use galoisexport and GAP4 to identify large Galois groups.

The library syntax is GEN galoisidentify(GEN gal).

3.6.38 galoisinit(pol, {den}). Computes the Galois group and all necessary information for computing the fixed fields of the Galois extension K/Q where K is the number field defined by pol (monic irreducible polynomial in Z[X] or a number field as output by nfinit). The extension K/Q must be Galois with Galois group “weakly” super-solvable, see below; returns 0 otherwise. Hence this permits to quickly check whether a polynomial of order strictly less than 36 is Galois or not.

The algorithm used is an improved version of the paper “An efficient algorithm for the computation of Galois automorphisms”, Bill Allombert, Math. Comp, vol. 73, 245, 2001, pp. 359–375.

A group G is said to be “weakly” super-solvable if there exists a normal series

{1} = H_{0} ◁ H_{1} ◁ ⋯ ◁ H_{n−1} ◁ H_{n}

such that each H_{i} is normal in G and for i < n, each quotient group H_{i+1}/H_{i} is cyclic, and either H_{n} = G (then G is super-solvable) or G/H_{n} is isomorphic to either A_{4} or S_{4}.

In practice, almost all small groups are WKSS, the exceptions having order 36(1 exception), 48(2), 56(1), 60(1), 72(5), 75(1), 80(1), 96(10) and ≥ 108.

This function is a prerequisite for most of the galoisxxx routines. For instance:

P = x^6 + 108;
G = galoisinit(P);
L = galoissubgroups(G);
vector(#L, i, galoisisabelian(L[i],1))
vector(#L, i, galoisidentify(L[i]))

The output is an 8-component vector gal.

gal[1] contains the polynomial pol (gal.pol).

gal[2] is a three-components vector [p, e, q] where p is a prime number (gal.p) such that pol totally split modulo p, e is an integer and q = p^{e} (gal.mod) is the modulus of the roots in gal.roots.

gal[3] is a vector L containing the p-adic roots of pol as integers implicitly modulo gal.mod.

(gal.roots).

gal[4] is the inverse of the Vandermonde matrix of the p-adic roots of pol, multiplied by gal[5].

gal[5] is a multiple of the least common denominator of the automorphisms expressed as polynomial in a root of pol. gal[6] is the Galois group G expressed as a vector of permutations of L (gal.group). gal[7] is a generating subset S = [s_{1}, …, s_{g}] of G expressed as a vector of permutations of L

(gal.gen).

gal[8] contains the relative orders [o_{1}, …, o_{g}] of the generators of S (gal.orders). Let H_{n} be as above, we have the following properties:

• if G/H_{n} ≃ A_{4} then [o_{1}, …, o_{g}] ends by [2, 2, 3].

• if G/H_{n} ≃ S_{4} then [o_{1}, …, o_{g}] ends by [2, 2, 3, 2].

• for 1 ≤ i ≤ g the subgroup of G generated by [s_{1}, …, s_{g}] is normal, with the exception of i = g − 2 in the A_{4} case and of i = g − 3 in the S_{A} case.

• the relative order o_{i} of s_{i} is its order in the quotient group G/⟨s_{1}, …, s_{i−1}⟩, with the same exceptions.

• for any x ∈ G there exists a unique family [e_{1}, …, e_{g}] such that (no exceptions):

– for 1 ≤ i ≤ g we have 0 ≤ e_{i} < o_{i}

– x = g_{1}^{e_{1}}g_{2}^{e_{2}} … g_{n}^{e_{n}} If present den must be a suitable value for gal[5].

The library syntax is GEN galoisinit(GEN pol, GEN den = NULL).

3.6.39 galoisisabelian(gal, {flag = 0}). gal being as output by galoisinit, return 0 if gal is not an abelian group, and the HNF matrix of gal over gal.gen if fl = 0, 1 if fl = 1.

This command also accepts subgroups returned by galoissubgroups.

The library syntax is GEN galoisisabelian(GEN gal, long flag).

3.6.40 galoisisnormal(gal, subgrp). gal being as output by galoisinit, and subgrp a subgroup of gal as output by galoissubgroups,return 1 if subgrp is a normal subgroup of gal, else return 0.

This command also accepts subgroups returned by galoissubgroups.

The library syntax is long galoisisnormal(GEN gal, GEN subgrp).

3.6.41 galoispermtopol(gal, perm). gal being a Galois group as output by galoisinit and perm a element of gal.group, return the polynomial defining the Galois automorphism, as output by nfgaloisconj, associated with the permutation perm of the roots gal.roots. perm can also be a vector or matrix, in this case, galoispermtopol is applied to all components recursively. Note that

G = galoisinit(pol);
galoispermtopol(G, G[6])~

is equivalent to nfgaloisconj(pol), if degree of pol is greater or equal to 2.

The library syntax is GEN galoispermtopol(GEN gal, GEN perm).

3.6.42 galoissubcyclo(N, H, {fl = 0}, {v}). Computes the subextension of Q(ζ_{n}) fixed by the subgroup H ⊂ (Z/nZ)^{∗}. By the Kronecker-Weber theorem, all abelian number fields can be generated in this way (uniquely if n is taken to be minimal).

The pair (n, H) is deduced from the parameters (N, H) as follows

• N an integer: then n = N; H is a generator, i.e. an integer or an integer modulo n; or a vector of generators.

• N the output of znstar(n). H as in the first case above, or a matrix, taken to be a HNF left divisor of the SNF for (Z/nZ)^{∗} (of type N.cyc), giving the generators of H in terms of N.gen.

• N the output of bnrinit(bnfinit(y), m, 1) where m is a module. H as in the first case, or a matrix taken to be a HNF left divisor of the SNF for the ray class group modulo m (of type N.cyc), giving the generators of H in terms of N.gen.

In this last case, beware that H is understood relatively to N; in particular, if the infinite place does not divide the module, e.g if m is an integer, then it is not a subgroup of (Z/nZ)^{∗}, but of its quotient by {±1}.

If fl = 0, compute a polynomial (in the variable v) defining the the subfield of Q(ζ_{n}) fixed by the subgroup H of (Z/nZ)^{∗}.

If fl = 1, compute only the conductor of the abelian extension, as a module.

If fl = 2, output [pol, N], where pol is the polynomial as output when fl = 0 and N the conductor as output when fl = 1.

The following function can be used to compute all subfields of Q(ζ_{n}) (of exact degree d, if d is set):

    polsubcyclo(n, d = -1)=
    { my(bnr,L,IndexBound);
      IndexBound = if (d < 0, n, [d]);
      bnr = bnrinit(bnfinit(y), [n,[1]], 1);
      L = subgrouplist(bnr, IndexBound, 1);
      vector(#L,i, galoissubcyclo(bnr,L[i]));
    }

Setting L = subgrouplist(bnr, IndexBound) would produce subfields of exact conductor n∞.

    The library syntax is GEN galoissubcyclo(GEN N, GEN H = NULL, long fl, long v = -

1), where v is a variable number.

3.6.43 galoissubfields(G, {flags = 0}, {v}). Outputs all the subfields of the Galois group G, as a vector. This works by applying galoisfixedfield to all subgroups. The meaning of the flag fl is

the same as for galoisfixedfield.
    The library syntax is GEN galoissubfields(GEN G, long flags, long v = -1), where v is

a variable number.

3.6.44 galoissubgroups(G). Outputs all the subgroups of the Galois group gal. A subgroup is a vector [gen, orders], with the same meaning as for gal.gen and gal.orders. Hence gen is a vector of permutations generating the subgroup, and orders is the relatives orders of the generators. The cardinal of a subgroup is the product of the relative orders. Such subgroup can be used instead

of a Galois group in the following command: galoisisabelian, galoissubgroups, galoisexport
and galoisidentify.

To get the subfield fixed by a subgroup sub of gal, use

    galoisfixedfield(gal,sub[1])
    The library syntax is GEN galoissubgroups(GEN G).

3.6.45 idealadd(nf , x, y). Sum of the two ideals x and y in the number field nf . The result is given in HNF.

     ? K = nfinit(x^2 + 1);
     ? a = idealadd(K, 2, x + 1)  \\ ideal generated by 2 and 1+I
     %2 =
     [2 1]
     [0 1]
     ? pr = idealprimedec(K, 5)[1];  \\ a prime ideal above 5
     ? idealadd(K, a, pr)     \\ coprime, as expected
     %4 =
     [1 0]
     [0 1]

This function cannot be used to add arbitrary Z-modules, since it assumes that its arguments are ideals:

      ? b = Mat([1,0]~);
      ? idealadd(K, b, b)     \\ only square t_MATs represent ideals
      *** idealadd: non-square t_MAT in idealtyp.
      ? c = [2, 0; 2, 0]; idealadd(K, c, c)   \\ non-sense
      %6 =
      [2 0]
      [0 2]
      ? d = [1, 0; 0, 2]; idealadd(K, d, d)   \\ non-sense
      %7 =
      [1 0]
      [0 1]

In the last two examples, we get wrong results since the matrices c and d do not correspond to an ideal: the Z-span of their columns (as usual interpreted as coordinates with respect to the integer basis K.zk) is not an O_{K}-module. To add arbitrary Z-modules generated by the columns of matrices A and B, use mathnf(concat(A,B)).

    The library syntax is GEN idealadd(GEN nf, GEN x, GEN y).

3.6.46 idealaddtoone(nf , x, {y}). x and y being two co-prime integral ideals (given in any form), this gives a two-component row vector [a, b] such that a ∈ x, b ∈ y and a + b = 1.

The alternative syntax idealaddtoone(nf , v), is supported, where v is a k-component vector of ideals (given in any form) which sum to Z_{K}. This outputs a k-component vector e such that e[i] ∈ x[i] for 1 ≤ i ≤ k and ∑_{1≤i≤k} e[i] = 1.

    The library syntax is GEN idealaddtoone0(GEN nf, GEN x, GEN y = NULL).

3.6.47 idealappr(nf , x, {flag = 0}). If x is a fractional ideal (given in any form), gives an element α in nf such that for all prime ideals 𝔭 such that the valuation of x at 𝔭 is non-zero, we have v_{𝔭}(α) = v_{𝔭}(x), and v_{𝔭}(α) ≥ 0 for all other 𝔭.

If flag is non-zero, x must be given as a prime ideal factorization, as output by idealfactor, but possibly with zero or negative exponents. This yields an element α such that for all prime ideals 𝔭 occurring in x, v_{𝔭}(α) is equal to the exponent of 𝔭 in x, and for all other prime ideals, v_{𝔭}(α) ≥ 0. This generalizes idealappr(nf , x, 0) since zero exponents are allowed. Note that the algorithm used is slightly different, so that

      idealappr(nf, idealfactor(nf,x))

may not be the same as idealappr(nf,x,1).

    The library syntax is GEN idealappr0(GEN nf, GEN x, long flag).

3.6.48 idealchinese(nf , x, y). x being a prime ideal factorization (i.e. a 2 by 2 matrix whose first column contains prime ideals, and the second column integral exponents), y a vector of elements in nf indexed by the ideals in x, computes an element b such that

v_{𝔭}(b − y_{𝔭}) ≥ v_{𝔭}(x) for all prime ideals in x and v_{𝔭}(b) ≥ 0 for all other 𝔭.

    The library syntax is GEN idealchinese(GEN nf, GEN x, GEN y).

3.6.49 idealcoprime(nf , x, y). Given two integral ideals x and y in the number field nf , returns a β in the field, such that β · x is an integral ideal coprime to y.

    The library syntax is GEN idealcoprime(GEN nf, GEN x, GEN y).

3.6.50 idealdiv(nf , x, y, {flag = 0}). Quotient x · y^{−1} of the two ideals x and y in the number field nf . The result is given in HNF.

If flag is non-zero, the quotient x · y^{−1} is assumed to be an integral ideal. This can be much faster when the norm of the quotient is small even though the norms of x and y are large.

    The library syntax is GEN idealdiv0(GEN nf, GEN x, GEN y, long flag). Also available
are GEN idealdiv(GEN nf, GEN x, GEN y) (flag = 0) and GEN idealdivexact(GEN nf, GEN x,

GEN y) (flag = 1).

3.6.51 idealfactor(nf , x). Factors into prime ideal powers the ideal x in the number field nf . The output format is similar to the factor function, and the prime ideals are represented in the form output by the idealprimedec function, i.e. as 5-element vectors.

    The library syntax is GEN idealfactor(GEN nf, GEN x).

3.6.52 idealfactorback(nf , f, {e}, {flag = 0}). Gives back the ideal corresponding to a factorization. The integer 1 corresponds to the empty factorization. If e is present, e and f must be vectors of the same length (e being integral), and the corresponding factorization is the product of the f[i]^{e[i]}.

If not, and f is vector, it is understood as in the preceding case with e a vector of 1s: we return the product of the f[i]. Finally, f can be a regular factorization, as produced by idealfactor.

? nf = nfinit(y^2+1); idealfactor(nf, 4 + 2*y)
%1 =
[[2, [1, 1]~, 2, 1, [1, 1]~] 2]
[[5, [2, 1]~, 1, 1, [-2, 1]~] 1]
? idealfactorback(nf, %)
%2 =
[10 4]
[0  2]
? f = %1[,1]; e = %1[,2]; idealfactorback(nf, f, e)
%3 =
[10 4]
[0  2]
? % == idealhnf(nf, 4 + 2*y)
%4 = 1

If flag is non-zero, perform ideal reductions (idealred) along the way. This is most useful if the ideals involved are all extended ideals (for instance with trivial principal part), so that the principal parts extracted by idealred are not lost. Here is an example:

? f = vector(#f, i, [f[i], [;]]);  \\ transform to extended ideals
? idealfactorback(nf, f, e, 1)
%6 = [[1, 0; 0, 1], [2, 1; [2, 1]~, 1]]
? nffactorback(nf, %[2])
%7 = [4, 2]~

The extended ideal returned in %6 is the trivial ideal 1, extended with a principal generator given in factored form. We use nffactorback to recover it in standard form.

The library syntax is GEN idealfactorback(GEN nf, GEN f, GEN e = NULL, long flag ).

3.6.53 idealfrobenius(nf , gal, pr). Let K be the number field defined by nf and assume K/Q be a Galois extension with Galois group given gal=galoisinit(nf), and that pr is the prime ideal 𝔓 in prid format, and that 𝔓 is unramified. This function returns a permutation of gal.group which defines the automorphism σ = (\frac{𝔓}{K/Q}), i.e the Frobenius element associated to 𝔓. If p is the unique prime number in 𝔓, then σ(x) ≡ x^{p} mod P for all x ∈ Z_{K}.

? nf = nfinit(polcyclo(31));
? gal = galoisinit(nf);
? pr = idealprimedec(nf,101)[1];
? g = idealfrobenius(nf,gal,pr);
? galoispermtopol(gal,g)
%5 = x^8

This is correct since 101 ≡ 8 mod 31.

The library syntax is GEN idealfrobenius(GEN nf, GEN gal, GEN pr).

3.6.54 idealhnf(nf , u, {v}). Gives the Hermite normal form of the ideal uZ_{K} + vZ_{K}, where u and v are elements of the number field K defined by nf.

? nf = nfinit(y^3 - 2);
? idealhnf(nf, 2, y+1)
%2 =
[1 0 0]
[0 1 0]
[0 0 1]
? idealhnf(nf, y/2, [0,0,1/3]~)
%3 =
[1/3 0 0]
[0 1/6 0]
[0 0 1/6]

If b is omitted, returns the HNF of the ideal defined by u: u may be an algebraic number (defining a principal ideal), a maximal ideal (as given by idealprimedec or idealfactor), or a matrix whose columns give generators for the ideal. This last format is a little complicated, but useful to reduce general modules to the canonical form once in a while:

• if strictly less than N = [K : Q] generators are given, u is the Z_{K}-module they generate,

• if N or more are given, it is assumed that they form a Z-basis of the ideal, in particular that the matrix has maximal rank N. This acts as mathnf since the Z_{K}-module structure is (taken for granted hence) not taken into account in this case.

? idealhnf(nf, idealprimedec(nf,2)[1])
%4 =
[2 0 0]
[0 1 0]
[0 0 1]
? idealhnf(nf, [1,2;2,3;3,4])
%5 =
[1 0 0]
[0 1 0]
[0 0 1]

Finally, when K is quadratic with discriminant D_{K}, we allow u = Qfb(a,b,c), provided b^{2} −4ac = D_{K}. As usual, this represents the ideal aZ + (1/2)(−b + \sqrt{D_{K}})Z.

? K = nfinit(x^2 - 60); K.disc
%1 = 60
? idealhnf(K, qfbprimeform(60,2))
%2 =
[2 1]
[0 1]

? idealhnf(K, Qfb(1,2,3))
  ***   at top-level: idealhnf(K,Qfb(1,2,3
  ***                 ^--------------------
  *** idealhnf: Qfb(1, 2, 3) has discriminant != 60 in idealhnf.
The library syntax is GEN idealhnf0(GEN nf, GEN u, GEN v = NULL). Also available is GEN
idealhnf(GEN nf, GEN a).

3.6.55 idealintersect(nf , A, B). Intersection of the two ideals A and B in the number field nf . The result is given in HNF.

? nf = nfinit(x^2+1);
? idealintersect(nf, 2, x+1)
%2 =
[2 0]
[0 2]

This function does not apply to general Z-modules, e.g. orders, since its arguments are replaced by the ideals they generate. The following script intersects Z-modules A and B given by matrices of compatible dimensions with integer coefficients:

ZM_intersect(A,B) =
{ my(Ker = matkerint(concat(A,B)));
  mathnf( A * Ker[1..#A,] )
}
The library syntax is GEN idealintersect(GEN nf, GEN A, GEN B).

3.6.56 idealinv(nf , x). Inverse of the ideal x in the number field nf , given in HNF. If x is an extended ideal, its principal part is suitably updated: i.e. inverting [I, t], yields [I^{−1}, 1/t].

The library syntax is GEN idealinv(GEN nf, GEN x).

3.6.57 ideallist(nf , bound, {flag = 4}). Computes the list of all ideals of norm less or equal to bound in the number field nf . The result is a row vector with exactly bound components. Each component is itself a row vector containing the information about ideals of a given norm, in no specific order, depending on the value of flag:

The possible values of flag are:

0: give the bid associated to the ideals, without generators.

1: as 0, but include the generators in the bid.

2: in this case, nf must be a bnf with units. Each component is of the form [bid, U], where bid is as case 0 and U is a vector of discrete logarithms of the units. More precisely, it gives the ideallogs with respect to bid of bnf.tufu. This structure is technical, and only meant to be used

in conjunction with bnrclassnolist or bnrdisclist.

3: as 2, but include the generators in the bid.

4: give only the HNF of the ideal.

? nf = nfinit(x^2+1);
? L = ideallist(nf, 100);

? L[1]

%3 = [[1, 0; 0, 1]] \\ A single ideal of norm 1

? #L[65]

%4 = 4 \\ There are 4 ideals of norm 4 in Z[i]

If one wants more information, one could do instead:

? nf = nfinit(x^2+1);
? L = ideallist(nf, 100, 0);
? l = L[25]; vector(#l, i, l[i].clgp)
%3 = [[20, [20]], [16, [4, 4]], [20, [20]]]
? l[1].mod
%4 = [[25, 18; 0, 1], []]
? l[2].mod
%5 = [[5, 0; 0, 5], []]
? l[3].mod
%6 = [[25, 7; 0, 1], []]

where we ask for the structures of the (Z[i]/I)^{∗} for all three ideals of norm 25. In fact, for all moduli with finite part of norm 25 and trivial Archimedean part, as the last 3 commands show. See ideallistarch to treat general moduli.

The library syntax is GEN ideallist0(GEN nf, long bound, long flag).

3.6.58 ideallistarch(nf , list, arch). list is a vector of vectors of bid’s, as output by ideallist with flag 0 to 3. Return a vector of vectors with the same number of components as the original list. The leaves give information about moduli whose finite part is as in original list, in the same order, and Archimedean part is now arch (it was originally trivial). The information contained is of the same kind as was present in the input; see ideallist, in particular the meaning of flag.

? bnf = bnfinit(x^2-2);
? bnf.sign

%2 = [2, 0] \\ two places at infinity

? L = ideallist(bnf, 100, 0);
? l = L[98]; vector(#l, i, l[i].clgp)
%4 = [[42, [42]], [36, [6, 6]], [42, [42]]]
? La = ideallistarch(bnf, L, [1,1]); \\ add them to the modulus
? l = La[98]; vector(#l, i, l[i].clgp)
%6 = [[168, [42, 2, 2]], [144, [6, 6, 2, 2]], [168, [42, 2, 2]]]

Of course, the results above are obvious: adding t places at infinity will add t copies of Z/2Z to the ray class group. The following application is more typical:

? L = ideallist(bnf, 100, 2);        \\ units are required now
? La = ideallistarch(bnf, L, [1,1]);
? H = bnrclassnolist(bnf, La);
? H[98];
%6 = [2, 12, 2]
The library syntax is GEN ideallistarch(GEN nf, GEN list, GEN arch).

3.6.59 ideallog(nf , x, bid). nf is a number field, bid is as output by idealstar(nf, D, …) and x a non-necessarily integral element of nf which must have valuation equal to 0 at all prime ideals in the support of D. This function computes the discrete logarithm of x on the generators given in bid.gen. In other words, if g_{i} are these generators, of orders d_{i} respectively, the result is a column vector of integers (x_{i}) such that 0 ≤ x_{i} < d_{i} and

x ≡ ∏_{i} g_{i}^{x_{i}} (mod ^{∗}D) .

Note that when the support of D contains places at infinity, this congruence implies also sign conditions on the associated real embeddings. See znlog for the limitations of the underlying discrete log algorithms.

    The library syntax is GEN ideallog(GEN nf, GEN x, GEN bid).

3.6.60 idealmin(nf , ix, {vdir}). This function is useless and kept for backward compatibility only, use idealred. Computes a pseudo-minimum of the ideal x in the direction vdir in the number field nf .

    The library syntax is GEN idealmin(GEN nf, GEN ix, GEN vdir = NULL).

3.6.61 idealmul(nf , x, y, {flag = 0}). Ideal multiplication of the ideals x and y in the number field nf ; the result is the ideal product in HNF. If either x or y are extended ideals, their principal part is suitably updated: i.e. multiplying [I, t], [J, u] yields [IJ, tu]; multiplying I and [J, u] yields [IJ, u].

    ? nf = nfinit(x^2 + 1);
    ? idealmul(nf, 2, x+1)
    %2 =
    [4 2]
    [0 2]
    ? idealmul(nf, [2, x], x+1)        \\ extended ideal * ideal
    %4 = [[4, 2; 0, 2], x]
    ? idealmul(nf, [2, x], [x+1, x])   \\ two extended ideals
    %5 = [[4, 2; 0, 2], [-1, 0]~]

If flag is non-zero, reduce the result using idealred.

    The library syntax is GEN idealmul0(GEN nf, GEN x, GEN y, long flag).
See also GEN idealmul(GEN nf, GEN x, GEN y) (flag = 0) and GEN idealmulred(GEN nf, GEN

x, GEN y) (flag ≠ 0).

3.6.62 idealnorm(nf , x). Computes the norm of the ideal x in the number field nf .

    The library syntax is GEN idealnorm(GEN nf, GEN x).

3.6.63 idealnumden(nf , x). Returns [A, B], where A, B are coprime integer ideals such that x = A/B, in the number field nf .

    ? nf = nfinit(x^2+1);
    ? idealnumden(nf, (x+1)/2)
    %2 = [[1, 0; 0, 1], [2, 1; 0, 1]]
    The library syntax is GEN idealnumden(GEN nf, GEN x).

3.6.64 idealpow(nf , x, k, {flag = 0}). Computes the k-th power of the ideal x in the number field nf ; k ∈ Z. If x is an extended ideal, its principal part is suitably updated: i.e. raising [I, t] to the k-th power, yields [I^{k}, t^{k}].

If flag is non-zero, reduce the result using idealred, throughout the (binary) powering process; in particular, this is not the same as as idealpow(nf , x, k) followed by reduction.

The library syntax is GEN idealpow0(GEN nf, GEN x, GEN k, long flag).
See also GEN idealpow(GEN nf, GEN x, GEN k) and GEN idealpows(GEN nf, GEN x, long k)

(flag = 0). Corresponding to flag = 1 is GEN idealpowred(GEN nf, GEN vp, GEN k).

3.6.65 idealprimedec(nf , p). Computes the prime ideal decomposition of the (positive) prime number p in the number field K represented by nf . If a non-prime p is given the result is undefined.

The result is a vector of prid structures, each representing one of the prime ideals above p in the number field nf . The representation pr = [p, a, e, f, mb] of a prime ideal means the following: a and is an algebraic integer in the maximal order Z_{K} and the prime ideal is equal to 𝔭 = pZ_{K} +aZ_{K}; e is the ramification index; f is the residual index; finally, mb is the multiplication table associated to the algebraic integer b is such that 𝔭^{−1} = Z_{K} + b/pZ_{K}, which is used internally to compute valuations. In other words if p is inert, then mb is the integer 1, and otherwise it’s a square t_MAT whose j-th column is b · nf.zk[j].

The algebraic number a is guaranteed to have a valuation equal to 1 at the prime ideal (this is automatic if e > 1).

The components of pr should be accessed by member functions: pr.p, pr.e, pr.f, and pr.gen (returns the vector [p, a]):

? K = nfinit(x^3-2);
? L = idealprimedec(K, 5);
? #L       \\ 2 primes above 5 in Q(2^(1/3))
%3 = 2
? p1 = L[1]; p2 = L[2];
? [p1.e, p1.f]    \\ the first is unramified of degree 1
%4 = [1, 1]
? [p2.e, p2.f]    \\ the second is unramified of degree 2
%5 = [1, 2]
? p1.gen
%6 = [5, [2, 1, 0]~]
? nfbasistoalg(K, %[2])  \\ a uniformizer for p1
%7 = Mod(x + 2, x^3 - 2)
The library syntax is GEN idealprimedec(GEN nf, GEN p).

3.6.66 idealprincipalunits(nf , pr, k). Given a prime ideal in idealprimedec format, returns the multiplicative group (1 + pr)/(1 + pr^{k}) as an abelian group. This function is much faster than idealstar when the norm of pr is large, since it avoids (useless) work in the multiplicative group of the residue field.

? K = nfinit(y^2+1);
? P = idealprimedec(K,2)[1];
? G = idealprincipalunits(K, P, 20);
? G.cyc
[512, 256, 4]   \\ Z/512 x Z/256 x Z/4
? G.gen
%5 = [[-1, -2]~, 1021, [0, -1]~] \\ minimal generators of given order
The library syntax is GEN idealprincipalunits(GEN nf, GEN pr, long k).

3.6.67 idealramgroups(nf , gal, pr). Let K be the number field defined by nf and assume that K/Q is Galois with Galois group G given by gal=galoisinit(nf). Let pr be the prime ideal 𝔓 in prid format. This function returns a vector g of subgroups of gal as follow:

• g[1] is the decomposition group of 𝔓,

• g[2] is G_{0}(𝔓), the inertia group of 𝔓,

and for i ≥ 2,

• g[i] is G_{i−2}(𝔓), the i − 2-th ramification group of 𝔓.

The length of g is the number of non-trivial groups in the sequence, thus is 0 if e = 1 and f = 1, and 1 if f > 1 and e = 1. The following function computes the cardinality of a subgroup of G, as given by the components of g:

card(H) =my(o=H[2]); prod(i=1,#o,o[i]);
? nf=nfinit(x^6+3); gal=galoisinit(nf); pr=idealprimedec(nf,3)[1];
? g = idealramgroups(nf, gal, pr);
? apply(card,g)
%4 = [6, 6, 3, 3, 3] \\ cardinalities of the G_i
? nf=nfinit(x^6+108); gal=galoisinit(nf); pr=idealprimedec(nf,2)[1];
? iso=idealramgroups(nf,gal,pr)[2]
%4 = [[Vecsmall([2, 3, 1, 5, 6, 4])], Vecsmall([3])]
? nfdisc(galoisfixedfield(gal,iso,1))
%5 = -3

The field fixed by the inertia group of 2 is not ramified at 2.

The library syntax is GEN idealramgroups(GEN nf, GEN gal, GEN pr).

3.6.68 idealred(nf , I, {v = 0}). LLL reduction of the ideal I in the number field nf , along the direction v. The v parameter is best left omitted, but if it is present, it must be an nf.r1 + nf.r2component vector of non-negative integers. (What counts is the relative magnitude of the entries: if all entries are equal, the effect is the same as if the vector had been omitted.)

This function finds a “small” a in I (see the end for technical details). The result is the Hermite normal form of the “reduced” ideal J = rI/a, where r is the unique rational number such that J is integral and primitive. (This is usually not a reduced ideal in the sense of Buchmann.)

    ? K = nfinit(y^2+1);
    ? P = idealprimedec(K,5)[1];
    ? idealred(K, P)
    %3 =
    [1 0]
    [0 1]

More often than not, a principal ideal yields the unit ideal as above. This is a quick and dirty way to check if ideals are principal, but it is not a necessary condition: a non-trivial result does not prove that the ideal is non-principal. For guaranteed results, see bnfisprincipal, which requires the computation of a full bnf structure.

If the input is an extended ideal [I, s], the output is [J, sa/r]; this way, one can keep track of the principal ideal part:

    ? idealred(K, [P, 1])
    %5 = [[1, 0; 0, 1], [-2, 1]~]

meaning that P is generated by [−2, 1] . The number field element in the extended part is an algebraic number in any form or a factorization matrix (in terms of number field elements, not ideals!). In the latter case, elements stay in factored form, which is a convenient way to avoid coefficient explosion; see also idealpow.

Technical note. The routine computes an LLL-reduced basis for the lattice I equipped with the quadratic form

||x||_{v}^{2} = ∑_{i=1}^{r_{1}+r_{2}} 2^{v_{i}}ε_{i}|σ_{i}(x)|^{2},

where as usual the σ_{i} are the (real and) complex embeddings and ε_{i} = 1, resp. 2, for a real, resp. complex place. The element a is simply the first vector in the LLL basis. The only reason you may want to try to change some directions and set some v_{i} ≠ 0 is to randomize the elements found for a fixed ideal, which is heuristically useful in index calculus algorithms like bnfinit and

bnfisprincipal.

Even more technical note. In fact, the above is a white lie. We do not use || · ||_{v} exactly but a rescaled rounded variant which gets us faster and simpler LLLs. There’s no harm since we are not using any theoretical property of a after all, except that it belongs to I and is “expected to be small”.

    The library syntax is GEN idealred0(GEN nf, GEN I, GEN v = NULL).

3.6.69 idealstar(nf , I, {flag = 1}). Outputs a bid structure, necessary for computing in the finite abelian group G = (Z_{K}/I)^{∗}. Here, nf is a number field and I is a modulus: either an ideal in any form, or a row vector whose first component is an ideal and whose second component is a row vector of r_{1} 0 or 1. Ideals can also be given by a factorization into prime ideals, as produced by

idealfactor.

This bid is used in ideallog to compute discrete logarithms. It also contains useful information which can be conveniently retrieved as bid.mod (the modulus), bid.clgp (G as a finite abelian group), bid.no (the cardinality of G), bid.cyc (elementary divisors) and bid.gen (generators).

If flag = 1 (default), the result is a bid structure without generators.

If flag = 2, as flag = 1, but including generators, which wastes some time.

If flag = 0, only outputs (Z_{K}/I)^{∗} as an abelian group, i.e as a 3-component vector [h, d, g]: h is the order, d is the vector of SNF cyclic components and g the corresponding generators.

The library syntax is GEN idealstar0(GEN nf, GEN I, long flag). Instead the above hardcoded numerical flags, one should rather use GEN Idealstar(GEN nf, GEN ideal, long flag), where flag is an or-ed combination of nf_GEN (include generators) and nf_INIT (return a full bid, not a group), possibly 0. This offers one more combination: gen, but no init.

3.6.70 idealtwoelt(nf , x, {a}). Computes a two-element representation of the ideal x in the number field nf , combining a random search and an approximation theorem; x is an ideal in any form (possibly an extended ideal, whose principal part is ignored)

• When called as idealtwoelt(nf,x), the result is a row vector [a, α] with two components such that x = aZ_{K} + αZ_{K} and a is chosen to be the positive generator of x ∩ Z, unless x was given as a principal ideal (in which case we may choose a = 0). The algorithm uses a fast lazy factorization of x ∩ Z and runs in randomized polynomial time.

• When called as idealtwoelt(nf,x,a) with an explicit non-zero a supplied as third argument, the function assumes that a ∈ x and returns α ∈ x such that x = aZ_{K} + αZ_{K}. Note that we must factor a in this case, and the algorithm is generally much slower than the default variant.

    The library syntax is GEN idealtwoelt0(GEN nf, GEN x, GEN a = NULL). Also available are
GEN idealtwoelt(GEN nf, GEN x) and GEN idealtwoelt2(GEN nf, GEN x, GEN a).

3.6.71 idealval(nf , x, pr). Gives the valuation of the ideal x at the prime ideal pr in the number field nf , where pr is in idealprimedec format.

    The library syntax is long idealval(GEN nf, GEN x, GEN pr).

3.6.72 matalgtobasis(nf , x). nf being a number field in nfinit format, and x a (row or column) vector or matrix, apply nfalgtobasis to each entry of x.

    The library syntax is GEN matalgtobasis(GEN nf, GEN x).

3.6.73 matbasistoalg(nf , x). nf being a number field in nfinit format, and x a (row or column) vector or matrix, apply nfbasistoalg to each entry of x.

    The library syntax is GEN matbasistoalg(GEN nf, GEN x).

3.6.74 modreverse(z). Let z = Mod(A, T) be a polmod, and Q be its minimal polynomial, which must satisfy deg(Q) = deg(T). Returns a “reverse polmod” Mod(B, Q), which is a root of T.

This is quite useful when one changes the generating element in algebraic extensions:

? u = Mod(x, x^3 - x -1); v = u^5;
? w = modreverse(v)
%2 = Mod(x^2 - 4*x + 1, x^3 - 5*x^2 + 4*x - 1)

which means that x^{3} − 5x^{2} + 4x − 1 is another defining polynomial for the cubic field

Q(u) = Q[x]/(x^{3} − x − 1) = Q[x]/(x^{3} − 5x^{2} + 4x − 1) = Q(v),

and that u → v^{2} − 4v + 1 gives an explicit isomorphism. From this, it is easy to convert elements between the A(u) ∈ Q(u) and B(v) ∈ Q(v) representations:

? A = u^2 + 2*u + 3; subst(lift(A), 'x, w)
%3 = Mod(x^2 - 3*x + 3, x^3 - 5*x^2 + 4*x - 1)
? B = v^2 + v + 1;   subst(lift(B), 'x, v)
%4 = Mod(26*x^2 + 31*x + 26, x^3 - x - 1)

If the minimal polynomial of z has lower degree than expected, the routine fails

? u = Mod(-x^3 + 9*x, x^4 - 10*x^2 + 1)
? modreverse(u)
 *** modreverse: domain error in modreverse: deg(minpoly(z)) < 4
 ***   Break loop: type 'break' to go back to GP prompt
break> Vec( dbg_err() ) \\ ask for more info
["e_DOMAIN", "modreverse", "deg(minpoly(z))", "<", 4,
  Mod(-x^3 + 9*x, x^4 - 10*x^2 + 1)]
break> minpoly(u)
x^2 - 8
The library syntax is GEN modreverse(GEN z).

3.6.75 newtonpoly(x, p). Gives the vector of the slopes of the Newton polygon of the polynomial x with respect to the prime number p. The n components of the vector are in decreasing order, where n is equal to the degree of x. Vertical slopes occur iff the constant coefficient of x is zero and are denoted by LONG_MAX, the biggest single precision integer representable on the machine (2^{31} − 1 (resp. 2^{63} − 1) on 32-bit (resp. 64-bit) machines), see Section 3.2.55.

The library syntax is GEN newtonpoly(GEN x, GEN p).

3.6.76 nfalgtobasis(nf , x). Given an algebraic number x in the number field nf , transforms it to a column vector on the integral basis nf .zk.

? nf = nfinit(y^2 + 4);
? nf.zk
%2 = [1, 1/2*y]
? nfalgtobasis(nf, [1,1]~)
%3 = [1, 1]~
? nfalgtobasis(nf, y)
%4 = [0, 2]~
? nfalgtobasis(nf, Mod(y, y^2+4))
%4 = [0, 2]~

This is the inverse function of nfbasistoalg.

The library syntax is GEN algtobasis(GEN nf, GEN x).

3.6.77 nfbasis(T). Let T (X) be an irreducible polynomial with integral coefficients. This function returns an integral basis of the number field defined by T, that is a Z-basis of its maximal order. The basis elements are given as elements in Q[X]/(T):

? nfbasis(x^2 + 1)
%1 = [1, x]

This function uses a modified version of the round 4 algorithm, due to David Ford, Sebastian Pauli and Xavier Roblot.

Local basis, orders maximal at certain primes.

Obtaining the maximal order is hard: it requires factoring the discriminant D of T. Obtaining an order which is maximal at a finite explicit set of primes is easy, but if may then be a strict suborder of the maximal order. To specify that we are interested in a given set of places only, we can replace the argument T by an argument [T, listP], where listP encodes the primes we are interested in: it must be a factorization matrix, a vector of integers or a single integer.

• Vector: we assume that it contains distinct prime numbers.

• Matrix: we assume that it is a two-column matrix of a (partial) factorization of D; namely the first column contains primes and the second one the valuation of D at each of these primes.

• Integer B: this is replaced by the vector of primes up to B. Note that the function will use at least O(B) time: a small value, about 10^{5}, should be enough for most applications. Values larger than 2^{32} are not supported.

In all these cases, the primes may or may not divide the discriminant D of T. The function then returns a Z-basis of an order whose index is not divisible by any of these prime numbers. The result is actually a global integral basis if all prime divisors of the field discriminant are included! Note that nfinit has built-in support for such a check:

? K = nfinit([T, listP]);
? nfcertify(K)   \\ we computed an actual maximal order
%2 = [];

The first line initializes a number field structure incorporating nfbasis([T, listP] in place of a proven integral basis. The second line certifies that the resulting structure is correct. This allows to create an nf structure associated to the number field K = Q[X]/(T), when the discriminant of T cannot be factored completely, whereas the prime divisors of discK are known.

Of course, if listP contains a single prime number p, the function returns a local integral basis for Z_{p}[X]/(T):

    ? nfbasis(x^2+x-1001)
    %1 = [1, 1/3*x - 1/3]
    ? nfbasis( [x^2+x-1001, [2]] )
    %2 = [1, x]

The Buchmann-Lenstra algorithm.

We now complicate the picture: it is in fact allowed to include composite numbers instead of primes in listP (Vector or Matrix case), provided they are pairwise coprime. The result will still be a correct integral basis if the field discriminant factors completely over the actual primes in the list. Adding a composite C such that C^{2} divides D may help because when we consider C as a prime and run the algorithm, two good things can happen: either we succeed in proving that no prime dividing C can divide the index (without actually needing to find those primes), or the computation exhibits a non-trivial zero divisor, thereby factoring C and we go on with the refined factorization. (Note that including a C such that C^{2} does not divide D is useless.) If neither happen, then the computed basis need not generate the maximal order. Here is an example:

    ? B = 10^5;
    ? P = factor(poldisc(T), B)[,1]; \\ primes <= B dividing D + cofactor
    ? basis = nfbasis([T, listP])
    ? disc = nfdisc([T, listP])

We obtain the maximal order and its discriminant if the field discriminant factors completely over the primes less than B (together with the primes contained in the addprimes table). This can be tested as follows:

      check = factor(disc, B);
      lastp = check[-1..-1,1];
      if (lastp > B && !setsearch(addprimes(), lastp),
        warning("nf may be incorrect!"))

This is a sufficient but not a necessary condition, hence the warning, instead of an error. N.B. lastp is the last entry in the first column of the check matrix, i.e. the largest prime dividing nf.disc if ≤ B or if it belongs to the prime table.

The function nfcertify speeds up and automates the above process:

    ? B = 10^5;
    ? nf = nfinit([T, B]);
    ? nfcertify(nf)
    %3 = []      \\ nf is unconditionally correct
    ? basis = nf.zk;
    ? disc = nf.disc;

The library syntax is nfbasis(GEN T, GEN *d, GEN listP = NULL), which returns the order basis, and where *d receives the order discriminant.

3.6.78 nfbasistoalg(nf , x). Given an algebraic number x in the number field nf, transforms it into t_POLMOD form.

? nf = nfinit(y^2 + 4);
? nf.zk
%2 = [1, 1/2*y]
? nfbasistoalg(nf, [1,1]~)
%3 = Mod(1/2*y + 1, y^2 + 4)
? nfbasistoalg(nf, y)
%4 = Mod(y, y^2 + 4)
? nfbasistoalg(nf, Mod(y, y^2+4))
%4 = Mod(y, y^2 + 4)

This is the inverse function of nfalgtobasis.

The library syntax is GEN basistoalg(GEN nf, GEN x).

3.6.79 nfcertify(nf ). nf being as output by nfinit, checks whether the integer basis is known unconditionally. This is in particular useful when the argument to nfinit was of the form [T, listP], specifying a finite list of primes when p-maximality had to be proven.

The function returns a vector of composite integers. If this vector is empty, then nf.zk and nf.disc are correct. Otherwise, the result is dubious. In order to obtain a certified result, one must completely factor each of the given integers, then addprime each of them, then check whether

nfdisc(nf.pol) is equal to nf.disc.
The library syntax is GEN nfcertify(GEN nf).

3.6.80 nfdetint(nf , x). Given a pseudo-matrix x, computes a non-zero ideal contained in (i.e. multiple of) the determinant of x. This is particularly useful in conjunction with nfhnfmod.

The library syntax is GEN nfdetint(GEN nf, GEN x).

3.6.81 nfdisc(T). field discriminant of the number field defined by the integral, preferably monic, irreducible polynomial T (X). Returns the discriminant of the number field Q[X]/(T), using the Round 4 algorithm.

Local discriminants, valuations at certain primes.

As in nfbasis, the argument T can be replaced by [T, listP], where listP is as in nfbasis: a vector of pairwise coprime integers (usually distinct primes), a factorization matrix, or a single integer. In that case, the function returns the discriminant of an order whose basis is given by nfbasis(T,listP), which need not be the maximal order, and whose valuation at a prime entry in listP is the same as the valuation of the field discriminant.

In particular, if listP is [p] for a prime p, we can return the p-adic discriminant of the maximal order of Z_{p}[X]/(T), as a power of p, as follows:

? padicdisc(T,p) = p^valuation(nfdisc(T,[p]), p);
? nfdisc(x^2 + 6)
%1 = -24
? padicdisc(x^2 + 6, 2)
%2 = 8
? padicdisc(x^2 + 6, 3)

    %3 = 3

The library syntax is nfdisc(GEN T) (listP = NULL). Also available is GEN nfbasis(GEN T, GEN *d, GEN listP = NULL), which returns the order basis, and where *d receives the order discriminant.

3.6.82 nfeltadd(nf , x, y). Given two elements x and y in nf , computes their sum x + y in the number field nf .

    The library syntax is GEN nfadd(GEN nf, GEN x, GEN y).

3.6.83 nfeltdiv(nf , x, y). Given two elements x and y in nf , computes their quotient x/y in the number field nf .

    The library syntax is GEN nfdiv(GEN nf, GEN x, GEN y).

3.6.84 nfeltdiveuc(nf , x, y). Given two elements x and y in nf , computes an algebraic integer q in the number field nf such that the components of x − qy are reasonably small. In fact, this is functionally identical to round(nfdiv(nf ,x,y)).

    The library syntax is GEN nfdiveuc(GEN nf, GEN x, GEN y).

3.6.85 nfeltdivmodpr(nf , x, y, pr). Given two elements x and y in nf and pr a prime ideal in modpr format (see nfmodprinit), computes their quotient x/y modulo the prime ideal pr.

    The library syntax is GEN nfdivmodpr(GEN nf, GEN x, GEN y, GEN pr). This function is

normally useless in library mode. Project your inputs to the residue field using nf to Fq, then work there.

3.6.86 nfeltdivrem(nf , x, y). Given two elements x and y in nf , gives a two-element row vector [q, r] such that x = qy + r, q is an algebraic integer in nf , and the components of r are reasonably small.

    The library syntax is GEN nfdivrem(GEN nf, GEN x, GEN y).

3.6.87 nfeltmod(nf , x, y). Given two elements x and y in nf , computes an element r of nf of the form r = x−qy with q and algebraic integer, and such that r is small. This is functionally identical to

                        x − nfmul(nf , round(nfdiv(nf , x, y)), y).
    The library syntax is GEN nfmod(GEN nf, GEN x, GEN y).

3.6.88 nfeltmul(nf , x, y). Given two elements x and y in nf , computes their product x ∗ y in the number field nf .

    The library syntax is GEN nfmul(GEN nf, GEN x, GEN y).

3.6.89 nfeltmulmodpr(nf , x, y, pr). Given two elements x and y in nf and pr a prime ideal in modpr format (see nfmodprinit), computes their product x ∗ y modulo the prime ideal pr.

    The library syntax is GEN nfmulmodpr(GEN nf, GEN x, GEN y, GEN pr). This function is

normally useless in library mode. Project your inputs to the residue field using nf to Fq, then work there.

3.6.90 nfeltnorm(nf , x). Returns the absolute norm of x.

    The library syntax is GEN nfnorm(GEN nf, GEN x).

3.6.91 nfeltpow(nf , x, k). Given an element x in nf , and a positive or negative integer k, computes x^{k} in the number field nf .

    The library syntax is GEN nfpow(GEN nf, GEN x, GEN k). GEN nfinv(GEN nf, GEN x) cor-

respond to k = −1, and GEN nfsqr(GEN nf,GEN x) to k = 2.

3.6.92 nfeltpowmodpr(nf , x, k, pr). Given an element x in nf , an integer k and a prime ideal pr in modpr format (see nfmodprinit), computes x^{k} modulo the prime ideal pr.

    The library syntax is GEN nfpowmodpr(GEN nf, GEN x, GEN k, GEN pr). This function is

normally useless in library mode. Project your inputs to the residue field using nf to Fq, then work there.

3.6.93 nfeltreduce(nf , a, id). Given an ideal id in Hermite normal form and an element a of the number field nf , finds an element r in nf such that a − r belongs to the ideal and r is small.

    The library syntax is GEN nfreduce(GEN nf, GEN a, GEN id).

3.6.94 nfeltreducemodpr(nf , x, pr). Given an element x of the number field nf and a prime ideal pr in modpr format compute a canonical representative for the class of x modulo pr.

The library syntax is GEN nfreducemodpr(GEN nf, GEN x, GEN pr). This function is normally useless in library mode. Project your inputs to the residue field using nf to Fq, then work there.

3.6.95 nfelttrace(nf , x). Returns the absolute trace of x.

    The library syntax is GEN nftrace(GEN nf, GEN x).

3.6.96 nfeltval(nf , x, pr). Given an element x in nf and a prime ideal pr in the format output by idealprimedec, computes the valuation at pr of the element x. The same result can be obtained using idealval(nf ,x,pr), since x is then converted to a principal ideal.

    The library syntax is long nfval(GEN nf, GEN x, GEN pr).

3.6.97 nffactor(nf , T). Factorization of the univariate polynomial T over the number field nf given by nfinit; T has coefficients in nf (i.e. either scalar, polmod, polynomial or column vector). The factors are sorted by increasing degree.

The main variable of nf must be of lower priority than that of T, see Section 2.5.3. However if the polynomial defining the number field occurs explicitly in the coefficients of T as modulus of a t_POLMOD or as a t_POL coefficient, its main variable must be the same as the main variable of T. For example,

    ? nf = nfinit(y^2 + 1);
    ? nffactor(nf, x^2 + y); \\ OK
    ? nffactor(nf, x^2 + Mod(y, y^2+1)); \\ OK
    ? nffactor(nf, x^2 + Mod(z, z^2+1)); \\ WRONG

It is possible to input a defining polynomial for nf instead, but this is in general less efficient since parts of an nf structure will then be computed internally. This is useful in two situations: when you do not need the nf elsewhere, or when you cannot compute the field discriminant due to integer factorization difficulties. In the latter case, if you must use a partial discriminant factorization (as allowed by both nfdisc or nfbasis) to build a partially correct nf structure, always input nf.pol to nffactor, and not your makeshift nf : otherwise factors could be missed.

The library syntax is GEN nffactor(GEN nf, GEN T).

3.6.98 nffactorback(nf , f, {e}). Gives back the nf element corresponding to a factorization. The integer 1 corresponds to the empty factorization.

If e is present, e and f must be vectors of the same length (e being integral), and the corresponding factorization is the product of the f[i]^{e[i]}.

If not, and f is vector, it is understood as in the preceding case with e a vector of 1s: we return the product of the f[i]. Finally, f can be a regular factorization matrix.

? nf = nfinit(y^2+1);
? nffactorback(nf, [3, y+1, [1,2]~], [1, 2, 3])
%2 = [12, -66]~
? 3 * (I+1)^2 * (1+2*I)^3
%3 = 12 - 66*I
The library syntax is GEN nffactorback(GEN nf, GEN f, GEN e = NULL).

3.6.99 nffactormod(nf , Q, pr). Factors the univariate polynomial Q modulo the prime ideal pr in the number field nf . The coefficients of Q belong to the number field (scalar, polmod, polynomial, even column vector) and the main variable of nf must be of lower priority than that of Q (see Section 2.5.3). The prime ideal pr is either in idealprimedec or (preferred) modprinit format. The coefficients of the polynomial factors are lifted to elements of nf :

? K = nfinit(y^2+1);
? P = idealprimedec(K, 3)[1];
? nffactormod(K, x^2 + y*x + 18*y+1, P)
%3 =
[x + (2*y + 1) 1]
[x + (2*y + 2) 1]
? P = nfmodprinit(K, P);  \\ convert to nfmodprinit format
? nffactormod(K, x^2 + y*x + 18*y+1)
[x + (2*y + 1) 1]
[x + (2*y + 2) 1]

Same result, of course, here about 10% faster due to the precomputation.

The library syntax is GEN nffactormod(GEN nf, GEN Q, GEN pr).

3.6.100 nfgaloisapply(nf , aut, x). Let nf be a number field as output by nfinit, and let aut be a Galois automorphism of nf expressed by its image on the field generator (such automorphisms can be found using nfgaloisconj). The function computes the action of the automorphism aut on the object x in the number field; x can be a number field element, or an ideal (possibly extended). Because of possible confusion with elements and ideals, other vector or matrix arguments are forbidden.

? nf = nfinit(x^2+1);
? L = nfgaloisconj(nf)
%2 = [-x, x]~
? aut = L[1]; /* the non-trivial automorphism */
? nfgaloisapply(nf, aut, x)
%4 = Mod(-x, x^2 + 1)
? P = idealprimedec(nf,5); /* prime ideals above 5 */
? nfgaloisapply(nf, aut, P[2]) == P[1]
%7 = 0 \\ !!!!
? idealval(nf, nfgaloisapply(nf, aut, P[2]), P[1])
%8 = 1

The surprising failure of the equality test (%7) is due to the fact that although the corresponding prime ideals are equal, their representations are not. (A prime ideal is specified by a uniformizer, and there is no guarantee that applying automorphisms yields the same elements as a direct ide-

alprimedec call.)

The automorphism can also be given as a column vector, representing the image of Mod(x, nf.pol) as an algebraic number. This last representation is more efficient and should be preferred if a given automorphism must be used in many such calls.

? nf = nfinit(x^3 - 37*x^2 + 74*x - 37);
? l = nfgaloisconj(nf); aut = l[2] \\ automorphisms in basistoalg form
%2 = -31/11*x^2 + 1109/11*x - 925/11
? L = matalgtobasis(nf, l); AUT = L[2] \\ same in algtobasis form
%3 = [16, -6, 5]~
? v = [1, 2, 3]~; nfgaloisapply(nf, aut, v) == nfgaloisapply(nf, AUT, v)

%4 = 1 \\ same result...

? for (i=1,10^5, nfgaloisapply(nf, aut, v))
time = 1,451 ms.
? for (i=1,10^5, nfgaloisapply(nf, AUT, v))

time = 1,045 ms. \\ but the latter is faster

The library syntax is GEN galoisapply(GEN nf, GEN aut, GEN x).

3.6.101 nfgaloisconj(nf , {flag = 0}, {d}). nf being a number field as output by nfinit, computes the conjugates of a root r of the non-constant polynomial x = nf [1] expressed as polynomials in r. This also makes sense when the number field is not Galois since some conjugates may lie in the field. nf can simply be a polynomial.

If no flags or flag = 0, use a combination of flag 4 and 1 and the result is always complete. There is no point whatsoever in using the other flags.

If flag = 1, use nfroots: a little slow, but guaranteed to work in polynomial time.

If flag = 2 (OBSOLETE), use complex approximations to the roots and an integral LLL. The result is not guaranteed to be complete: some conjugates may be missing (a warning is issued if the result is not proved complete), especially so if the corresponding polynomial has a huge index, and increasing the default precision may help. This variant is slow and unreliable: don’t use it.

If flag = 4, use galoisinit: very fast, but only applies to (most) Galois fields. If the field is Galois with weakly super-solvable Galois group (see galoisinit), return the complete list of automorphisms, else only the identity element. If present, d is assumed to be a multiple of the least common denominator of the conjugates expressed as polynomial in a root of pol.

This routine can only compute Q-automorphisms, but it may be used to get K-automorphism for any base field K as follows:

    rnfgaloisconj(nfK, R) = \\ K-automorphisms of L = K[X] / (R)
    { my(polabs, N);
      R *= Mod(1, nfK.pol);             \\ convert coeffs to polmod elts of K
      polabs = rnfequation(nfK, R);
      N = nfgaloisconj(polabs) % R;     \\ Q-automorphisms of L
      \\ select the ones that fix K
      select(s->subst(R, variable(R), Mod(s,R)) == 0, N);
    }
    K  = nfinit(y^2 + 7);
    rnfgaloisconj(K, x^4 - y*x^3 - 3*x^2 + y*x + 1)  \\ K-automorphisms of L
    The library syntax is GEN galoisconj0(GEN nf, long flag, GEN d = NULL, long prec).

Use directly GEN galoisconj(GEN nf, GEN d), corresponding to flag = 0, the others only have historical interest.

3.6.102 nfhilbert(nf , a, b, {pr}). If pr is omitted, compute the global quadratic Hilbert symbol (a, b) in nf , that is 1 if x^{2} − ay^{2} − bz^{2} has a non trivial solution (x, y, z) in nf , and −1 otherwise. Otherwise compute the local symbol modulo the prime ideal pr, as output by idealprimedec.

    The library syntax is long nfhilbert0(GEN nf, GEN a, GEN b, GEN pr = NULL).

Also available is long nfhilbert(GEN bnf,GEN a,GEN b) (global quadratic Hilbert symbol).

3.6.103 nfhnf(nf , x). Given a pseudo-matrix (A, I), finds a pseudo-basis in Hermite normal form of the module it generates.

The library syntax is GEN nfhnf(GEN nf, GEN x). Also available:

GEN rnfsimplifybasis(GEN bnf, GEN x) simplifies the pseudo-basis given by x = (A, I). The ideals in the list I are integral, primitive and either trivial (equal to the full ring of integer) or non-principal.

3.6.104 nfhnfmod(nf , x, detx). Given a pseudo-matrix (A, I) and an ideal detx which is contained in (read integral multiple of) the determinant of (A, I), finds a pseudo-basis in Hermite normal form of the module generated by (A, I). This avoids coefficient explosion. detx can be computed using the function nfdetint.

    The library syntax is GEN nfhnfmod(GEN nf, GEN x, GEN detx).

3.6.105 nfinit(pol, {flag = 0}). pol being a non-constant, preferably monic, irreducible polynomial in Z[X], initializes a number field structure (nf) associated to the field K defined by pol. As such, it’s a technical object passed as the first argument to most nfxxx functions, but it contains some information which may be directly useful. Access to this information via member functions is preferred since the specific data organization specified below may change in the future. Currently, nf is a row vector with 9 components:

nf [1] contains the polynomial pol (nf .pol).

nf [2] contains [r1, r2] (nf .sign, nf .r1, nf .r2), the number of real and complex places of K.

nf [3] contains the discriminant d(K) (nf .disc) of K.

nf [4] contains the index of nf [1] (nf .index), i.e. [Z_{K} : Z[θ]], where θ is any root of nf [1].

nf [5] is a vector containing 7 matrices M, G, roundG, T, MD, T I, MDI useful for certain computations in the number field K.

• M is the (r1+r2)×n matrix whose columns represent the numerical values of the conjugates of the elements of the integral basis.

• G is an n × n matrix such that T 2 = ^{t}GG, where T 2 is the quadratic form T_{2}(x) = ∑ |σ(x)|^{2}, σ running over the embeddings of K into C.

• roundG is a rescaled copy of G, rounded to nearest integers.

• T is the n × n matrix whose coefficients are Tr(ω_{i}ω_{j}) where the ω_{i} are the elements of the integral basis. Note also that det(T) is equal to the discriminant of the field K. Also, when understood as an ideal, the matrix T^{−1} generates the codifferent ideal.

• The columns of MD (nf .diff) express a Z-basis of the different of K on the integral basis.

• T I is equal to the primitive part of T^{−1}, which has integral coefficients.

• Finally, MDI is a two-element representation (for faster ideal product) of d(K) times the codifferent ideal (nf .disc∗nf .codiff, which is an integral ideal). MDI is only used in idealinv.

nf [6] is the vector containing the r1+r2 roots (nf .roots) of nf [1] corresponding to the r1+r2 embeddings of the number field into C (the first r1 components are real, the next r2 have positive imaginary part).

nf [7] is an integral basis for Z_{K} (nf .zk) expressed on the powers of θ. Its first element is guaranteed to be 1. This basis is LLL-reduced with respect to T_{2} (strictly speaking, it is a permutation of such a basis, due to the condition that the first element be 1).

nf [8] is the n × n integral matrix expressing the power basis in terms of the integral basis, and finally

nf [9] is the n × n^{2} matrix giving the multiplication table of the integral basis.

If a non monic polynomial is input, nfinit will transform it into a monic one, then reduce it (see flag = 3). It is allowed, though not very useful given the existence of nfnewprec, to input a nf or a bnf instead of a polynomial.

    ? nf = nfinit(x^3 - 12); \\ initialize number field Q[X] / (X^3 - 12)
    ? nf.pol   \\ defining polynomial
    %2 = x^3 - 12

? nf.disc  \\ field discriminant
%3 = -972
? nf.index \\ index of power basis order in maximal order
%4 = 2
? nf.zk    \\ integer basis, lifted to Q[X]
%5 = [1, x, 1/2*x^2]
? nf.sign  \\ signature
%6 = [1, 1]
? factor(abs(nf.disc ))  \\ determines ramified primes
%7 =
[2 2]
[3 5]
? idealfactor(nf, 2)
%8 =
[[2, [0, 0, -1]~, 3, 1, [0, 1, 0]~] 3]  \\ 𝔭_{2}^{3}

Huge discriminants, helping nfdisc.

In case pol has a huge discriminant which is difficult to factor, it is hard to compute from scratch the maximal order. The special input format [pol, B] is also accepted where pol is a polynomial as above and B has one of the following forms

• an integer basis, as would be computed by nfbasis: a vector of polynomials with first element 1. This is useful if the maximal order is known in advance.

• an argument listP which specifies a list of primes (see nfbasis). Instead of the maximal order, nfinit then computes an order which is maximal at these particular primes as well as the primes contained in the private prime table (see addprimes). The result is unconditionaly correct when the discriminant nf.disc factors completely over this set of primes. The function nfcertify automates this:

? pol = polcompositum(x^5 - 101, polcyclo(7))[1];
? nf = nfinit( [pol, 10^3] );
? nfcertify(nf)
%3 = []

A priori, nf.zk defines an order which is only known to be maximal at all primes ≤ 10^{3} (no prime ≤ 10^{3} divides nf.index). The certification step proves the correctness of the computation.

If flag = 2: pol is changed into another polynomial P defining the same number field, which is as simple as can easily be found using the polredbest algorithm, and all the subsequent computations are done using this new polynomial. In particular, the first component of the result is the modified polynomial.

If flag = 3, apply polredbest as in case 2, but outputs [nf , Mod(a, P)], where nf is as before and Mod(a, P) = Mod(x, pol) gives the change of variables. This is implicit when pol is not monic: first a linear change of variables is performed, to get a monic polynomial, then polredbest.

The library syntax is GEN nfinit0(GEN pol, long flag, long prec). Also available are
GEN nfinit(GEN x, long prec) (flag = 0), GEN nfinitred(GEN x, long prec) (flag = 2), GEN

nfinitred2(GEN x, long prec) (flag = 3). Instead of the above hardcoded numerical flags in nfinit0, one should rather use

GEN nfinitall(GEN x, long flag, long prec), where flag is an or-ed combination of

• nf_RED: find a simpler defining polynomial,

• nf_ORIG: if nf_RED set, also return the change of variable,

• nf_ROUND2: Deprecated. Slow down the routine by using an obsolete normalization algorithm (do not use this one!),

• nf_PARTIALFACT: Deprecated. Lazy factorization of the polynomial discriminant. Result is conditional unless nfcertify can certify it.

3.6.106 nfisideal(nf , x). Returns 1 if x is an ideal in the number field nf , 0 otherwise.

    The library syntax is long isideal(GEN nf, GEN x).

3.6.107 nfisincl(x, y). Tests whether the number field K defined by the polynomial x is conjugate to a subfield of the field L defined by y (where x and y must be in Q[X]). If they are not, the output is the number 0. If they are, the output is a vector of polynomials, each polynomial a representing an embedding of K into L, i.e. being such that y | x ∘ a.

If y is a number field (nf ), a much faster algorithm is used (factoring x over y using nffactor). Before version 2.0.14, this wasn’t guaranteed to return all the embeddings, hence was triggered by a special flag. This is no more the case.

    The library syntax is GEN nfisincl(GEN x, GEN y).

3.6.108 nfisisom(x, y). As nfisincl, but tests for isomorphism. If either x or y is a number field, a much faster algorithm will be used.

    The library syntax is GEN nfisisom(GEN x, GEN y).

3.6.109 nfkermodpr(nf , x, pr). Kernel of the matrix a in Z_{K}/pr, where pr is in modpr format

(see nfmodprinit).

The library syntax is GEN nfkermodpr(GEN nf, GEN x, GEN pr). This function is normally useless in library mode. Project your inputs to the residue field using nfM to FqM, then work there.

3.6.110 nfmodprinit(nf , pr). Transforms the prime ideal pr into modpr format necessary for all operations modulo pr in the number field nf .

    The library syntax is GEN nfmodprinit(GEN nf, GEN pr).

3.6.111 nfnewprec(nf ). Transforms the number field nf into the corresponding data using current (usually larger) precision. This function works as expected if nf is in fact a bnf (update bnf to current precision) but may be quite slow (many generators of principal ideals have to be computed).

    The library syntax is GEN nfnewprec(GEN nf, long prec). See also GEN bnfnewprec(GEN
bnf, long prec) and GEN bnrnewprec(GEN bnr, long prec).

3.6.112 nfroots({nf }, x). Roots of the polynomial x in the number field nf given by nfinit without multiplicity (in Q if nf is omitted). x has coefficients in the number field (scalar, polmod, polynomial, column vector). The main variable of nf must be of lower priority than that of x (see Section 2.5.3). However if the coefficients of the number field occur explicitly (as polmods) as coefficients of x, the variable of these polmods must be the same as the main variable of t (see

nffactor).

It is possible to input a defining polynomial for nf instead, but this is in general less efficient since parts of an nf structure will be computed internally. This is useful in two situations: when you don’t need the nf, or when you can’t compute its discriminant due to integer factorization difficulties. In the latter case, addprimes is a possibility but a dangerous one: roots will probably be missed if the (true) field discriminant and an addprimes entry are strictly divisible by some prime. If you have such an unsafe nf , it is safer to input nf.pol.

    The library syntax is GEN nfroots(GEN nf = NULL, GEN x). See also GEN nfrootsQ(GEN

x), corresponding to nf = NULL.

3.6.113 nfrootsof1(nf ). Returns a two-component vector [w, z] where w is the number of roots of unity in the number field nf , and z is a primitive w-th root of unity.

    ? K = nfinit(polcyclo(11));
    ? nfrootsof1(K)
    %2 = [22, [0, 0, 0, 0, 0, -1, 0, 0, 0, 0]~]
    ? z = nfbasistoalg(K, %[2])   \\ in algebraic form
    %3 = Mod(-x^5, x^10 + x^9 + x^8 + x^7 + x^6 + x^5 + x^4 + x^3 + x^2 + x + 1)
    ? [lift(z^11), lift(z^2)]     \\ proves that the order of z is 22
    %4 = [-1, -x^9 - x^8 - x^7 - x^6 - x^5 - x^4 - x^3 - x^2 - x - 1]

This function guesses the number w as the gcd of the #k(v)^{∗} for unramified v above odd primes, then computes the roots in nf of the w-th cyclotomic polynomial: the algorithm is polynomial time with respect to the field degree and the bitsize of the multiplication table in nf (both of them polynomially bounded in terms of the size of the discriminant). Fields of degree up to 100 or so should require less than one minute.

    The library syntax is GEN rootsof1(GEN nf). Also available is GEN rootsof1_kannan(GEN

nf), that computes all algebraic integers of T_{2} norm equal to the field degree (all roots of 1, by Kronecker’s theorem). This is in general a little faster than the default when there are roots of 1 in the field (say twice faster), but can be much slower (say, days slower), since the algorithm is a priori exponential in the field degree.

3.6.114 nfsnf(nf , x). Given a Z_{K}-module x associated to the integral pseudo-matrix (A, I, J), returns an ideal list d_{1}, …, d_{n} which is the Smith normal form of x. In other words, x is isomorphic to Z_{K}/d_{1} ⊕ ⋯ ⊕ Z_{K}/d_{n} and d_{i} divides d_{i−1} for i ≥ 2.

See Section 3.6.4.1 for the definition of integral pseudo-matrix; briefly, it is input as a 3component row vector [A, I, J] where I = [b_{1}, …, b_{n}] and J = [a_{1}, …, a_{n}] are two ideal lists, and A is a square n × n matrix with columns (A_{1}, …, A_{n}), seen as elements in K^{n} (with canonical basis (e_{1}, …, e_{n})). This data defines the Z_{K} module x given by

(b_{1}e_{1} ⊕ ⋯ ⊕ b_{n}e_{n})/(a_{1}A_{1} ⊕ ⋯ ⊕ a_{n}A_{n}) ,

The integrality condition is a_{i,j} ∈ b_{i}a_{j}^{−1} for all i, j. If it is not satisfied, then the d_{i} will not be integral. Note that every finitely generated torsion module is isomorphic to a module of this form and even with b_{i} = Z_{K} for all i.

    The library syntax is GEN nfsnf(GEN nf, GEN x).

3.6.115 nfsolvemodpr(nf , a, b, P). Let P be a prime ideal in modpr format (see nfmodprinit), let a be a matrix, invertible over the residue field, and let b be a column vector or matrix. This function returns a solution of a · x = b; the coefficients of x are lifted to nf elements.

    ? K = nfinit(y^2+1);
    ? P = idealprimedec(K, 3)[1];
    ? P = nfmodprinit(K, P);
    ? a = [y+1, y; y, 0]; b = [1, y]~
    ? nfsolvemodpr(K, a,b, P)
    %5 = [1, 2]~
    The library syntax is GEN nfsolvemodpr(GEN nf, GEN a, GEN b, GEN P). This function is

normally useless in library mode. Project your inputs to the residue field using nfM to FqM, then work there.

3.6.116 nfsubfields(pol, {d = 0}). Finds all subfields of degree d of the number field defined by the (monic, integral) polynomial pol (all subfields if d is null or omitted). The result is a vector of subfields, each being given by [g, h], where g is an absolute equation and h expresses one of the roots of g in terms of the root x of the polynomial defining nf . This routine uses J. Klüners’s algorithm in the general case, and B. Allombert’s galoissubfields when nf is Galois (with weakly supersolvable Galois group).

    The library syntax is GEN nfsubfields(GEN pol, long d).

3.6.117 polcompositum(P, Q, {flag = 0}). P and Q being squarefree polynomials in Z[X] in the same variable, outputs the simple factors of the étale Q-algebra A = Q(X, Y)/(P (X), Q(Y)). The factors are given by a list of polynomials R in Z[X], associated to the number field Q(X)/(R), and sorted by increasing degree (with respect to lexicographic ordering for factors of equal degrees). Returns an error if one of the polynomials is not squarefree.

Note that it is more efficient to reduce to the case where P and Q are irreducible first. The routine will not perform this for you, since it may be expensive, and the inputs are irreducible in most applications anyway. In this case, there will be a single factor R if and only if the number fields defined by P and Q are disjoint.

Assuming P is irreducible (of smaller degree than Q for efficiency), it is in general much faster to proceed as follows

    nf = nfinit(P); L = nffactor(nf, Q)[,1];
    vector(#L, i, rnfequation(nf, L[i]))

to obtain the same result. If you are only interested in the degrees of the simple factors, the

rnfequation instruction can be replaced by a trivial poldegree(P) * poldegree(L[i]).

If flag = 1, outputs a vector of 4-component vectors [R, a, b, k], where R ranges through the list of all possible compositums as above, and a (resp. b) expresses the root of P (resp. Q) as an element of Q(X)/(R). Finally, k is a small integer such that b + ka = X modulo R.

A compositum is often defined by a complicated polynomial, which it is advisable to reduce before further work. Here is an example involving the field Q(ζ_{5}, 5^{1/5}):

    ? L = polcompositum(x^5 - 5, polcyclo(5), 1); \\ list of [R, a, b, k]

? [R, a] = L[1]; \\ pick the single factor, extract R, a (ignore b, k) ? R \\ defines the compositum

%3 = x^20 + 5*x^19 + 15*x^18 + 35*x^17 + 70*x^16 + 141*x^15 + 260*x^14\
+ 355*x^13 + 95*x^12 - 1460*x^11 - 3279*x^10 - 3660*x^9 - 2005*x^8    \
+ 705*x^7 + 9210*x^6 + 13506*x^5 + 7145*x^4 - 2740*x^3 + 1040*x^2     \
- 320*x + 256

? a^5 - 5 \\ a fifth root of 5

%4 = 0
? [T, X] = polredbest(R, 1);

? T \\ simpler defining polynomial for Q[x]/(R)

%6 = x^20 + 25*x^10 + 5

? X \\ root of R in Q[y]/(T (y))

%7 = Mod(-1/11*x^15 - 1/11*x^14 + 1/22*x^10 - 47/22*x^5 - 29/11*x^4 + 7/22,\
x^20 + 25*x^10 + 5)
? a = subst(a.pol, 'x, X)  \\ a in the new coordinates
%8 = Mod(1/11*x^14 + 29/11*x^4, x^20 + 25*x^10 + 5)
? a^5 - 5
%9 = 0
The library syntax is GEN polcompositum0(GEN P, GEN Q, long flag). Also available are
GEN compositum(GEN P, GEN Q) (flag = 0) and GEN compositum2(GEN P, GEN Q) (flag = 1).

3.6.118 polgalois(T). Galois group of the non-constant polynomial T ∈ Q[X]. In the present version 2.7.2, T must be irreducible and the degree d of T must be less than or equal to 7. If the galdata package has been installed, degrees 8, 9, 10 and 11 are also implemented. By definition, if K = Q[x]/(T), this computes the action of the Galois group of the Galois closure of K on the d distinct roots of T, up to conjugacy (corresponding to different root orderings).

The output is a 4-component vector [n, s, k, name] with the following meaning: n is the cardinality of the group, s is its signature (s = 1 if the group is a subgroup of the alternating group A_{d}, s = −1 otherwise) and name is a character string containing name of the transitive group according to the GAP 4 transitive groups library by Alexander Hulpke.

k is more arbitrary and the choice made up to version 2.2.3 of PARI is rather unfortunate: for d > 7, k is the numbering of the group among all transitive subgroups of S_{d}, as given in “The transitive groups of degree up to eleven”, G. Butler and J. McKay, Communications in Algebra, vol. 11, 1983, pp. 863–911 (group k is denoted T_{k} there). And for d ≤ 7, it was ad hoc, so as to ensure that a given triple would denote a unique group. Specifically, for polynomials of degree d ≤ 7, the groups are coded as follows, using standard notations

In degree 1: S_{1} = [1, 1, 1].

In degree 2: S_{2} = [2, −1, 1].

In degree 3: A_{3} = C_{3} = [3, 1, 1], S_{3} = [6, −1, 1].

In degree 4: C_{4} = [4, −1, 1], V_{4} = [4, 1, 1], D_{4} = [8, −1, 1], A_{4} = [12, 1, 1], S_{4} = [24, −1, 1].

In degree 5: C_{5} = [5, 1, 1], D_{5} = [10, 1, 1], M_{20} = [20, −1, 1], A_{5} = [60, 1, 1], S_{5} = [120, −1, 1].

In degree 6: C_{6} = [6, −1, 1], S_{3} = [6, −1, 2], D_{6} = [12, −1, 1], A_{4} = [12, 1, 1], G_{18} = [18, −1, 1], S_{4}^{−} = [24, −1, 1], A_{4} × C_{2} = [24, −1, 2], S_{4}^{+} = [24, 1, 1], G_{36}^{−} = [36, −1, 1], G_{36}^{+} = [36, 1, 1], S_{4} × C_{2} = [48, −1, 1], A_{5} = P SL_{2}(5) = [60, 1, 1], G_{72} = [72, −1, 1], S_{5} = P GL_{2}(5) = [120, −1, 1], A_{6} = [360, 1, 1], S_{6} = [720, −1, 1].

In degree 7: C_{7} = [7, 1, 1], D_{7} = [14, −1, 1], M_{21} = [21, 1, 1], M_{42} = [42, −1, 1], P SL_{2}(7) = P SL_{3}(2) = [168, 1, 1], A_{7} = [2520, 1, 1], S_{7} = [5040, −1, 1].

This is deprecated and obsolete, but for reasons of backward compatibility, we cannot change this behavior yet. So you can use the default new_galois_format to switch to a consistent naming scheme, namely k is always the standard numbering of the group among all transitive subgroups of S_{n}. If this default is in effect, the above groups will be coded as:

In degree 1: S_{1} = [1, 1, 1].
In degree 2: S_{2} = [2, −1, 1].
In degree 3: A_{3} = C_{3} = [3, 1, 1], S_{3} = [6, −1, 2].
In degree 4: C_{4} = [4, −1, 1], V_{4} = [4, 1, 2], D_{4} = [8, −1, 3], A_{4} = [12, 1, 4], S_{4} = [24, −1, 5].

In degree 5: C_{5} = [5, 1, 1], D_{5} = [10, 1, 2], M_{20} = [20, −1, 3], A_{5} = [60, 1, 4], S_{5} = [120, −1, 5].

In degree 6: C_{6} = [6, −1, 1], S_{3} = [6, −1, 2], D_{6} = [12, −1, 3], A_{4} = [12, 1, 4], G_{18} = [18, −1, 5], A_{4} × C_{2} = [24, −1, 6], S_{4}^{+} = [24, 1, 7], S_{4}^{−} = [24, −1, 8], G_{36}^{−} = [36, −1, 9], G_{36}^{+} = [36, 1, 10], S_{4} × C_{2} = [48, −1, 11], A_{5} = P SL_{2}(5) = [60, 1, 12], G_{72} = [72, −1, 13], S_{5} = P GL_{2}(5) = [120, −1, 14], A_{6} = [360, 1, 15], S_{6} = [720, −1, 16].

In degree 7: C_{7} = [7, 1, 1], D_{7} = [14, −1, 2], M_{21} = [21, 1, 3], M_{42} = [42, −1, 4], P SL_{2}(7) = P SL_{3}(2) = [168, 1, 5], A_{7} = [2520, 1, 6], S_{7} = [5040, −1, 7].

Warning. The method used is that of resolvent polynomials and is sensitive to the current precision. The precision is updated internally but, in very rare cases, a wrong result may be returned if the initial precision was not sufficient.

The library syntax is GEN polgalois(GEN T, long prec). To enable the new format in library mode, set the global variable new_galois_format to 1.

3.6.119 polred(T, {flag = 0}). This function is deprecated, use polredbest instead. Finds polynomials with reasonably small coefficients defining subfields of the number field defined by T. One of the polynomials always defines Q (hence is equal to x − 1), and another always defines the same number field as T if T is irreducible.

All T accepted by nfinit are also allowed here; in particular, the format [T, listP] is recommended, e.g. with listP = 10^{5} or a vector containing all ramified primes. Otherwise, the maximal order of Q[x]/(T) must be computed.

The following binary digits of flag are significant:

1: Possibly use a suborder of the maximal order. The primes dividing the index of the order chosen are larger than primelimit or divide integers stored in the addprimes table. This flag is deprecated, the [T, listP] format is more flexible.

2: gives also elements. The result is a two-column matrix, the first column giving primitive elements defining these subfields, the second giving the corresponding minimal polynomials.

    ? M = polred(x^4 + 8, 2)
    %1 =
    [1 x - 1]
    [1/2*x^2 x^2 + 2]
    [1/4*x^3 x^4 + 2]
    [x x^4 + 8]
    ? minpoly(Mod(M[2,1], x^4+8))
    %2 = x^2 + 2

The library syntax is polred(GEN T) (flag = 0). Also available is GEN polred2(GEN T) (flag = 2). The function polred0 is deprecated, provided for backward compatibility.

3.6.120 polredabs(T, {flag = 0}). Returns a canonical defining polynomial P for the number field Q[X]/(T) defined by T, such that the sum of the squares of the modulus of the roots (i.e. the T_{2}-norm) is minimal. Different T defining isomorphic number fields will yield the same P. All T accepted by nfinit are also allowed here, e.g. non-monic polynomials, or pairs [T, listP] specifying that a non-maximal order may be used.

Warning 1. Using a t_POL T requires fully factoring the discriminant of T, which may be very hard. The format [T, listP] computes only a suborder of the maximal order and replaces this part of the algorithm by a polynomial time computation. In that case the polynomial P is a priori no longer canonical, and it may happen that it does not have minimal T_{2} norm. The routine attempts to certify the result independently of this order computation (as per nfcertify: we try to prove that the order is maximal); if it fails, the routine returns 0 instead of P. In order to force an output in that case as well, you may either use polredbest, or polredabs(,16), or

      polredabs([T, nfbasis([T, listP])])

(In all three cases, the result is no longer canonical.)

Warning 2. Apart from the factorization of the discriminant of T, this routine runs in polynomial time for a fixed degree. But the complexity is exponential in the degree: this routine may be exceedingly slow when the number field has many subfields, hence a lot of elements of small T_{2}- norm. If you do not need a canonical polynomial, the function polredbest is in general much faster (it runs in polynomial time), and tends to return polynomials with smaller discriminants.

The binary digits of flag mean

1: outputs a two-component row vector [P, a], where P is the default output and Mod(a, P) is a root of the original T.

4: gives all polynomials of minimal T_{2} norm; of the two polynomials P (x) and ±P (−x), only one is given.

16: Possibly use a suborder of the maximal order, without attempting to certify the result as in Warning 1: we always return a polynomial and never 0. The result is a priori not canonical.

    ? T = x^16 - 136*x^14 + 6476*x^12 - 141912*x^10 + 1513334*x^8 \
          - 7453176*x^6 + 13950764*x^4 - 5596840*x^2 + 46225
    ? T1 = polredabs(T); T2 = polredbest(T);
    ? [ norml2(polroots(T1)), norml2(polroots(T2)) ]
    %3 = [88.0000000, 120.000000]

? [ sizedigit(poldisc(T1)), sizedigit(poldisc(T2)) ]
%4 = [75, 67]

The library syntax is GEN polredabs0(GEN T, long flag). Instead of the above hardcoded numerical flags, one should use an or-ed combination of

• nf_PARTIALFACT: possibly use a suborder of the maximal order, without attempting to certify the result.

• nf_ORIG: return [P, a], where Mod(a, P) is a root of T.

• nf_RAW: return [P, b], where Mod(b, T) is a root of P. The algebraic integer b is the raw result produced by the small vectors enumeration in the maximal order; P was computed as the characteristic polynomial of Mod(b, T). Mod(a, P) as in nf_ORIG is obtained with modreverse.

• nf_ADDZK: if r is the result produced with some of the above flags (of the form P or [P, c]), return [r,zk], where zk is a Z-basis for the maximal order of Q[X]/(P).

• nf_ALL: return a vector of results of the above form, for all polynomials of minimal T_{2}-norm.

3.6.121 polredbest(T, {flag = 0}). Finds a polynomial with reasonably small coefficients defining the same number field as T. All T accepted by nfinit are also allowed here (e.g. non-monic polynomials, nf, bnf, [T,Z K basis]). Contrary to polredabs, this routine runs in polynomial time, but it offers no guarantee as to the minimality of its result.

This routine computes an LLL-reduced basis for the ring of integers of Q[X]/(T), then examines small linear combinations of the basis vectors, computing their characteristic polynomials. It returns the separable P polynomial of smallest discriminant (the one with lexicographically smallest abs(Vec(P)) in case of ties). This is a good candidate for subsequent number field computations, since it guarantees that the denominators of algebraic integers, when expressed in the power basis, are reasonably small. With no claim of minimality, though.

It can happen that iterating this functions yields better and better polynomials, until it stabilizes:

? \p5
? P = X^12+8*X^8-50*X^6+16*X^4-3069*X^2+625;
? poldisc(P)*1.
%2 = 1.2622 E55
? P = polredbest(P);
? poldisc(P)*1.
%4 = 2.9012 E51
? P = polredbest(P);
? poldisc(P)*1.
%6 = 8.8704 E44

In this example, the initial polynomial P is the one returned by polredabs, and the last one is stable.

If flag = 1: outputs a two-component row vector [P, a], where P is the default output and Mod(a, P) is a root of the original T.

? [P,a] = polredbest(x^4 + 8, 1)
%1 = [x^4 + 2, Mod(x^3, x^4 + 2)]
? charpoly(a)

    %2 = x^4 + 8

In particular, the map Q[x]/(T) → Q[x]/(P), x ↦ Mod(a, P) defines an isomorphism of number fields, which can be computed as

      subst(lift(Q), 'x, a)

if Q is a t_POLMOD modulo T; b = modreverse(a) returns a t_POLMOD giving the inverse of the above map (which should be useless since Q[x]/(P) is a priori a better representation for the number field and its elements).

    The library syntax is GEN polredbest(GEN T, long flag).

3.6.122 polredord(x). Finds polynomials with reasonably small coefficients and of the same degree as that of x defining suborders of the order defined by x. One of the polynomials always defines Q (hence is equal to (x − 1)^{n}, where n is the degree), and another always defines the same order as x if x is irreducible. Useless function: try polredbest.

The library syntax is GEN polredord(GEN x).

3.6.123 poltschirnhaus(x). Applies a random Tschirnhausen transformation to the polynomial x, which is assumed to be non-constant and separable, so as to obtain a new equation for the étale algebra defined by x. This is for instance useful when computing resolvents, hence is used by the

polgalois function.
    The library syntax is GEN tschirnhaus(GEN x).

3.6.124 rnfalgtobasis(rnf , x). Expresses x on the relative integral basis. Here, rnf is a relative number field extension L/K as output by rnfinit, and x an element of L in absolute form, i.e. expressed as a polynomial or polmod with polmod coefficients, not on the relative integral basis.

    The library syntax is GEN rnfalgtobasis(GEN rnf, GEN x).

3.6.125 rnfbasis(bnf , M). Let K the field represented by bnf , as output by bnfinit. M is a projective Z_{K}-module of rank n (M ⊗ K is an n-dimensional K-vector space), given by a pseudobasis of size n. The routine returns either a true Z_{K}-basis of M (of size n) if it exists, or an n + 1-element generating set of M if not.

It is allowed to use an irreducible polynomial P in K[X] instead of M, in which case, M is defined as the ring of integers of K[X]/(P), viewed as a Z_{K}-module.

    The library syntax is GEN rnfbasis(GEN bnf, GEN M).

3.6.126 rnfbasistoalg(rnf , x). Computes the representation of x as a polmod with polmods coefficients. Here, rnf is a relative number field extension L/K as output by rnfinit, and x an element of L expressed on the relative integral basis.

    The library syntax is GEN rnfbasistoalg(GEN rnf, GEN x).

3.6.127 rnfcharpoly(nf , T, a, {var =′ x}). Characteristic polynomial of a over nf , where a belongs to the algebra defined by T over nf , i.e. nf [X]/(T). Returns a polynomial in variable v (x by default).

    ? nf = nfinit(y^2+1);
    ? rnfcharpoly(nf, x^2+y*x+1, x+y)
    %2 = x^2 + Mod(-y, y^2 + 1)*x + 1
    The library syntax is GEN rnfcharpoly(GEN nf, GEN T, GEN a, long var = -1), where

var is a variable number.

3.6.128 rnfconductor(bnf , pol). Given bnf as output by bnfinit, and pol a relative polynomial defining an Abelian extension, computes the class field theory conductor of this Abelian extension. The result is a 3-component vector [conductor, rayclgp, subgroup], where conductor is the conductor of the extension given as a 2-component row vector [f_{0}, f_{∞}], rayclgp is the full ray class group corresponding to the conductor given as a 3-component vector [h,cyc,gen] as usual for a group, and subgroup is a matrix in HNF defining the subgroup of the ray class group on the given generators gen.

    The library syntax is GEN rnfconductor(GEN bnf, GEN pol).

3.6.129 rnfdedekind(nf , pol, {pr}, {flag = 0}). Given a number field K coded by nf and a monic polynomial P ∈ Z_{K}[X], irreducible over K and thus defining a relative extension L of K, applies Dedekind’s criterion to the order Z_{K}[X]/(P), at the prime ideal pr. It is possible to set pr to a vector of prime ideals (test maximality at all primes in the vector), or to omit altogether, in which case maximality at all primes is tested; in this situation flag is automatically set to 1.

The default historic behavior (flag is 0 or omitted and pr is a single prime ideal) is not so useful since rnfpseudobasis gives more information and is generally not that much slower. It returns a 3-component vector [max, basis, v]:

• basis is a pseudo-basis of an enlarged order O produced by Dedekind’s criterion, containing the original order Z_{K}[X]/(P) with index a power of pr. Possibly equal to the original order.

• max is a flag equal to 1 if the enlarged order O could be proven to be pr-maximal and to 0 otherwise; it may still be maximal in the latter case if pr is ramified in L,

• v is the valuation at pr of the order discriminant.

If flag is non-zero, on the other hand, we just return 1 if the order Z_{K}[X]/(P) is pr-maximal (resp. maximal at all relevant primes, as described above), and 0 if not. This is much faster than the default, since the enlarged order is not computed.

    ? nf = nfinit(y^2-3); P = x^3 - 2*y;
    ? pr3 = idealprimedec(nf,3)[1];
    ? rnfdedekind(nf, P, pr3)
    %2 = [1, [[1, 0, 0; 0, 1, 0; 0, 0, 1], [1, 1, 1]], 8]
    ? rnfdedekind(nf, P, pr3, 1)
    %3 = 1

In this example, pr3 is the ramified ideal above 3, and the order generated by the cube roots of y is already pr3-maximal. The order-discriminant has valuation 8. On the other hand, the order is not maximal at the prime above 2:

    ? pr2 = idealprimedec(nf,2)[1];

? rnfdedekind(nf, P, pr2, 1)
%5 = 0
? rnfdedekind(nf, P, pr2)
%6 = [0, [[2, 0, 0; 0, 1, 0; 0, 0, 1], [[1, 0; 0, 1], [1, 0; 0, 1],
     [1, 1/2; 0, 1/2]]], 2]

The enlarged order is not proven to be pr2-maximal yet. In fact, it is; it is in fact the maximal order:

? B = rnfpseudobasis(nf, P)
%7 = [[1, 0, 0; 0, 1, 0; 0, 0, 1], [1, 1, [1, 1/2; 0, 1/2]],
     [162, 0; 0, 162], -1]
? idealval(nf,B[3], pr2)
%4 = 2

It is possible to use this routine with non-monic P = ∑_{i≤n} a_{i}X^{i} ∈ Z_{K}[X] if flag = 1; in this case, we test maximality of Dedekind’s order generated by

1, a_{n}α, a_{n}α^{2} + a_{n−1}α, …, a_{n}α^{n−1} + a_{n−1}α^{n−2} + ⋯ + a_{1}α.

The routine will fail if P is 0 on the projective line over the residue field Z_{K}/pr (FIXME).

The library syntax is GEN rnfdedekind(GEN nf, GEN pol, GEN pr = NULL, long flag).

3.6.130 rnfdet(nf , M). Given a pseudo-matrix M over the maximal order of nf , computes its determinant.

The library syntax is GEN rnfdet(GEN nf, GEN M).

3.6.131 rnfdisc(nf , pol). Given a number field nf as output by nfinit and a polynomial pol with coefficients in nf defining a relative extension L of nf , computes the relative discriminant of L. This is a two-element row vector [D, d], where D is the relative ideal discriminant and d is the relative discriminant considered as an element of nf^{∗}/nf^{∗}^{2}. The main variable of nf must be of lower priority than that of pol, see Section 2.5.3.

The library syntax is GEN rnfdiscf(GEN nf, GEN pol).

3.6.132 rnfeltabstorel(rnf , x). rnf being a relative number field extension L/K as output by rnfinit and x being an element of L expressed as a polynomial modulo the absolute equation rnf .pol, computes x as an element of the relative extension L/K as a polmod with polmod coefficients.

? K = nfinit(y^2+1); L = rnfinit(K, x^2-y);
? L.pol
%2 = x^4 + 1
? rnfeltabstorel(L, Mod(x, L.pol))
%3 = Mod(x, x^2 + Mod(-y, y^2 + 1))
? rnfeltabstorel(L, Mod(2, L.pol))
%4 = 2
? rnfeltabstorel(L, Mod(x, x^2-y))
 ***   at top-level: rnfeltabstorel(L,Mod
 ***                 ^--------------------
 *** rnfeltabstorel: inconsistent moduli in rnfeltabstorel: x^2-y != x^4+1
The library syntax is GEN rnfeltabstorel(GEN rnf, GEN x).

3.6.133 rnfeltdown(rnf , x). rnf being a relative number field extension L/K as output by rnfinit and x being an element of L expressed as a polynomial or polmod with polmod coefficients, computes x as an element of K as a polmod, assuming x is in K (otherwise a domain error occurs).

? K = nfinit(y^2+1); L = rnfinit(K, x^2-y);
? L.pol
%2 = x^4 + 1
? rnfeltdown(L, Mod(x^2, L.pol))
%3 = Mod(y, y^2 + 1)
? rnfeltdown(L, Mod(y, x^2-y))
%4 = Mod(y, y^2 + 1)
? rnfeltdown(L, Mod(y,K.pol))
%5 = Mod(y, y^2 + 1)
? rnfeltdown(L, Mod(x, L.pol))
 ***   at top-level: rnfeltdown(L,Mod(x,x
 ***                 ^--------------------
 *** rnfeltdown: domain error in rnfeltdown: element not in the base field
The library syntax is GEN rnfeltdown(GEN rnf, GEN x).

3.6.134 rnfeltnorm(rnf , x). rnf being a relative number field extension L/K as output by rnfinit and x being an element of L, returns the relative norm N_{L/K}(x) as an element of K.

? K = nfinit(y^2+1); L = rnfinit(K, x^2-y);
? rnfeltnorm(L, Mod(x, L.pol))
%2 = Mod(x, x^2 + Mod(-y, y^2 + 1))
? rnfeltnorm(L, 2)
%3 = 4
? rnfeltnorm(L, Mod(x, x^2-y))
The library syntax is GEN rnfeltnorm(GEN rnf, GEN x).

3.6.135 rnfeltreltoabs(rnf , x). rnf being a relative number field extension L/K as output by rnfinit and x being an element of L expressed as a polynomial or polmod with polmod coefficients, computes x as an element of the absolute extension L/Q as a polynomial modulo the absolute equation rnf .pol.

? K = nfinit(y^2+1); L = rnfinit(K, x^2-y);
? L.pol
%2 = x^4 + 1
? rnfeltreltoabs(L, Mod(x, L.pol))
%3 = Mod(x, x^4 + 1)
? rnfeltreltoabs(L, Mod(y, x^2-y))
%4 = Mod(x^2, x^4 + 1)
? rnfeltreltoabs(L, Mod(y,K.pol))
%5 = Mod(x^2, x^4 + 1)
The library syntax is GEN rnfeltreltoabs(GEN rnf, GEN x).

3.6.136 rnfelttrace(rnf , x). rnf being a relative number field extension L/K as output by rnfinit and x being an element of L, returns the relative trace N_{L/K}(x) as an element of K.

? K = nfinit(y^2+1); L = rnfinit(K, x^2-y);
? rnfelttrace(L, Mod(x, L.pol))
%2 = 0
? rnfelttrace(L, 2)
%3 = 4
? rnfelttrace(L, Mod(x, x^2-y))
The library syntax is GEN rnfelttrace(GEN rnf, GEN x).

3.6.137 rnfeltup(rnf , x). rnf being a relative number field extension L/K as output by rnfinit and x being an element of K, computes x as an element of the absolute extension L/Q as a polynomial modulo the absolute equation rnf .pol.

? K = nfinit(y^2+1); L = rnfinit(K, x^2-y);
? L.pol
%2 = x^4 + 1
? rnfeltup(L, Mod(y, K.pol))
%4 = Mod(x^2, x^4 + 1)
? rnfeltup(L, y)
%5 = Mod(x^2, x^4 + 1)
? rnfeltup(L, [1,2]~) \\ in terms of K.zk
%6 = Mod(2*x^2 + 1, x^4 + 1)
The library syntax is GEN rnfeltup(GEN rnf, GEN x).

3.6.138 rnfequation(nf , pol, {flag = 0}). Given a number field nf as output by nfinit (or simply a polynomial) and a polynomial pol with coefficients in nf defining a relative extension L of nf , computes an absolute equation of L over Q.

The main variable of nf must be of lower priority than that of pol (see Section 2.5.3). Note that for efficiency, this does not check whether the relative equation is irreducible over nf , but only if it is squarefree. If it is reducible but squarefree, the result will be the absolute equation of the étale algebra defined by pol. If pol is not squarefree, raise an e DOMAIN exception.

? rnfequation(y^2+1, x^2 - y)
%1 = x^4 + 1
? T = y^3-2; rnfequation(nfinit(T), (x^3-2)/(x-Mod(y,T)))
%2 = x^6 + 108  \\ Galois closure of Q(2^(1/3))

If flag is non-zero, outputs a 3-component row vector [z, a, k], where

• z is the absolute equation of L over Q, as in the default behavior,

• a expresses as a t_POLMOD modulo z a root α of the polynomial defining the base field nf ,

• k is a small integer such that θ = β + kα is a root of z, where β is a root of pol.

? T = y^3-2; pol = x^2 +x*y + y^2;
? [z,a,k] = rnfequation(T, pol, 1);
? z
%4 = x^6 + 108

? subst(T, y, a)
%5 = 0
? alpha= Mod(y, T);
? beta = Mod(x*Mod(1,T), pol);
? subst(z, x, beta + k*alpha)
%8 = 0
The library syntax is GEN rnfequation0(GEN nf, GEN pol, long flag). Also available
are GEN rnfequation(GEN nf, GEN pol) (flag = 0) and GEN rnfequation2(GEN nf, GEN pol)

(flag = 1).

3.6.139 rnfhnfbasis(bnf , x). Given bnf as output by bnfinit, and either a polynomial x with coefficients in bnf defining a relative extension L of bnf , or a pseudo-basis x of such an extension, gives either a true bnf -basis of L in upper triangular Hermite normal form, if it exists, and returns 0 otherwise.

The library syntax is GEN rnfhnfbasis(GEN bnf, GEN x).

3.6.140 rnfidealabstorel(rnf , x). Let rnf be a relative number field extension L/K as output by rnfinit and x be an ideal of the absolute extension L/Q given by a Z-basis of elements of L. Returns the relative pseudo-matrix in HNF giving the ideal x considered as an ideal of the relative extension L/K, i.e. as a Z_{K}-module.

The reason why the input does not use the customary HNF in terms of a fixed Z-basis for Z_{L} is precisely that no such basis has been explicitly specified. On the other hand, if you already computed an (absolute) nf structure Labs associated to L, and m is in HNF, defining an (absolute) ideal with respect to the Z-basis Labs.zk, then Labs.zk * m is a suitable Z-basis for the ideal, and

  rnfidealabstorel(rnf, Labs.zk * m)

converts m to a relative ideal.

? K = nfinit(y^2+1); L = rnfinit(K, x^2-y); Labs = nfinit(L.pol);
? m = idealhnf(Labs, 17, x^3+2);
? B = rnfidealabstorel(L, Labs.zk * m)
%3 = [[1, 8; 0, 1], [[17, 4; 0, 1], 1]]  \\ pseudo-basis for m as Z_K-module
? A = rnfidealreltoabs(L, B)
%4 = [17, x^2 + 4, x + 8, x^3 + 8*x^2]   \\ Z-basis for m in Q[x]/(L.pol)
? mathnf(matalgtobasis(Labs, A))
%5 =
[17 8 4 2]
[ 0 1 0 0]
[ 0 0 1 0]
[ 0 0 0 1]
? % == m
%6 = 1
The library syntax is GEN rnfidealabstorel(GEN rnf, GEN x).

3.6.141 rnfidealdown(rnf , x). Let rnf be a relative number field extension L/K as output by rnfinit, and x an ideal of L, given either in relative form or by a Z-basis of elements of L (see Section 3.6.140). This function returns the ideal of K below x, i.e. the intersection of x with K.

    The library syntax is GEN rnfidealdown(GEN rnf, GEN x).

3.6.142 rnfidealhnf(rnf , x). rnf being a relative number field extension L/K as output by rnfinit and x being a relative ideal (which can be, as in the absolute case, of many different types, including of course elements), computes the HNF pseudo-matrix associated to x, viewed as a Z_{K}- module.

    The library syntax is GEN rnfidealhnf(GEN rnf, GEN x).

3.6.143 rnfidealmul(rnf , x, y). rnf being a relative number field extension L/K as output by rnfinit and x and y being ideals of the relative extension L/K given by pseudo-matrices, outputs the ideal product, again as a relative ideal.

    The library syntax is GEN rnfidealmul(GEN rnf, GEN x, GEN y).

3.6.144 rnfidealnormabs(rnf , x). Let rnf be a relative number field extension L/K as output by rnfinit and let x be a relative ideal (which can be, as in the absolute case, of many different types, including of course elements). This function computes the norm of the x considered as an ideal of the absolute extension L/Q. This is identical to

       idealnorm(rnf, rnfidealnormrel(rnf,x))

but faster.

    The library syntax is GEN rnfidealnormabs(GEN rnf, GEN x).

3.6.145 rnfidealnormrel(rnf , x). Let rnf be a relative number field extension L/K as output by rnfinit and let x be a relative ideal (which can be, as in the absolute case, of many different types, including of course elements). This function computes the relative norm of x as an ideal of K in HNF.

    The library syntax is GEN rnfidealnormrel(GEN rnf, GEN x).

3.6.146 rnfidealreltoabs(rnf , x). Let rnf be a relative number field extension L/K as output by rnfinit and let x be a relative ideal, given as a Z_{K}-module by a pseudo matrix [A, I]. This function returns the ideal x as an absolute ideal of L/Q in the form of a Z-basis, given by a vector of polynomials (modulo rnf.pol).

The reason why we do not return the customary HNF in terms of a fixed Z-basis for Z_{L} is precisely that no such basis has been explicitly specified. On the other hand, if you already computed an (absolute) nf structure Labs associated to L, then

      xabs = rnfidealreltoabs(L, x);
      xLabs = mathnf(matalgtobasis(Labs, xabs));

computes a traditional HNF xLabs for x in terms of the fixed Z-basis Labs.zk.

    The library syntax is GEN rnfidealreltoabs(GEN rnf, GEN x).

3.6.147 rnfidealtwoelt(rnf , x). rnf being a relative number field extension L/K as output by rnfinit and x being an ideal of the relative extension L/K given by a pseudo-matrix, gives a vector of two generators of x over Z_{L} expressed as polmods with polmod coefficients.

    The library syntax is GEN rnfidealtwoelement(GEN rnf, GEN x).

3.6.148 rnfidealup(rnf , x). Let rnf be a relative number field extension L/K as output by rnfinit and let x be an ideal of K. This function returns the ideal xZ_{L} as an absolute ideal of L/Q, in the form of a Z-basis, given by a vector of polynomials (modulo rnf.pol).

The reason why we do not return the customary HNF in terms of a fixed Z-basis for Z_{L} is precisely that no such basis has been explicitly specified. On the other hand, if you already computed an (absolute) nf structure Labs associated to L, then

      xabs = rnfidealup(L, x);
      xLabs = mathnf(matalgtobasis(Labs, xabs));

computes a traditional HNF xLabs for x in terms of the fixed Z-basis Labs.zk.

    The library syntax is GEN rnfidealup(GEN rnf, GEN x).

3.6.149 rnfinit(nf , pol). nf being a number field in nfinit format considered as base field, and pol a polynomial defining a relative extension over nf , this computes data to work in the relative extension. The main variable of pol must be of higher priority (see Section 2.5.3) than that of nf , and the coefficients of pol must be in nf .

The result is a row vector, whose components are technical. In the following description, we let K be the base field defined by nf and L/K the large field associated to the rnf . Furthermore, we let m = [K : Q] the degree of the base field, n = [L : K] the relative degree, r_{1} and r_{2} the number of real and complex places of K. Access to this information via member functions is preferred since the specific data organization specified below will change in the future.

rnf [1](rnf.pol) contains the relative polynomial pol.

rnf [2] contains the integer basis [A, d] of K, as (integral) elements of L/Q. More precisely, A is a vector of polynomial with integer coefficients, d is a denominator, and the integer basis is given by A/d.

rnf [3] (rnf.disc) is a two-component row vector [𝔡(L/K), s] where 𝔡(L/K) is the relative ideal discriminant of L/K and s is the discriminant of L/K viewed as an element of K^{∗}/(K^{∗})^{2}, in other words it is the output of rnfdisc.

rnf [4](rnf.index) is the ideal index 𝔣, i.e. such that d(pol)Z_{K} = 𝔣^{2}𝔡(L/K).

rnf [5] is currently unused.

rnf [6] is currently unused.

rnf [7] (rnf.zk) is the pseudo-basis (A, I) for the maximal order Z_{L} as a Z_{K}-module: A is the relative integral pseudo basis expressed as polynomials (in the variable of pol) with polmod coefficients in nf , and the second component I is the ideal list of the pseudobasis in HNF.

rnf [8] is the inverse matrix of the integral basis matrix, with coefficients polmods in nf .

rnf [9] is currently unused.

rnf [10] (rnf.nf) is nf .

rnf [11] is the output of rnfequation(K, pol, 1). Namely, a vector [P, a, k] describing the absolute extension L/Q: P is an absolute equation, more conveniently obtained as rnf.polabs; a expresses the generator α = y mod K.pol of the number field K as an element of L, i.e. a polynomial modulo the absolute equation P;

k is a small integer such that, if β is an abstract root of pol and α the generator of K given above, then P (β + kα) = 0.

Caveat.. Be careful if k ≠ 0 when dealing simultaneously with absolute and relative quantities since L = Q(β + kα) = K(α), and the generator chosen for the absolute extension is not the same as for the relative one. If this happens, one can of course go on working, but we advise to change the relative polynomial so that its root becomes β + kα. Typical GP instructions would be

      [P,a,k] = rnfequation(K, pol, 1);
      if (k, pol = subst(pol, x, x - k*Mod(y, K.pol)));
      L = rnfinit(K, pol);

rnf [12] is by default unused and set equal to 0. This field is used to store further information about the field as it becomes available (which is rarely needed, hence would be too expensive to compute during the initial rnfinit call).

    The library syntax is GEN rnfinit(GEN nf, GEN pol).

3.6.150 rnfisabelian(nf , T). T being a relative polynomial with coefficients in nf , return 1 if it defines an abelian extension, and 0 otherwise.

    ? K = nfinit(y^2 + 23);
    ? rnfisabelian(K, x^3 - 3*x - y)
    %2 = 1
    The library syntax is long rnfisabelian(GEN nf, GEN T).

3.6.151 rnfisfree(bnf , x). Given bnf as output by bnfinit, and either a polynomial x with coefficients in bnf defining a relative extension L of bnf , or a pseudo-basis x of such an extension, returns true (1) if L/bnf is free, false (0) if not.

    The library syntax is long rnfisfree(GEN bnf, GEN x).

3.6.152 rnfisnorm(T, a, {flag = 0}). Similar to bnfisnorm but in the relative case. T is as output by rnfisnorminit applied to the extension L/K. This tries to decide whether the element a in K is the norm of some x in the extension L/K.

The output is a vector [x, q], where a = Norm(x) ∗ q. The algorithm looks for a solution x which is an S-integer, with S a list of places of K containing at least the ramified primes, the generators of the class group of L, as well as those primes dividing a. If L/K is Galois, then this is enough; otherwise, flag is used to add more primes to S: all the places above the primes p ≤ flag (resp. p|flag) if flag > 0 (resp. flag < 0).

The answer is guaranteed (i.e. a is a norm iff q = 1) if the field is Galois, or, under GRH, if S contains all primes less than 12 log^{2} |disc(M)|, where M is the normal closure of L/K.

If rnfisnorminit has determined (or was told) that L/K is Galois, and flag ≠ 0, a Warning is issued (so that you can set flag = 1 to check whether L/K is known to be Galois, according to T). Example:

    bnf = bnfinit(y^3 + y^2 - 2*y - 1);
    p = x^2 + Mod(y^2 + 2*y + 1, bnf.pol);
    T = rnfisnorminit(bnf, p);
    rnfisnorm(T, 17)

checks whether 17 is a norm in the Galois extension Q(β)/Q(α), where α^{3} + α^{2} − 2α − 1 = 0 and β^{2} + α^{2} + 2α + 1 = 0 (it is).

    The library syntax is GEN rnfisnorm(GEN T, GEN a, long flag).

3.6.153 rnfisnorminit(pol, polrel, {flag = 2}). Let K be defined by a root of pol, and L/K the extension defined by the polynomial polrel. As usual, pol can in fact be an nf , or bnf , etc; if pol has degree 1 (the base field is Q), polrel is also allowed to be an nf , etc. Computes technical data needed by rnfisnorm to solve norm equations Nx = a, for x in L, and a in K.

If flag = 0, do not care whether L/K is Galois or not.

If flag = 1, L/K is assumed to be Galois (unchecked), which speeds up rnfisnorm.

If flag = 2, let the routine determine whether L/K is Galois.

    The library syntax is GEN rnfisnorminit(GEN pol, GEN polrel, long flag).

3.6.154 rnfkummer(bnr, {subgp}, {d = 0}). bnr being as output by bnrinit, finds a relative equation for the class field corresponding to the module in bnr and the given congruence subgroup (the full ray class field if subgp is omitted). If d is positive, outputs the list of all relative equations of degree d contained in the ray class field defined by bnr, with the same conductor as (bnr, subgp).

Warning. This routine only works for subgroups of prime index. It uses Kummer theory, adjoining necessary roots of unity (it needs to compute a tough bnfinit here), and finds a generator via Hecke’s characterization of ramification in Kummer extensions of prime degree. If your extension does not have prime degree, for the time being, you have to split it by hand as a tower / compositum of such extensions.

    The library syntax is GEN rnfkummer(GEN bnr, GEN subgp = NULL, long d, long prec).

3.6.155 rnflllgram(nf , pol, order). Given a polynomial pol with coefficients in nf defining a relative extension L and a suborder order of L (of maximal rank), as output by rnfpseudobasis(nf , pol) or similar, gives [[neworder], U], where neworder is a reduced order and U is the unimodular transformation matrix.

    The library syntax is GEN rnflllgram(GEN nf, GEN pol, GEN order, long prec).

3.6.156 rnfnormgroup(bnr, pol). bnr being a big ray class field as output by bnrinit and pol a relative polynomial defining an Abelian extension, computes the norm group (alias Artin or Takagi group) corresponding to the Abelian extension of bnf =bnr.bnf defined by pol, where the module corresponding to bnr is assumed to be a multiple of the conductor (i.e. pol defines a subextension of bnr). The result is the HNF defining the norm group on the given generators of bnr.gen. Note that neither the fact that pol defines an Abelian extension nor the fact that the module is a multiple of the conductor is checked. The result is undefined if the assumption is not correct.

    The library syntax is GEN rnfnormgroup(GEN bnr, GEN pol).

3.6.157 rnfpolred(nf , pol). THIS FUNCTION IS OBSOLETE: use rnfpolredbest instead. Relative version of polred. Given a monic polynomial pol with coefficients in nf , finds a list of relative polynomials defining some subfields, hopefully simpler and containing the original field. In the present version 2.7.2, this is slower and less efficient than rnfpolredbest.

Remark. this function is based on an incomplete reduction theory of lattices over number fields, implemented by rnflllgram, which deserves to be improved.

    The library syntax is GEN rnfpolred(GEN nf, GEN pol, long prec).

3.6.158 rnfpolredabs(nf , pol, {flag = 0}). THIS FUNCTION IS OBSOLETE: use rnfpolredbest instead. Relative version of polredabs. Given a monic polynomial pol with coefficients in nf , finds a simpler relative polynomial defining the same field. The binary digits of flag mean

The binary digits of flag correspond to 1: add information to convert elements to the new representation, 2: absolute polynomial, instead of relative, 16: possibly use a suborder of the maximal order. More precisely:

0: default, return P

1: returns [P, a] where P is the default output and a, a t_POLMOD modulo P, is a root of pol.

2: returns Pabs, an absolute, instead of a relative, polynomial. Same as but faster than

      rnfequation(nf, rnfpolredabs(nf,pol))

3: returns [Pabs, a, b], where Pabs is an absolute polynomial as above, a, b are t_POLMOD modulo Pabs, roots of nf.pol and pol respectively.

16: possibly use a suborder of the maximal order. This is slower than the default when the relative discriminant is smooth, and much faster otherwise. See Section 3.6.120.

Warning. In the present implementation, rnfpolredabs produces smaller polynomials than rnfpolred and is usually faster, but its complexity is still exponential in the absolute degree. The function rnfpolredbest runs in polynomial time, and tends to return polynomials with smaller discriminants.

    The library syntax is GEN rnfpolredabs(GEN nf, GEN pol, long flag).

3.6.159 rnfpolredbest(nf , pol, {flag = 0}). Relative version of polredbest. Given a monic polynomial pol with coefficients in nf , finds a simpler relative polynomial P defining the same field. As opposed to rnfpolredabs this function does not return a smallest (canonical) polynomial with respect to some measure, but it does run in polynomial time.

The binary digits of flag correspond to 1: add information to convert elements to the new representation, 2: absolute polynomial, instead of relative. More precisely:

0: default, return P

1: returns [P, a] where P is the default output and a, a t_POLMOD modulo P, is a root of pol.

2: returns Pabs, an absolute, instead of a relative, polynomial. Same as but faster than

      rnfequation(nf, rnfpolredbest(nf,pol))

3: returns [Pabs, a, b], where Pabs is an absolute polynomial as above, a, b are t_POLMOD modulo Pabs, roots of nf.pol and pol respectively.

? K = nfinit(y^3-2); pol = x^2 +x*y + y^2;
? [P, a] = rnfpolredbest(K,pol,1);
? P
%3 = x^2 - x + Mod(y - 1, y^3 - 2)
? a
%4 = Mod(Mod(2*y^2+3*y+4,y^3-2)*x + Mod(-y^2-2*y-2,y^3-2),
         x^2 - x + Mod(y-1,y^3-2))
? subst(K.pol,y,a)
%5 = 0
? [Pabs, a, b] = rnfpolredbest(K,pol,3);
? Pabs
%7 = x^6 - 3*x^5 + 5*x^3 - 3*x + 1
? a
%8 = Mod(-x^2+x+1, x^6-3*x^5+5*x^3-3*x+1)
? b
%9 = Mod(2*x^5-5*x^4-3*x^3+10*x^2+5*x-5, x^6-3*x^5+5*x^3-3*x+1)
? subst(K.pol,y,a)
%10 = 0
? substvec(pol,[x,y],[a,b])
%11 = 0
The library syntax is GEN rnfpolredbest(GEN nf, GEN pol, long flag).

3.6.160 rnfpseudobasis(nf , pol). Given a number field nf as output by nfinit and a polynomial pol with coefficients in nf defining a relative extension L of nf , computes a pseudo-basis (A, I) for the maximal order Z_{L} viewed as a Z_{K}-module, and the relative discriminant of L. This is output as a four-element row vector [A, I, D, d], where D is the relative ideal discriminant and d is the relative discriminant considered as an element of nf^{∗}/nf^{∗}^{2}.

The library syntax is GEN rnfpseudobasis(GEN nf, GEN pol).

3.6.161 rnfsteinitz(nf , x). Given a number field nf as output by nfinit and either a polynomial x with coefficients in nf defining a relative extension L of nf , or a pseudo-basis x of such an extension as output for example by rnfpseudobasis, computes another pseudo-basis (A, I) (not in HNF in general) such that all the ideals of I except perhaps the last one are equal to the ring of integers of nf , and outputs the four-component row vector [A, I, D, d] as in rnfpseudobasis. The name of this function comes from the fact that the ideal class of the last ideal of I, which is well defined, is the Steinitz class of the Z_{K}-module Z_{L} (its image in SK_{0}(Z_{K})).

The library syntax is GEN rnfsteinitz(GEN nf, GEN x).

3.6.162 subgrouplist(bnr, {bound}, {flag = 0}). bnr being as output by bnrinit or a list of cyclic components of a finite Abelian group G, outputs the list of subgroups of G. Subgroups are given as HNF left divisors of the SNF matrix corresponding to G.

If flag = 0 (default) and bnr is as output by bnrinit, gives only the subgroups whose modulus is the conductor. Otherwise, the modulus is not taken into account.

If bound is present, and is a positive integer, restrict the output to subgroups of index less than bound. If bound is a vector containing a single positive integer B, then only subgroups of index exactly equal to B are computed. For instance

? subgrouplist([6,2])
%1 = [[6, 0; 0, 2], [2, 0; 0, 2], [6, 3; 0, 1], [2, 1; 0, 1], [3, 0; 0, 2],
[1, 0; 0, 2], [6, 0; 0, 1], [2, 0; 0, 1], [3, 0; 0, 1], [1, 0; 0, 1]]
? subgrouplist([6,2],3)    \\ index less than 3
%2 = [[2, 1; 0, 1], [1, 0; 0, 2], [2, 0; 0, 1], [3, 0; 0, 1], [1, 0; 0, 1]]
? subgrouplist([6,2],[3])  \\ index 3
%3 = [[3, 0; 0, 1]]
? bnr = bnrinit(bnfinit(x), [120,[1]], 1);
? L = subgrouplist(bnr, [8]);

In the last example, L corresponds to the 24 subfields of Q(ζ_{120}), of degree 8 and conductor 120∞ (by setting flag, we see there are a total of 43 subgroups of degree 8).

? vector(#L, i, galoissubcyclo(bnr, L[i]))

will produce their equations. (For a general base field, you would have to rely on bnrstark, or

rnfkummer.)
The library syntax is GEN subgrouplist0(GEN bnr, GEN bound = NULL, long flag).

3.6.163 zetak(nfz, x, {flag = 0}). znf being a number field initialized by zetakinit (not by nfinit), computes the value of the Dedekind zeta function of the number field at the complex number x. If flag = 1 computes Dedekind Λ function instead (i.e. the product of the Dedekind zeta function by its gamma and exponential factors).

CAVEAT. This implementation is not satisfactory and must be rewritten. In particular

• The accuracy of the result depends in an essential way on the accuracy of both the zetakinit program and the current accuracy. Be wary in particular that x of large imaginary part or, on the contrary, very close to an ordinary integer will suffer from precision loss, yielding fewer significant digits than expected. Computing with 28 digits of relative accuracy, we have

? zeta(3)
%1 = 1.202056903159594285399738161
? zeta(3-1e-20)
%2 = 1.202056903159594285401719424
? zetak(zetakinit(x), 3-1e-20)
%3 = 1.2020569031595952919  \\ 5 digits are wrong
? zetak(zetakinit(x), 3-1e-28)
%4 = -25.33411749           \\ junk

• As the precision increases, results become unexpectedly completely wrong:

? \p100
? zetak(zetakinit(x^2-5), -1) - 1/30
%1 = 7.26691813 E-108    \\ perfect
? \p150
? zetak(zetakinit(x^2-5), -1) - 1/30
%2 = -2.486113578 E-156  \\ perfect
? \p200
? zetak(zetakinit(x^2-5), -1) - 1/30
%3 = 4.47... E-75        \\ more than half of the digits are wrong
? \p250

    ? zetak(zetakinit(x^2-5), -1) - 1/30
    %4 = 1.6 E43             \\ junk
    The library syntax is GEN gzetakall(GEN nfz, GEN x, long flag, long prec). See also
GEN glambdak(GEN znf, GEN x, long prec) or GEN gzetak(GEN znf, GEN x, long prec).

3.6.164 zetakinit(bnf ). Computes a number of initialization data concerning the number field associated to bnf so as to be able to compute the Dedekind zeta and lambda functions, respectively zetak(x) and zetak(x, 1), at the current real precision. If you do not need the bnfinit data somewhere else, you may call it with an irreducible polynomial instead of a bnf : it will call bnfinit itself.

The result is a 9-component vector v whose components are very technical and cannot really be used except through the zetak function.

This function is very inefficient and should be rewritten. It needs to computes millions of coefficients of the corresponding Dirichlet series if the precision is big. Unless the discriminant is small it will not be able to handle more than 9 digits of relative precision. For instance, zetakinit(x^8 - 2) needs 440MB of memory at default precision.

This function will fail with the message

     *** bnrL1: overflow in zeta_get_N0 [need too many primes].

if the approximate functional equation requires us to sum too many terms (if the discriminant of the number field is too large).

    The library syntax is GEN initzeta(GEN bnf, long prec).

3.7 Polynomials and power series.

We group here all functions which are specific to polynomials or power series. Many other functions which can be applied on these objects are described in the other sections. Also, some of the functions described here can be applied to other types.

3.7.1 O(p^e). If p is an integer greater than 2, returns a p-adic 0 of precision e. In all other cases, returns a power series zero with precision given by ev, where v is the X-adic valuation of p with respect to its main variable.

    The library syntax is GEN ggrando(). GEN zeropadic(GEN p, long e) for a p-adic and GEN

zeroser(long v, long e) for a power series zero in variable v.

3.7.2 bezoutres(A, B, {v}). Deprecated alias for polresultantext

    The library syntax is GEN polresultantext0(GEN A, GEN B, long v = -1), where v is a

variable number.

3.7.3 deriv(x, {v}). Derivative of x with respect to the main variable if v is omitted, and with respect to v otherwise. The derivative of a scalar type is zero, and the derivative of a vector or matrix is done componentwise. One can use x′ as a shortcut if the derivative is with respect to the main variable of x.

By definition, the main variable of a t_POLMOD is the main variable among the coefficients from its two polynomial components (representative and modulus); in other words, assuming a polmod represents an element of R[X]/(T (X)), the variable X is a mute variable and the derivative is taken with respect to the main variable used in the base ring R.

The library syntax is GEN deriv(GEN x, long v = -1), where v is a variable number.

3.7.4 diffop(x, v, d, {n = 1}). Let v be a vector of variables, and d a vector of the same length, return the image of x by the n-power (1 if n is not given) of the differential operator D that assumes the value d[i] on the variable v[i]. The value of D on a scalar type is zero, and D applies componentwise to a vector or matrix. When applied to a t_POLMOD, if no value is provided for the variable of the modulus, such value is derived using the implicit function theorem.

Some examples: This function can be used to differentiate formal expressions: If E = exp(X^{2}) then we have E′ = 2 ∗ X ∗ E. We can derivate X ∗ exp(X^{2}) as follow:

    ? diffop(E*X,[X,E],[1,2*X*E])
    %1 = (2*X^2 + 1)*E

Let Sin and Cos be two function such that Sin^{2} + Cos^{2} = 1 and Cos′ = −Sin. We can differentiate Sin/Cos as follow, PARI inferring the value of Sin′ from the equation:

    ? diffop(Mod('Sin/'Cos,'Sin^2+'Cos^2-1),['Cos],[-'Sin])
    %1 = Mod(1/Cos^2, Sin^2 + (Cos^2 - 1))

Compute the Bell polynomials (both complete and partial) via the Faa di Bruno formula:

    Bell(k,n=-1)=
    {
      my(var(i)=eval(Str("X",i)));
      my(x,v,dv);
      v=vector(k,i,if(i==1,'E,var(i-1)));
      dv=vector(k,i,if(i==1,'X*var(1)*'E,var(i)));
      x=diffop('E,v,dv,k)/'E;
      if(n<0,subst(x,'X,1),polcoeff(x,n,'X))
    }
    The library syntax is GEN diffop0(GEN x, GEN v, GEN d, long n).

For n = 1, the function GEN diffop(GEN x, GEN v, GEN d) is also available.

3.7.5 eval(x). Replaces in x the formal variables by the values that have been assigned to them after the creation of x. This is mainly useful in GP, and not in library mode. Do not confuse this with substitution (see subst).

If x is a character string, eval(x) executes x as a GP command, as if directly input from the keyboard, and returns its output.

? x1 = "one"; x2 = "two";
? n = 1; eval(Str("x", n))
%2 = "one"
? f = "exp"; v = 1;
? eval(Str(f, "(", v, ")"))
%4 = 2.7182818284590452353602874713526624978

Note that the first construct could be implemented in a simpler way by using a vector x = ["one","two"]; x[n], and the second by using a closure f = exp; f(v). The final example is more interesting:

? genmat(u,v) = matrix(u,v,i,j, eval( Str("x",i,j) ));
? genmat(2,3)   \\ generic 2 x 3 matrix
%2 =
[x11 x12 x13]
[x21 x22 x23]

A syntax error in the evaluation expression raises an e SYNTAX exception, which can be trapped as usual:

? 1a
 ***   unused characters: 1a
 ***                       ^-
? E(expr) =
  {
    iferr(eval(expr),
          e, print("syntax error"),
          errname(e) == "e_SYNTAX");
  }
? E("1+1")
%1 = 2
? E("1a")
syntax error

The library syntax is geval(GEN x).

3.7.6 factorpadic(pol, p, r). p-adic factorization of the polynomial pol to precision r, the result being a two-column matrix as in factor. Note that this is not the same as a factorization over Z/p^{r}Z (polynomials over that ring do not form a unique factorization domain, anyway), but approximations in Q/p^{r}Z of the true factorization in Q_{p}[X].

    ? factorpadic(x^2 + 9, 3,5)
    %1 =
    [(1 + O(3^5))*x^2 + O(3^5)*x + (3^2 + O(3^5)) 1]
    ? factorpadic(x^2 + 1, 5,3)
    %2 =
    [  (1 + O(5^3))*x + (2 + 5 + 2*5^2 + O(5^3)) 1]
    [(1 + O(5^3))*x + (3 + 3*5 + 2*5^2 + O(5^3)) 1]

The factors are normalized so that their leading coefficient is a power of p. The method used is a modified version of the round 4 algorithm of Zassenhaus.

If pol has inexact t_PADIC coefficients, this is not always well-defined; in this case, the polynomial is first made integral by dividing out the p-adic content, then lifted to Z using truncate coefficientwise. Hence we actually factor exactly a polynomial which is only p-adically close to the input. To avoid pitfalls, we advise to only factor polynomials with exact rational coefficients.

The library syntax is factorpadic(GEN f,GEN p, long r) . The function factorpadic0 is deprecated, provided for backward compatibility.

3.7.7 intformal(x, {v}). formal integration of x with respect to the variable v (wrt. the main variable if v is omitted). Since PARI cannot represent logarithmic or arctangent terms, any such term in the result will yield an error:

     ? intformal(x^2)
     %1 = 1/3*x^3
     ? intformal(x^2, y)
     %2 = y*x^2
     ? intformal(1/x)
       ***   at top-level: intformal(1/x)
       ***                 ^--------------
       *** intformal: domain error in intformal: residue(series, pole) != 0

The argument x can be of any type. When x is a rational function, we assume that the base ring is an integral domain of characteristic zero.

By definition, the main variable of a t_POLMOD is the main variable among the coefficients from its two polynomial components (representative and modulus); in other words, assuming a polmod represents an element of R[X]/(T (X)), the variable X is a mute variable and the integral is taken with respect to the main variable used in the base ring R. In particular, it is meaningless to integrate with respect to the main variable of x.mod:

    ? intformal(Mod(1,x^2+1), 'x)
    *** intformal: incorrect priority in intformal: variable x = x

The library syntax is GEN integ(GEN x, long v = -1), where v is a variable number.

3.7.8 padicappr(pol, a). Vector of p-adic roots of the polynomial pol congruent to the p-adic number a modulo p, and with the same p-adic precision as a. The number a can be an ordinary padic number (type t_PADIC, i.e. an element of Z_{p}) or can be an integral element of a finite extension of Q_{p}, given as a t_POLMOD at least one of whose coefficients is a t_PADIC. In this case, the result is the vector of roots belonging to the same extension of Q_{p} as a.

    The library syntax is GEN padicappr(GEN pol, GEN a). Also available is GEN Zp_appr(GEN
f, GEN a) when a is a t_PADIC.

3.7.9 padicfields(p, N, {flag = 0}). Returns a vector of polynomials generating all the extensions of degree N of the field Q_{p} of p-adic rational numbers; N is allowed to be a 2-component vector [n, d], in which case we return the extensions of degree n and discriminant p^{d}.

The list is minimal in the sense that two different polynomials generate non-isomorphic extensions; in particular, the number of polynomials is the number of classes of non-isomorphic extensions. If P is a polynomial in this list, α is any root of P and K = Q_{p}(α), then α is the sum of a uniformizer and a (lift of a) generator of the residue field of K; in particular, the powers of α generate the ring of p-adic integers of K.

If flag = 1, replace each polynomial P by a vector [P, e, f, d, c] where e is the ramification index, f the residual degree, d the valuation of the discriminant, and c the number of conjugate fields. If flag = 2, only return the number of extensions in a fixed algebraic closure (Krasner’s formula), which is much faster.

    The library syntax is GEN padicfields0(GEN p, GEN N, long flag). Also available is GEN
padicfields(GEN p, long n, long d, long flag), which computes extensions of Q_{p} of degree

n and discriminant p^{d}.

3.7.10 polchebyshev(n, {flag = 1}, {a =′ x}). Returns the n^{th} Chebyshev polynomial of the first kind T_{n} (flag = 1) or the second kind U_{n} (flag = 2), evaluated at a ('x by default). Both series of polynomials satisfy the 3-term relation

P_{n+1} = 2xP_{n} − P_{n−1},

and are determined by the initial conditions U_{0} = T_{0} = 1, T_{1} = x, U_{1} = 2x. In fact T′_{n} = nU_{n−1} and, for all complex numbers z, we have T_{n}(cos z) = cos(nz) and U_{n−1}(cos z) = sin(nz)/ sin z. If n ≥ 0, then these polynomials have degree n. For n < 0, T_{n} is equal to T_{−n} and U_{n} is equal to −U_{−2−n}. In particular, U_{−1} = 0.

    The library syntax is GEN polchebyshev_eval(long n, long flag, GEN a = NULL). Also
available are GEN polchebyshev(long n, long \fl, long v), GEN polchebyshev1(long n,
long v) and GEN polchebyshev2(long n, long v) for T_{n} and U_{n} respectively.

3.7.11 polcoeff(x, n, {v}). Coefficient of degree n of the polynomial x, with respect to the main variable if v is omitted, with respect to v otherwise. If n is greater than the degree, the result is zero.

Naturally applies to scalars (polynomial of degree 0), as well as to rational functions whose denominator is a monomial. It also applies to power series: if n is less than the valuation, the result is zero. If it is greater than the largest significant degree, then an error message is issued.

For greater flexibility, x can be a vector or matrix type and the function then returns compo-

nent(x,n).

The library syntax is GEN polcoeff0(GEN x, long n, long v = -1), where v is a variable number.

3.7.12 polcyclo(n, {a =′ x}). n-th cyclotomic polynomial, evaluated at a ('x by default). The integer n must be positive.

Algorithm used: reduce to the case where n is squarefree; to compute the cyclotomic polynomial, use Φ_{np}(x) = Φ_{n}(x^{p})/Φ(x); to compute it evaluated, use Φ_{n}(x) = ∏_{d|n}(x^{d} − 1)^{μ(n/d)}. In the evaluated case, the algorithm assumes that a^{d} − 1 is either 0 or invertible, for all d | n. If this is not the case (the base ring has zero divisors), use subst(polcyclo(n),x,a).

The library syntax is GEN polcyclo_eval(long n, GEN a = NULL). The variant GEN polcy-

clo(long n, long v) returns the n-th cyclotomic polynomial in variable v.

3.7.13 polcyclofactors(f). Returns a vector of polynomials, whose product is the product of distinct cyclotomic polynomials dividing f.

? f = x^10+5*x^8-x^7+8*x^6-4*x^5+8*x^4-3*x^3+7*x^2+3;
? v = polcyclofactors(f)
%2 = [x^2 + 1, x^2 + x + 1, x^4 - x^3 + x^2 - x + 1]
? apply(poliscycloprod, v)
%3 = [1, 1, 1]
? apply(poliscyclo, v)
%4 = [4, 3, 10]

In general, the polynomials are products of cyclotomic polynomials and not themselves irreducible:

? g = x^8+2*x^7+6*x^6+9*x^5+12*x^4+11*x^3+10*x^2+6*x+3;
? polcyclofactors(g)
%2 = [x^6 + 2*x^5 + 3*x^4 + 3*x^3 + 3*x^2 + 2*x + 1]
? factor(%[1])
%3 =
[            x^2 + x + 1 1]
[x^4 + x^3 + x^2 + x + 1 1]
The library syntax is GEN polcyclofactors(GEN f).

3.7.14 poldegree(x, {v}). Degree of the polynomial x in the main variable if v is omitted, in the variable v otherwise.

The degree of 0 is a fixed negative number, whose exact value should not be used. The degree of a non-zero scalar is 0. Finally, when x is a non-zero polynomial or rational function, returns the ordinary degree of x. Raise an error otherwise.

The library syntax is long poldegree(GEN x, long v = -1), where v is a variable number.

3.7.15 poldisc(pol, {v}). Discriminant of the polynomial pol in the main variable if v is omitted, in v otherwise. The algorithm used is the subresultant algorithm.

The library syntax is GEN poldisc0(GEN pol, long v = -1), where v is a variable number.

3.7.16 poldiscreduced(f). Reduced discriminant vector of the (integral, monic) polynomial f. This is the vector of elementary divisors of Z[α]/f′(α)Z[α], where α is a root of the polynomial f. The components of the result are all positive, and their product is equal to the absolute value of the discriminant of f.

The library syntax is GEN reduceddiscsmith(GEN f).

3.7.17 polgraeffe(f). Returns the Graeffe transform g of f, such that g(x^{2}) = f(x)f(−x).

    The library syntax is GEN polgraeffe(GEN f).

3.7.18 polhensellift(A, B, p, e). Given a prime p, an integral polynomial A whose leading coefficient is a p-unit, a vector B of integral polynomials that are monic and pairwise relatively prime modulo p, and whose product is congruent to A/lc(A) modulo p, lift the elements of B to polynomials whose product is congruent to A modulo p^{e}.

More generally, if T is an integral polynomial irreducible mod p, and B is a factorization of A over the finite field F_{p}[t]/(T), you can lift it to Z_{p}[t]/(T, p^{e}) by replacing the p argument with [p, T]:

    ? { T = t^3 - 2; p = 7; A = x^2 + t + 1;
        B = [x + (3*t^2 + t + 1), x + (4*t^2 + 6*t + 6)];
        r = polhensellift(A, B, [p, T], 6) }
    %1 = [x + (20191*t^2 + 50604*t + 75783), x + (97458*t^2 + 67045*t + 41866)]
    ? liftall( r[1] * r[2] * Mod(Mod(1,p^6),T) )
    %2 = x^2 + (t + 1)
    The library syntax is GEN polhensellift(GEN A, GEN B, GEN p, long e).

3.7.19 polhermite(n, {a =′ x}). n^{th} Hermite polynomial H_{n} evaluated at a ('x by default), i.e.

H_{n}(x) = (−1)^{n}e^{x^{2}} \frac{d^{n}}{dx^{n}} e^{−x^{2}}.

    The library syntax is GEN polhermite_eval(long n, GEN a = NULL). The variant GEN pol-

hermite(long n, long v) returns the n-th Hermite polynomial in variable v.

3.7.20 polinterpolate(X, {Y }, {x}, {&e}). Given the data vectors X and Y of the same length n (X containing the x-coordinates, and Y the corresponding y-coordinates), this function finds the interpolating polynomial passing through these points and evaluates it at x. If Y is omitted, return the polynomial interpolating the (i, X[i]). If present, e will contain an error estimate on the returned value.

    The library syntax is GEN polint(GEN X, GEN Y = NULL, GEN x = NULL, GEN *e = NULL).

3.7.21 poliscyclo(f). Returns 0 if f is not a cyclotomic polynomial, and n > 0 if f = Φ_{n}, the n-th cyclotomic polynomial.

    ? poliscyclo(x^4-x^2+1)
    %1 = 12
    ? polcyclo(12)
    %2 = x^4 - x^2 + 1
    ? poliscyclo(x^4-x^2-1)
    %3 = 0
    The library syntax is long poliscyclo(GEN f).

3.7.22 poliscycloprod(f). Returns 1 if f is a product of cyclotomic polynomial, and 0 otherwise.

? f = x^6+x^5-x^3+x+1;
? poliscycloprod(f)
%2 = 1
? factor(f)
%3 =
[  x^2 + x + 1 1]
[x^4 - x^2 + 1 1]
? [ poliscyclo(T) | T <- %[,1] ]
%4 = [3, 12]
? polcyclo(3) * polcyclo(12)
%5 = x^6 + x^5 - x^3 + x + 1
The library syntax is long poliscycloprod(GEN f).

3.7.23 polisirreducible(pol). pol being a polynomial (univariate in the present version 2.7.2), returns 1 if pol is non-constant and irreducible, 0 otherwise. Irreducibility is checked over the smallest base field over which pol seems to be defined.

The library syntax is long isirreducible(GEN pol).

3.7.24 pollead(x, {v}). Leading coefficient of the polynomial or power series x. This is computed with respect to the main variable of x if v is omitted, with respect to the variable v otherwise.

The library syntax is GEN pollead(GEN x, long v = -1), where v is a variable number.

3.7.25 pollegendre(n, {a =′ x}). n^{th} Legendre polynomial evaluated at a ('x by default).

The library syntax is GEN pollegendre_eval(long n, GEN a = NULL). To obtain the n-th

Legendre polynomial in variable v, use GEN pollegendre(long n, long v).

3.7.26 polrecip(pol). Reciprocal polynomial of pol, i.e. the coefficients are in reverse order. pol must be a polynomial.

The library syntax is GEN polrecip(GEN pol).

3.7.27 polresultant(x, y, {v}, {flag = 0}). Resultant of the two polynomials x and y with exact entries, with respect to the main variables of x and y if v is omitted, with respect to the variable v otherwise. The algorithm assumes the base ring is a domain. If you also need the u and v such that x ∗ u + y ∗ v = Res(x, y), use the polresultantext function.

If flag = 0 (default), uses the the algorithm best suited to the inputs, either the subresultant algorithm (Lazard/Ducos variant, generic case), a modular algorithm (inputs in Q[X]) or Sylvester’s matrix (inexact inputs).

If flag = 1, uses the determinant of Sylvester’s matrix instead; this should always be slower than the default.

The library syntax is GEN polresultant0(GEN x, GEN y, long v = -1, long flag),

where v is a variable number.

3.7.28 polresultantext(A, B, {v}). Finds polynomials U and V such that A∗U +B∗V = R, where R is the resultant of U and V with respect to the main variables of A and B if v is omitted, and with respect to v otherwise. Returns the row vector [U, V, R]. The algorithm used (subresultant) assumes that the base ring is a domain.

    ? A = x*y; B = (x+y)^2;
    ? [U,V,R] = polresultantext(A, B)
    %2 = [-y*x - 2*y^2, y^2, y^4]
    ? A*U + B*V
    %3 = y^4
    ? [U,V,R] = polresultantext(A, B, y)
    %4 = [-2*x^2 - y*x, x^2, x^4]
    ? A*U+B*V
    %5 = x^4
    The library syntax is GEN polresultantext0(GEN A, GEN B, long v = -1), where v is a

variable number. Also available is GEN polresultantext(GEN x, GEN y).

3.7.29 polroots(x). Complex roots of the polynomial x, given as a column vector where each root is repeated according to its multiplicity. The precision is given as for transcendental functions: in GP it is kept in the variable realprecision and is transparent to the user, but it must be explicitly given as a second argument in library mode.

The algorithm used is a modification of A. Schönhage’s root-finding algorithm, due to and originally implemented by X. Gourdon. Barring bugs, it is guaranteed to converge and to give the roots to the required accuracy.

    The library syntax is GEN roots(GEN x, long prec).

3.7.30 polrootsmod(pol, p, {flag = 0}). Row vector of roots modulo p of the polynomial pol. Multiple roots are not repeated.

    ? polrootsmod(x^2-1,2)
    %1 = [Mod(1, 2)]~

If p is very small, you may set flag = 1, which uses a naive search.

    The library syntax is GEN rootmod0(GEN pol, GEN p, long flag).

3.7.31 polrootspadic(x, p, r). Vector of p-adic roots of the polynomial pol, given to p-adic precision r p is assumed to be a prime. Multiple roots are not repeated. Note that this is not the same as the roots in Z/p^{r}Z, rather it gives approximations in Z/p^{r}Z of the true roots living in Q_{p}.

    ? polrootspadic(x^3 - x^2 + 64, 2, 5)
    %1 = [2^3 + O(2^5), 2^3 + 2^4 + O(2^5), 1 + O(2^5)]~

If pol has inexact t_PADIC coefficients, this is not always well-defined; in this case, the polynomial is first made integral by dividing out the p-adic content, then lifted to Z using truncate coefficientwise. Hence the roots given are approximations of the roots of an exact polynomial which is p-adically close to the input. To avoid pitfalls, we advise to only factor polynomials with eact rational coefficients.

    The library syntax is GEN rootpadic(GEN x, GEN p, long r).

3.7.32 polsturm(pol, {a}, {b}). Number of real roots of the real squarefree polynomial pol in the interval ]a, b], using Sturm’s algorithm. a (resp. b) is taken to be −∞ (resp. +∞) if omitted.

    The library syntax is long sturmpart(GEN pol, GEN a = NULL, GEN b = NULL). Also avail-

able is long sturm(GEN pol) (total number of real roots).

3.7.33 polsubcyclo(n, d, {v =′ x}). Gives polynomials (in variable v) defining the sub-Abelian extensions of degree d of the cyclotomic field Q(ζ_{n}), where d | ϕ(n).

If there is exactly one such extension the output is a polynomial, else it is a vector of polynomials, possibly empty. To get a vector in all cases, use concat([], polsubcyclo(n,d)).

The function galoissubcyclo allows to specify exactly which sub-Abelian extension should be computed.

    The library syntax is GEN polsubcyclo(long n, long d, long v = -1), where v is a vari-

able number.

3.7.34 polsylvestermatrix(x, y). Forms the Sylvester matrix corresponding to the two polynomials x and y, where the coefficients of the polynomials are put in the columns of the matrix (which is the natural direction for solving equations afterwards). The use of this matrix can be essential when dealing with polynomials with inexact entries, since polynomial Euclidean division doesn’t make much sense in this case.

    The library syntax is GEN sylvestermatrix(GEN x, GEN y).

3.7.35 polsym(x, n). Creates the column vector of the symmetric powers of the roots of the polynomial x up to power n, using Newton’s formula.

    The library syntax is GEN polsym(GEN x, long n).

3.7.36 poltchebi(n, {v =′ x}). Deprecated alias for polchebyshev

The library syntax is GEN polchebyshev1(long n, long v = -1), where v is a variable number.

3.7.37 polzagier(n, m). Creates Zagier’s polynomial P_{n}^{(m)} used in the functions sumalt and sumpos (with flag = 1). One must have m ≤ n. The exact definition can be found in “Convergence acceleration of alternating series”, Cohen et al., Experiment. Math., vol. 9, 2000, pp. 3–12.

    The library syntax is GEN polzag(long n, long m).

3.7.38 serconvol(x, y). Convolution (or Hadamard product) of the two power series x and y; in other words if x = ∑ a_{k} ∗ X^{k} and y = ∑ b_{k} ∗ X^{k} then serconvol(x, y) = ∑ a_{k} ∗ b_{k} ∗ X^{k}.

    The library syntax is GEN convol(GEN x, GEN y).

3.7.39 serlaplace(x). x must be a power series with non-negative exponents. If x = ∑(a_{k}/k!)∗X^{k} then the result is ∑ a_{k} ∗ X^{k}.

The library syntax is GEN laplace(GEN x).

3.7.40 serreverse(s). Reverse power series of s, i.e. the series t such that t(s) = x; s must be a power series whose valuation is exactly equal to one.

? \ps 8
? t = serreverse(tan(x))
%2 = x - 1/3*x^3 + 1/5*x^5 - 1/7*x^7 + O(x^8)
? tan(t)
%3 = x + O(x^8)
The library syntax is GEN serreverse(GEN s).

3.7.41 subst(x, y, z). Replace the simple variable y by the argument z in the “polynomial” expression x. Every type is allowed for x, but if it is not a genuine polynomial (or power series, or rational function), the substitution will be done as if the scalar components were polynomials of degree zero. In particular, beware that:

? subst(1, x, [1,2; 3,4])
%1 =
[1 0]
[0 1]
? subst(1, x, Mat([0,1]))
  ***   at top-level: subst(1,x,Mat([0,1])
  ***                 ^--------------------
  *** subst: forbidden substitution by a non square matrix.

If x is a power series, z must be either a polynomial, a power series, or a rational function. Finally, if x is a vector, matrix or list, the substitution is applied to each individual entry.

Use the function substvec to replace several variables at once, or the function substpol to replace a polynomial expression.

The library syntax is GEN gsubst(GEN x, long y, GEN z), where y is a variable number.

3.7.42 substpol(x, y, z). Replace the “variable” y by the argument z in the “polynomial” expression x. Every type is allowed for x, but the same behavior as subst above apply.

The difference with subst is that y is allowed to be any polynomial here. The substitution is done moding out all components of x (recursively) by y − t, where t is a new free variable of lowest priority. Then substituting t by z in the resulting expression. For instance

? substpol(x^4 + x^2 + 1, x^2, y)
%1 = y^2 + y + 1
? substpol(x^4 + x^2 + 1, x^3, y)
%2 = x^2 + y*x + 1
? substpol(x^4 + x^2 + 1, (x+1)^2, y)
%3 = (-4*y - 6)*x + (y^2 + 3*y - 3)
The library syntax is GEN gsubstpol(GEN x, GEN y, GEN z). Further, GEN gdeflate(GEN

T, long v, long d) attempts to write T (x) in the form t(x^{d}), where x =pol x(v), and returns NULL if the substitution fails (for instance in the example %2 above).

3.7.43 substvec(x, v, w). v being a vector of monomials of degree 1 (variables), w a vector of expressions of the same length, replace in the expression x all occurrences of v_{i} by w_{i}. The substitutions are done simultaneously; more precisely, the v_{i} are first replaced by new variables in x, then these are replaced by the w_{i}:

? substvec([x,y], [x,y], [y,x])
%1 = [y, x]
? substvec([x,y], [x,y], [y,x+y])
%2 = [y, x + y]     \\ not [y, 2*y]
The library syntax is GEN gsubstvec(GEN x, GEN v, GEN w).

3.7.44 sumformal(f, {v}). formal sum of the polynomial expression f with respect to the main variable if v is omitted, with respect to the variable v otherwise; it is assumed that the base ring has characteristic zero. In other words, considering f as a polynomial function in the variable v, returns F, a polynomial in v vanishing at 0, such that F (b) − F (a) = sum_{v=a+1}^{b}f(v):

? sumformal(n)  \\ 1 + ... + n
%1 = 1/2*n^2 + 1/2*n
? f(n) = n^3+n^2+1;
? F = sumformal(f(n))  \\ f(1) + ... + f(n)
%3 = 1/4*n^4 + 5/6*n^3 + 3/4*n^2 + 7/6*n
? sum(n = 1, 2000, f(n)) == subst(F, n, 2000)
%4 = 1
? sum(n = 1001, 2000, f(n)) == subst(F, n, 2000) - subst(F, n, 1000)
%5 = 1
? sumformal(x^2 + x*y + y^2, y)
%6 = y*x^2 + (1/2*y^2 + 1/2*y)*x + (1/3*y^3 + 1/2*y^2 + 1/6*y)
? x^2 * y + x * sumformal(y) + sumformal(y^2) == %
%7 = 1

The library syntax is GEN sumformal(GEN f, long v = -1), where v is a variable number.

3.7.45 taylor(x, t, {d = seriesprecision}). Taylor expansion around 0 of x with respect to the simple variable t. x can be of any reasonable type, for example a rational function. Contrary to Ser, which takes the valuation into account, this function adds O(t^{d}) to all components of x.

? taylor(x/(1+y), y, 5)
%1 = (y^4 - y^3 + y^2 - y + 1)*x + O(y^5)
? Ser(x/(1+y), y, 5)
 ***   at top-level: Ser(x/(1+y),y,5)
 ***                 ^----------------
 *** Ser: main variable must have higher priority in gtoser.

The library syntax is GEN tayl(GEN x, long t, long precdl), where t is a variable number.

3.7.46 thue(tnf , a, {sol}). Returns all solutions of the equation P (x, y) = a in integers x and y, where tnf was created with thueinit(P). If present, sol must contain the solutions of Norm(x) = a modulo units of positive norm in the number field defined by P (as computed by bnfisintnorm). If there are infinitely many solutions, an error will be issued.

It is allowed to input directly the polynomial P instead of a tnf , in which case, the function first performs thueinit(P,0). This is very wasteful if more than one value of a is required.

If tnf was computed without assuming GRH (flag 1 in thueinit), then the result is unconditional. Otherwise, it depends in principle of the truth of the GRH, but may still be unconditionally correct in some favorable cases. The result is conditional on the GRH if a ≠ ±1 and, P has a single irreducible rational factor, whose associated tentative class number h and regulator R (as computed assuming the GRH) satisfy

• h > 1,

• R/0.2 > 1.5.

Here’s how to solve the Thue equation x^{13} − 5y^{13} = −4:

? tnf = thueinit(x^13 - 5);
? thue(tnf, -4)
%1 = [[1, 1]]

In this case, one checks that bnfinit(x^13 -5).no is 1. Hence, the only solution is (x, y) = (1, 1), and the result is unconditional. On the other hand:

? P = x^3-2*x^2+3*x-17; tnf = thueinit(P);
? thue(tnf, -15)
%2 = [[1, 1]]  \\ a priori conditional on the GRH.
? K = bnfinit(P); K.no
%3 = 3
? K.reg
%4 = 2.8682185139262873674706034475498755834

This time the result is conditional. All results computed using this particular tnf are likewise conditional, except for a right-hand side of ±1. The above result is in fact correct, so we did not just disprove the GRH:

? tnf = thueinit(x^3-2*x^2+3*x-17, 1 /*unconditional*/);
? thue(tnf, -15)
%4 = [[1, 1]]

Note that reducible or non-monic polynomials are allowed:

? tnf = thueinit((2*x+1)^5 * (4*x^3-2*x^2+3*x-17), 1);
? thue(tnf, 128)
%2 = [[-1, 0], [1, 0]]

Reducible polynomials are in fact much easier to handle.

The library syntax is GEN thue(GEN tnf, GEN a, GEN sol = NULL).

3.7.47 thueinit(P, {flag = 0}). Initializes the tnf corresponding to P, a univariate polynomial with integer coefficients. The result is meant to be used in conjunction with thue to solve Thue equations P (X/Y)Y^{deg P} = a, where a is an integer.

If flag is non-zero, certify results unconditionally. Otherwise, assume GRH, this being much faster of course. In the latter case, the result may still be unconditionally correct, see thue. For instance in most cases where P is reducible (not a pure power of an irreducible), or conditional computed class groups are trivial or the right hand side is ±1, then results are always unconditional.

    The library syntax is GEN thueinit(GEN P, long flag, long prec).

3.8 Vectors, matrices, linear algebra and sets.

Note that most linear algebra functions operating on subspaces defined by generating sets (such as mathnf, qflll, etc.) take matrices as arguments. As usual, the generating vectors are taken to be the columns of the given matrix.

Since PARI does not have a strong typing system, scalars live in unspecified commutative base rings. It is very difficult to write robust linear algebra routines in such a general setting. We thus assume that the base ring is a domain and work over its field of fractions. If the base ring is not a domain, one gets an error as soon as a non-zero pivot turns out to be non-invertible. Some functions, e.g. mathnf or mathnfmod, specifically assume that the base ring is Z.

3.8.1 algdep(x, k, {flag = 0}). x being real/complex, or p-adic, finds a polynomial of degree at most k with integer coefficients having x as approximate root. Note that the polynomial which is obtained is not necessarily the “correct” one. In fact it is not even guaranteed to be irreducible. One can check the closeness either by a polynomial evaluation (use subst), or by computing the roots of the polynomial given by algdep (use polroots).

Internally, lindep([1, x, …, x^{k}], flag) is used. A non-zero value of flag may improve on the default behavior if the input number is known to a huge accuracy, and you suspect the last bits are incorrect (this truncates the number, throwing away the least significant bits), but default values are usually sufficient:

    ? \p200
    ? algdep(2^(1/6)+3^(1/5), 30);      \\ wrong in 0.8s
    ? algdep(2^(1/6)+3^(1/5), 30, 100); \\ wrong in 0.4s
    ? algdep(2^(1/6)+3^(1/5), 30, 170); \\ right in 0.8s
    ? algdep(2^(1/6)+3^(1/5), 30, 200); \\ wrong in 1.0s
    ? \p250
    ? algdep(2^(1/6)+3^(1/5), 30);      \\ right in 1.0s
    ? algdep(2^(1/6)+3^(1/5), 30, 200); \\ right in 1.0s
    ? \p500
    ? algdep(2^(1/6)+3^(1/5), 30);      \\ right in 2.9s
    ? \p1000
    ? algdep(2^(1/6)+3^(1/5), 30);      \\ right in 10.6s

The changes in defaultprecision only affect the quality of the initial approximation to 2^{1/6}+3^{1/5}, algdep itself uses exact operations (the size of its operands depend on the accuracy of the input of course: more accurate input means slower operations).

Proceeding by increments of 5 digits of accuracy, algdep with default flag produces its first correct result at 205 digits, and from then on a steady stream of correct results.

The above example is the test case studied in a 2000 paper by Borwein and Lisonek: Applications of integer relation algorithms, Discrete Math., 217, p. 65–82. The version of PARI tested there was 1.39, which succeeded reliably from precision 265 on, in about 200 as much time as the current version.

The library syntax is GEN algdep0(GEN x, long k, long flag). Also available is GEN al-
gdep(GEN x, long k) (flag = 0).

3.8.2 charpoly(A, {v =′ x}, {flag = 5}). characteristic polynomial of A with respect to the variable v, i.e. determinant of v ∗ I − A if A is a square matrix.

? charpoly([1,2;3,4]);
%1 = x^2 - 5*x - 2
? charpoly([1,2;3,4],, 't)
%2 = t^2 - 5*t - 2

If A is not a square matrix, the function returns the characteristic polynomial of the map “multiplication by A” if A is a scalar:

? charpoly(Mod(x+2, x^3-2))
%1 = x^3 - 6*x^2 + 12*x - 10
? charpoly(I)
%2 = x^2 + 1
? charpoly(quadgen(5))
%3 = x^2 - x - 1
? charpoly(ffgen(ffinit(2,4)))
%4 = Mod(1, 2)*x^4 + Mod(1, 2)*x^3 + Mod(1, 2)*x^2 + Mod(1, 2)*x + Mod(1, 2)

The value of flag is only significant for matrices, and we advise to stick to the default value. Let n be the dimension of A.

If flag = 0, same method (Le Verrier’s) as for computing the adjoint matrix, i.e. using the traces of the powers of A. Assumes that n! is invertible; uses O(n^{4}) scalar operations.

If flag = 1, uses Lagrange interpolation which is usually the slowest method. Assumes that n! is invertible; uses O(n^{4}) scalar operations.

If flag = 2, uses the Hessenberg form. Assumes that the base ring is a field. Uses O(n^{3}) scalar operations, but suffers from coefficient explosion unless the base field is finite or R.

If flag = 3, uses Berkowitz’s division free algorithm, valid over any ring (commutative, with unit). Uses O(n^{4}) scalar operations.

If flag = 4, x must be integral. Uses a modular algorithm: Hessenberg form for various small primes, then Chinese remainders.

If flag = 5 (default), uses the “best” method given x. This means we use Berkowitz unless the base ring is Z (use flag = 4) or a field where coefficient explosion does not occur, e.g. a finite field or the reals (use flag = 2).

The library syntax is GEN charpoly0(GEN A, long v = -1, long flag), where v is a vari-
able number. Also available are GEN charpoly(GEN x, long v) (flag = 5), GEN caract(GEN A,

long v) (flag = 1), GEN carhess(GEN A, long v) (flag = 2), GEN carberkowitz(GEN A, long
v) (flag = 3) and GEN caradj(GEN A, long v, GEN *pt). In this last case, if pt is not NULL, *pt

receives the address of the adjoint matrix of A (see matadjoint), so both can be obtained at once.

3.8.3 concat(x, {y}). Concatenation of x and y. If x or y is not a vector or matrix, it is considered as a one-dimensional vector. All types are allowed for x and y, but the sizes must be compatible. Note that matrices are concatenated horizontally, i.e. the number of rows stays the same. Using transpositions, one can concatenate them vertically, but it is often simpler to use matconcat.

? x = matid(2); y = 2*matid(2);
? concat(x,y)
%2 =
[1 0 2 0]
[0 1 0 2]
? concat(x~,y~)~
%3 =
[1 0]
[0 1]
[2 0]
[0 2]
? matconcat([x;y])
%4 =
[1 0]
[0 1]
[2 0]
[0 2]

To concatenate vectors sideways (i.e. to obtain a two-row or two-column matrix), use Mat instead,

or matconcat:
? x = [1,2];
? y = [3,4];
? concat(x,y)
%3 = [1, 2, 3, 4]
? Mat([x,y]~)
%4 =
[1 2]
[3 4]
? matconcat([x;y])
%5 =
[1 2]
[3 4]

Concatenating a row vector to a matrix having the same number of columns will add the row to the matrix (top row if the vector is x, i.e. comes first, and bottom row otherwise).

The empty matrix [;] is considered to have a number of rows compatible with any operation, in particular concatenation. (Note that this is not the case for empty vectors [ ] or [ ]~.)

If y is omitted, x has to be a row vector or a list, in which case its elements are concatenated, from left to right, using the above rules.

? concat([1,2], [3,4])
%1 = [1, 2, 3, 4]
? a = [[1,2]~, [3,4]~]; concat(a)
%2 =
[1 3]
[2 4]
? concat([1,2; 3,4], [5,6]~)
%3 =
[1 2 5]
[3 4 6]
? concat([%, [7,8]~, [1,2,3,4]])
%5 =
[1 2 5 7]
[3 4 6 8]
[1 2 3 4]
The library syntax is GEN concat(GEN x, GEN y = NULL). GEN concat1(GEN x) is a shortcut
for concat(x,NULL).

3.8.4 forqfvec(v, q, b, expr). q being a square and symmetric matrix representing a positive definite quadratic form, evaluate expr for all vector v such that q(v) ≤ b. The formal variable v runs through all such vectors in turn.

? forqfvec(v, [3,2;2,3], 3, print(v))
[0, 1]~
[1, 0]~
[-1, 1]~

The library syntax is void forqfvec0(GEN v, GEN q = NULL, GEN b). The following func-

tion is also available: void forqfvec(void *E, long (*fun)(void *, GEN, double), GEN q,

GEN b): Evaluate fun(E,v,m) on all v such that q(v) < b, where v is a t_VECSMALL and m = q(v) is a C double. The function fun must return 0, unless forqfvec should stop, in which case, it should return 1.

3.8.5 lindep(v, {flag = 0}). finds a small non-trivial integral linear combination between components of v. If none can be found return an empty vector.

If v is a vector with real/complex entries we use a floating point (variable precision) LLL algorithm. If flag = 0 the accuracy is chosen internally using a crude heuristic. If flag > 0 the computation is done with an accuracy of flag decimal digits. To get meaningful results in the latter case, the parameter flag should be smaller than the number of correct decimal digits in the input.

? lindep([sqrt(2), sqrt(3), sqrt(2)+sqrt(3)])
%1 = [-1, -1, 1]~

If v is p-adic, flag is ignored and the algorithm LLL-reduces a suitable (dual) lattice.

? lindep([1, 2 + 3 + 3^2 + 3^3 + 3^4 + O(3^5)])

    %2 = [1, -2]~

If v is a matrix, flag is ignored and the function returns a non trivial kernel vector (combination of the columns).

    ? lindep([1,2,3;4,5,6;7,8,9])
    %3 = [1, -2, 1]~

If v contains polynomials or power series over some base field, finds a linear relation with coefficients in the field.

    ? lindep([x*y, x^2 + y, x^2*y + x*y^2, 1])
    %4 = [y, y, -1, -y^2]~

For better control, it is preferable to use t_POL rather than t_SER in the input, otherwise one gets a linear combination which is t-adically small, but not necessarily 0. Indeed, power series are first converted to the minimal absolute accuracy occurring among the entries of v (which can cause some coefficients to be ignored), then truncated to polynomials:

    ? v = [t^2+O(t^4), 1+O(t^2)]; L=lindep(v)
    %1 = [1, 0]~
    ? v*L
    %2 = t^2+O(t^4)  \\ small but not 0
    The library syntax is GEN lindep0(GEN v, long flag). Also available are GEN lindep(GEN

v) (real/complex entries, flag = 0), GEN lindep2(GEN v, long flag) (real/complex entries) GEN padic_lindep(GEN v) (p-adic entries) and GEN Xadic_lindep(GEN v) (polynomial entries). Finally GEN deplin(GEN v) returns a non-zero kernel vector for a t_MAT input.

3.8.6 listcreate(). Creates an empty list. This routine used to have a mandatory argument, which is now ignored (for backward compatibility). In fact, this function has become redundant and obsolete; it will disappear in future versions of PARI: just use List()

3.8.7 listinsert(L, x, n). Inserts the object x at position n in L (which must be of type t_LIST). This has complexity O(#L−n+1): all the remaining elements of list (from position n+1 onwards) are shifted to the right.

    The library syntax is GEN listinsert(GEN L, GEN x, long n).

3.8.8 listkill(L). Obsolete, retained for backward compatibility. Just use L = List() instead of listkill(L). In most cases, you won’t even need that, e.g. local variables are automatically cleared when a user function returns.

The library syntax is void listkill(GEN L).

3.8.9 listpop(list, {n}). Removes the n-th element of the list list (which must be of type t_LIST). If n is omitted, or greater than the list current length, removes the last element. If the list is already empty, do nothing. This runs in time O(#L − n + 1).

    The library syntax is void listpop(GEN list, long n).

3.8.10 listput(list, x, {n}). Sets the n-th element of the list list (which must be of type t_LIST) equal to x. If n is omitted, or greater than the list length, appends x. You may put an element into an occupied cell (not changing the list length), but it is easier to use the standard list[n] = x construct. This runs in time O(#L) in the worst case (when the list must be reallocated), but in time O(1) on average: any number of successive listputs run in time O(#L), where #L denotes the list final length.

    The library syntax is GEN listput(GEN list, GEN x, long n).

3.8.11 listsort(L, {flag = 0}). Sorts the t_LIST list in place, with respect to the (somewhat arbitrary) universal comparison function cmp. In particular, the ordering is the same as for sets and setsearch can be used on a sorted list.

    ? L = List([1,2,4,1,3,-1]); listsort(L); L
    %1 = List([-1, 1, 1, 2, 3, 4])
    ? setsearch(L, 4)
    %2 = 6
    ? setsearch(L, -2)
    %3 = 0

This is faster than the vecsort command since the list is sorted in place: no copy is made. No value returned.

If flag is non-zero, suppresses all repeated coefficients.

    The library syntax is void listsort(GEN L, long flag).

3.8.12 matadjoint(M, {flag = 0}). adjoint matrix of M, i.e. a matrix N of cofactors of M, satisfying M ∗ N = det(M) ∗ Id. M must be a (non-necessarily invertible) square matrix of dimension n. If flag is 0 or omitted, we try to use Leverrier-Faddeev’s algorithm, which assumes that n! invertible. If it fails or flag = 1, compute T = charpoly(M) independently first and return (−1)^{n−1}(T (x) − T (0))/x evaluated at M.

    ? a = [1,2,3;3,4,5;6,7,8] * Mod(1,4);
    %2 =
    [Mod(1, 4) Mod(2, 4) Mod(3, 4)]
    [Mod(3, 4) Mod(0, 4) Mod(1, 4)]
    [Mod(2, 4) Mod(3, 4) Mod(0, 4)]

Both algorithms use O(n^{4}) operations in the base ring, and are usually slower than computing the characteristic polynomial or the inverse of M directly.

    The library syntax is GEN matadjoint0(GEN M, long flag). Also available are GEN adj(GEN

x) (flag=0) and GEN adjsafe(GEN x) (flag=1).

3.8.13 matcompanion(x). The left companion matrix to the non-zero polynomial x.

    The library syntax is GEN matcompanion(GEN x).

3.8.14 matconcat(v). Returns a t_MAT built from the entries of v, which may be a t_VEC (concatenate horizontally), a t_COL (concatenate vertically), or a t_MAT (concatenate vertically each column, and concatenate vertically the resulting matrices). The entries of v are always considered as matrices: they can themselves be t_VEC (seen as a row matrix), a t_COL seen as a column matrix), a t_MAT, or a scalar (seen as an 1 × 1 matrix).

? A=[1,2;3,4]; B=[5,6]~; C=[7,8]; D=9;
? matconcat([A, B]) \\ horizontal
%1 =
[1 2 5]
[3 4 6]
? matconcat([A, C]~) \\ vertical
%2 =
[1 2]
[3 4]
[7 8]
? matconcat([A, B; C, D]) \\ block matrix
%3 =
[1 2 5]
[3 4 6]
[7 8 9]

If the dimensions of the entries to concatenate do not match up, the above rules are extended as follows:

• each entry v_{i,j} of v has a natural length and height: 1 × 1 for a scalar, 1 × n for a t_VEC of length n, n × 1 for a t_COL, m × n for an m × n t_MAT

• let H_{i} be the maximum over j of the lengths of the v_{i,j}, let L_{j} be the maximum over i of the heights of the v_{i,j}. The dimensions of the (i, j)-th block in the concatenated matrix are H_{i} × L_{j}.

• a scalar s = v_{i,j} is considered as s times an identity matrix of the block dimension min(H_{i}, L_{j})

• blocks are extended by 0 columns on the right and 0 rows at the bottom, as needed.

? matconcat([1, [2,3]~, [4,5,6]~]) \\ horizontal
%4 =
[1 2 4]
[0 3 5]
[0 0 6]
? matconcat([1, [2,3], [4,5,6]]~) \\ vertical
%5 =
[1 0 0]
[2 3 0]
[4 5 6]
? matconcat([B, C; A, D]) \\ block matrix
%6 =
[5 0 7 8]
[6 0 0 0]

[1 2 9 0]
[3 4 0 9]
? U=[1,2;3,4]; V=[1,2,3;4,5,6;7,8,9];
? matconcat(matdiagonal([U, V])) \\ block diagonal
%7 =
[1 2 0 0 0]
[3 4 0 0 0]
[0 0 1 2 3]
[0 0 4 5 6]
[0 0 7 8 9]

The library syntax is GEN matconcat(GEN v).

3.8.15 matdet(x, {flag = 0}). Determinant of the square matrix x.

If flag = 0, uses an appropriate algorithm depending on the coefficients:

• integer entries: modular method due to Dixon, Pernet and Stein.

• real or p-adic entries: classical Gaussian elimination using maximal pivot.

• intmod entries: classical Gaussian elimination using first non-zero pivot.

• other cases: Gauss-Bareiss. If flag = 1, uses classical Gaussian elimination with appropriate pivoting strategy (maximal

pivot for real or p-adic coefficients). This is usually worse than the default.

The library syntax is GEN det0(GEN x, long flag). Also available are GEN det(GEN x) (flag = 0), GEN det2(GEN x) (flag = 1) and GEN ZM_det(GEN x) for integer entries.

3.8.16 matdetint(B). Let B be an m × n matrix with integer coefficients. The determinant D of the lattice generated by the columns of B is the square root of det(B^{T} B) if B has maximal rank m, and 0 otherwise.

This function uses the Gauss-Bareiss algorithm to compute a positive multiple of D. When B is square, the function actually returns D = | det B|.

This function is useful in conjunction with mathnfmod, which needs to know such a multiple. If the rank is maximal and the matrix non-square, you can obtain D exactly using

  matdet( mathnfmod(B, matdetint(B)) )

Note that as soon as one of the dimensions gets large (m or n is larger than 20, say), it will often be much faster to use mathnf(B, 1) or mathnf(B, 4) directly.

The library syntax is GEN detint(GEN B).

3.8.17 matdiagonal(x). x being a vector, creates the diagonal matrix whose diagonal entries are those of x.

? matdiagonal([1,2,3]);
%1 =
[1 0 0]
[0 2 0]
[0 0 3]

Block diagonal matrices are easily created using matconcat:

? U=[1,2;3,4]; V=[1,2,3;4,5,6;7,8,9];
? matconcat(matdiagonal([U, V]))
%1 =
[1 2 0 0 0]
[3 4 0 0 0]
[0 0 1 2 3]
[0 0 4 5 6]
[0 0 7 8 9]

The library syntax is GEN diagonal(GEN x).

3.8.18 mateigen(x, {flag = 0}). Returns the (complex) eigenvectors of x as columns of a matrix. If flag = 1, return [L, H], where L contains the eigenvalues and H the corresponding eigenvectors; multiple eigenvalues are repeated according to the eigenspace dimension (which may be less than the eigenvalue multiplicity in the characteristic polynomial).

This function first computes the characteristic polynomial of x and approximates its complex roots (λ_{i}), then tries to compute the eigenspaces as kernels of the x − λ_{i}. This algorithm is illconditioned and is likely to miss kernel vectors if some roots of the characteristic polynomial are close, in particular if it has multiple roots.

? A = [13,2; 10,14]; mateigen(A)
%1 =
[-1/2 2/5]
[   1   1]
? [L,H] = mateigen(A, 1);
? L
%3 = [9, 18]
? H
%4 =
[-1/2 2/5]
[   1   1]

For symmetric matrices, use qfjacobi instead; for Hermitian matrices, compute

 A = real(x);
 B = imag(x);
 y = matconcat([A, -B; B, A]);

and apply qfjacobi to y.

    The library syntax is GEN mateigen(GEN x, long flag, long prec). Also available is GEN
eigen(GEN x, long prec) (flag = 0)

3.8.19 matfrobenius(M, {flag}, {v =′ x}). Returns the Frobenius form of the square matrix M. If flag = 1, returns only the elementary divisors as a vector of polynomials in the variable v. If flag = 2, returns a two-components vector [F,B] where F is the Frobenius form and B is the basis change so that M = B^{−1}F B.

    The library syntax is GEN matfrobenius(GEN M, long flag, long v = -1), where v is a

variable number.

3.8.20 mathess(x). Returns a matrix similar to the square matrix x, which is in upper Hessenberg form (zero entries below the first subdiagonal).

The library syntax is GEN hess(GEN x).

3.8.21 mathilbert(n). x being a long, creates the Hilbert matrixof order x, i.e. the matrix whose coefficient (i,j) is 1/(i + j − 1).

    The library syntax is GEN mathilbert(long n).

3.8.22 mathnf(M, {flag = 0}). Let R be a Euclidean ring, equal to Z or to K[X] for some field K. If M is a (not necessarily square) matrix with entries in R, this routine finds the upper triangular Hermite normal form of M. If the rank of M is equal to its number of rows, this is a square matrix. In general, the columns of the result form a basis of the R-module spanned by the columns of M.

The values 0, 1, 2, 3 of flag have a binary meaning, analogous to the one in matsnf; in this case, binary digits of flag mean:

• 1 (complete output): if set, outputs [H, U], where H is the Hermite normal form of M, and U is a transformation matrix such that MU = [0|H]. The matrix U belongs to GL(R). When M has a large kernel, the entries of U are in general huge.

• 2 (generic input): Deprecated. If set, assume that R = K[X] is a polynomial ring; otherwise, assume that R = Z. This flag is now useless since the routine always checks whether the matrix has integral entries.

For these 4 values, we use a naive algorithm, which behaves well in small dimension only. Larger values correspond to different algorithms, are restricted to integer matrices, and all output the unimodular matrix U. From now on all matrices have integral entries.

• flag = 4, returns [H, U] as in “complete output” above, using a variant of LLL reduction along the way. The matrix U is provably small in the L_{2} sense, and in general close to optimal; but the reduction is in general slow, although provably polynomial-time.

If flag = 5, uses Batut’s algorithm and output [H, U, P], such that H and U are as before and P is a permutation of the rows such that P applied to MU gives H. This is in general faster than flag = 4 but the matrix U is usually worse; it is heuristically smaller than with the default algorithm.

When the matrix is dense and the dimension is large (bigger than 100, say), flag = 4 will be fastest. When M has maximal rank, then

      H = mathnfmod(M, matdetint(M))

will be even faster. You can then recover U as M^{−1}H.

? M = matrix(3,4,i,j,random([-5,5]))
%1 =
[ 0 2  3  0]
[-5 3 -5 -5]
[ 4 3 -5  4]
? [H,U] = mathnf(M, 1);
? U
%3 =
[-1 0 -1 0]
[ 0 5  3 2]
[ 0 3  1 1]
[ 1 0  0 0]
? H
%5 =
[19 9 7]
[ 0 9 1]
[ 0 0 1]
? M*U
%6 =
[0 19 9 7]
[0  0 9 1]
[0  0 0 1]

For convenience, M is allowed to be a t_VEC, which is then automatically converted to a t_MAT, as per the Mat function. For instance to solve the generalized extended gcd problem, one may use

? v = [116085838, 181081878, 314252913,10346840];
? [H,U] = mathnf(v, 1);
? U
%2 =
[ 103 -603    15  -88]
[-146   13 -1208  352]
[  58  220   678 -167]
[-362 -144   381 -101]
? v*U
%3 = [0, 0, 0, 1]

This also allows to input a matrix as a t_VEC of t_COLs of the same length (which Mat would concatenate to the t_MAT having those columns):

? v = [[1,0,4]~, [3,3,4]~, [0,-4,-5]~]; mathnf(v)
%1 =
[47 32 12]
[ 0  1  0]

[ 0  0  1]
The library syntax is GEN mathnf0(GEN M, long flag). Also available are GEN hnf(GEN M)

(flag = 0) and GEN hnfall(GEN M) (flag = 1). To reduce huge relation matrices (sparse with small entries, say dimension 400 or more), you can use the pair hnfspec / hnfadd. Since this is quite technical and the calling interface may change, they are not documented yet. Look at the code in

basemath/hnf snf.c.

3.8.23 mathnfmod(x, d). If x is a (not necessarily square) matrix of maximal rank with integer entries, and d is a multiple of the (non-zero) determinant of the lattice spanned by the columns of x, finds the upper triangular Hermite normal form of x.

If the rank of x is equal to its number of rows, the result is a square matrix. In general, the columns of the result form a basis of the lattice spanned by the columns of x. Even when d is known, this is in general slower than mathnf but uses much less memory.

The library syntax is GEN hnfmod(GEN x, GEN d).

3.8.24 mathnfmodid(x, d). Outputs the (upper triangular) Hermite normal form of x concatenated with the diagonal matrix with diagonal d. Assumes that x has integer entries. Variant: if d is an integer instead of a vector, concatenate d times the identity matrix.

? m=[0,7;-1,0;-1,-1]
%1 =
[ 0  7]
[-1  0]
[-1 -1]
? mathnfmodid(m, [6,2,2])
%2 =
[2 1 1]
[0 1 0]
[0 0 1]
? mathnfmodid(m, 10)
%3 =
[10 7 3]
[ 0 1 0]
[ 0 0 1]
The library syntax is GEN hnfmodid(GEN x, GEN d).

3.8.25 mathouseholder(Q, v). applies a sequence Q of Householder transforms, as returned by matqr(M, 1) to the vector or matrix v.

The library syntax is GEN mathouseholder(GEN Q, GEN v).

3.8.26 matid(n). Creates the n × n identity matrix.

The library syntax is GEN matid(long n).

3.8.27 matimage(x, {flag = 0}). Gives a basis for the image of the matrix x as columns of a matrix. A priori the matrix can have entries of any type. If flag = 0, use standard Gauss pivot. If flag = 1, use matsupplement (much slower: keep the default flag!).

    The library syntax is GEN matimage0(GEN x, long flag). Also available is GEN image(GEN

x) (flag = 0).

3.8.28 matimagecompl(x). Gives the vector of the column indices which are not extracted by the function matimage, as a permutation (t_VECSMALL). Hence the number of components of matimagecompl(x) plus the number of columns of matimage(x) is equal to the number of columns of the matrix x.

    The library syntax is GEN imagecompl(GEN x).

3.8.29 matindexrank(x). x being a matrix of rank r, returns a vector with two t_VECSMALL components y and z of length r giving a list of rows and columns respectively (starting from 1) such that the extracted matrix obtained from these two vectors using vecextract(x, y, z) is invertible.

The library syntax is GEN indexrank(GEN x).

3.8.30 matintersect(x, y). x and y being two matrices with the same number of rows each of whose columns are independent, finds a basis of the Q-vector space equal to the intersection of the spaces spanned by the columns of x and y respectively. The faster function idealintersect can be used to intersect fractional ideals (projective Z_{K} modules of rank 1); the slower but much more general function nfhnf can be used to intersect general Z_{K}-modules.

    The library syntax is GEN intersect(GEN x, GEN y).

3.8.31 matinverseimage(x, y). Given a matrix x and a column vector or matrix y, returns a preimage z of y by x if one exists (i.e such that xz = y), an empty vector or matrix otherwise. The complete inverse image is z + Kerx, where a basis of the kernel of x may be obtained by matker.

    ? M = [1,2;2,4];
    ? matinverseimage(M, [1,2]~)
    %2 = [1, 0]~
    ? matinverseimage(M, [3,4]~)

%3 = []~ \\ no solution

    ? matinverseimage(M, [1,3,6;2,6,12])
    %4 =
    [1 3 6]
    [0 0 0]
    ? matinverseimage(M, [1,2;3,4])

%5 = [;] \\ no solution

    ? K = matker(M)
    %6 =
    [-2]
    [1]
    The library syntax is GEN inverseimage(GEN x, GEN y).

3.8.32 matisdiagonal(x). Returns true (1) if x is a diagonal matrix, false (0) if not.

    The library syntax is GEN isdiagonal(GEN x).

3.8.33 matker(x, {flag = 0}). Gives a basis for the kernel of the matrix x as columns of a matrix. The matrix can have entries of any type, provided they are compatible with the generic arithmetic operations (+, × and /).

If x is known to have integral entries, set flag = 1.

    The library syntax is GEN matker0(GEN x, long flag). Also available are GEN ker(GEN x)

(flag = 0), GEN keri(GEN x) (flag = 1). 3.8.34 matkerint(x, {flag = 0}). Gives an LLL-reduced Z-basis for the lattice equal to the kernel of the matrix x as columns of the matrix x with integer entries (rational entries are not permitted).

If flag = 0, uses an integer LLL algorithm.

If flag = 1, uses matrixqz(x, −2). Many orders of magnitude slower than the default: never use this.

    The library syntax is GEN matkerint0(GEN x, long flag). See also GEN kerint(GEN x)

(flag = 0), which is a trivial wrapper around

    ZM_lll(ZM_lll(x, 0.99, LLL_KER), 0.99, LLL_INPLACE);

Remove the outermost ZM lll if LLL-reduction is not desired (saves time). 3.8.35 matmuldiagonal(x, d). Product of the matrix x by the diagonal matrix whose diagonal entries are those of the vector d. Equivalent to, but much faster than x ∗ matdiagonal(d).

    The library syntax is GEN matmuldiagonal(GEN x, GEN d).

3.8.36 matmultodiagonal(x, y). Product of the matrices x and y assuming that the result is a diagonal matrix. Much faster than x∗y in that case. The result is undefined if x∗y is not diagonal.

    The library syntax is GEN matmultodiagonal(GEN x, GEN y).

3.8.37 matpascal(n, {q}). Creates as a matrix the lower triangular Pascal triangle of order x + 1 (i.e. with binomial coefficients up to x). If q is given, compute the q-Pascal triangle (i.e. using q-binomial coefficients).

    The library syntax is GEN matqpascal(long n, GEN q = NULL). Also available is GEN mat-
pascal(GEN x).

3.8.38 matqr(M, {flag = 0}). Returns [Q, R], the QR-decomposition of the square invertible matrix M with real entries: Q is orthogonal and R upper triangular. If flag = 1, the orthogonal matrix is returned as a sequence of Householder transforms: applying such a sequence is stabler and faster than multiplication by the corresponding Q matrix. More precisely, if

      [Q,R] = matqr(M);
      [q,r] = matqr(M, 1);

then r = R and mathouseholder(q, M) is R; furthermore

      mathouseholder(q, matid(#M)) == Q~

the inverse of Q. This function raises an error if the precision is too low or x is singular.

    The library syntax is GEN matqr(GEN M, long flag, long prec).

3.8.39 matrank(x). Rank of the matrix x.

The library syntax is long rank(GEN x).

3.8.40 matrix(m, n, {X}, {Y }, {expr = 0}). Creation of the m × n matrix whose coefficients are given by the expression expr. There are two formal parameters in expr, the first one (X) corresponding to the rows, the second (Y) to the columns, and X goes from 1 to m, Y goes from 1 to n. If one of the last 3 parameters is omitted, fill the matrix with zeroes.

3.8.41 matrixqz(A, {p = 0}). A being an m × n matrix in M_{m,n}(Q), let Im_{Q}A (resp. Im_{Z}A) the Q-vector space (resp. the Z-module) spanned by the columns of A. This function has varying behavior depending on the sign of p:

If p ≥ 0, A is assumed to have maximal rank n ≤ m. The function returns a matrix B ∈ M_{m,n}(Z), with Im_{Q}B = Im_{Q}A, such that the GCD of all its n × n minors is coprime to p; in particular, if p = 0 (default), this GCD is 1.

? minors(x) = vector(#x[,1], i, matdet(x[^i,]));
? A = [3,1/7; 5,3/7; 7,5/7]; minors(A)
%1 = [4/7, 8/7, 4/7]   \\ determinants of all 2x2 minors
? B = matrixqz(A)
%2 =
[3 1]
[5 2]
[7 3]
? minors(%)
%3 = [1, 2, 1]   \\ B integral with coprime minors

If p = −1, returns the HNF basis of the lattice Z^{n} ∩ Im_{Z}A.

If p = −2, returns the HNF basis of the lattice Z^{n} ∩ Im_{Q}A.

? matrixqz(A,-1)
%4 =
[8 5]
[4 3]
[0 1]
? matrixqz(A,-2)
%5 =
[2 -1]
[1 0]
[0 1]
The library syntax is GEN matrixqz0(GEN A, GEN p = NULL).

3.8.42 matsize(x). x being a vector or matrix, returns a row vector with two components, the first being the number of rows (1 for a row vector), the second the number of columns (1 for a column vector).

The library syntax is GEN matsize(GEN x).

3.8.43 matsnf(X, {flag = 0}). If X is a (singular or non-singular) matrix outputs the vector of elementary divisors of X, i.e. the diagonal of the Smith normal form of X, normalized so that d_{n} | d_{n−1} | … | d_{1}.

The binary digits of flag mean:

1 (complete output): if set, outputs [U, V, D], where U and V are two unimodular matrices such that UXV is the diagonal matrix D. Otherwise output only the diagonal of D. If X is not a square matrix, then D will be a square diagonal matrix padded with zeros on the left or the top.

2 (generic input): if set, allows polynomial entries, in which case the input matrix must be square. Otherwise, assume that X has integer coefficients with arbitrary shape.

4 (cleanup): if set, cleans up the output. This means that elementary divisors equal to 1 will be deleted, i.e. outputs a shortened vector D′ instead of D. If complete output was required, returns [U′, V′, D′] so that U′XV′ = D′ holds. If this flag is set, X is allowed to be of the form ‘vector of elementary divisors’ or [U, V, D] as would normally be output with the cleanup flag unset.

    The library syntax is GEN matsnf0(GEN X, long flag).

3.8.44 matsolve(M, B). M being an invertible matrix and B a column vector, finds the solution X of MX = B, using Dixon p-adic lifting method if M and B are integral and Gaussian elimination otherwise. This has the same effect as, but is faster, than M^{−1} ∗ B.

The library syntax is GEN gauss(GEN M, GEN B). For integral input, the function GEN

ZM_gauss(GEN M,GEN B) is also available.

3.8.45 matsolvemod(M, D, B, {flag = 0}). M being any integral matrix, D a column vector of non-negative integer moduli, and B an integral column vector, gives a small integer solution to the system of congruences ∑_{i} m_{i,j}x_{j} ≡ b_{i} (mod d_{i}) if one exists, otherwise returns zero. Shorthand notation: B (resp. D) can be given as a single integer, in which case all the b_{i} (resp. d_{i}) above are taken to be equal to B (resp. D).

    ? M = [1,2;3,4];
    ? matsolvemod(M, [3,4]~, [1,2]~)
    %2 = [-2, 0]~
    ? matsolvemod(M, 3, 1) \\ M X = [1,1]~ over F_3
    %3 = [-1, 1]~
    ? matsolvemod(M, [3,0]~, [1,2]~) \\ x + 2y = 1 (mod 3), 3x + 4y = 2 (in Z)
    %4 = [6, -4]~

If flag = 1, all solutions are returned in the form of a two-component row vector [x, u], where x is a small integer solution to the system of congruences and u is a matrix whose columns give a basis of the homogeneous system (so that all solutions can be obtained by adding x to any linear combination of columns of u). If no solution exists, returns zero.

    The library syntax is GEN matsolvemod0(GEN M, GEN D, GEN B, long flag). Also available
are GEN gaussmodulo(GEN M, GEN D, GEN B) (flag = 0) and GEN gaussmodulo2(GEN M, GEN D,

GEN B) (flag = 1).

3.8.46 matsupplement(x). Assuming that the columns of the matrix x are linearly independent (if they are not, an error message is issued), finds a square invertible matrix whose first columns are the columns of x, i.e. supplement the columns of x to a basis of the whole space.

? matsupplement([1;2])
%1 =
[1 0]
[2 1]

Raises an error if x has 0 columns, since (due to a long standing design bug), the dimension of the ambient space (the number of rows) is unknown in this case:

? matsupplement(matrix(2,0))
  ***   at top-level: matsupplement(matrix
  ***                 ^--------------------
  *** matsupplement: sorry, suppl [empty matrix] is not yet implemented.

The library syntax is GEN suppl(GEN x).

3.8.47 mattranspose(x). Transpose of x (also x˜ ). This has an effect only on vectors and matrices.

The library syntax is GEN gtrans(GEN x).

3.8.48 minpoly(A, {v =′ x}). minimal polynomial of A with respect to the variable v., i.e. the monic polynomial P of minimal degree (in the variable v) such that P (A) = 0.

The library syntax is GEN minpoly(GEN A, long v = -1), where v is a variable number.

3.8.49 norml2(x). Square of the L^{2}-norm of x. More precisely, if x is a scalar, norml2(x) is defined to be the square of the complex modulus of x (real t_QUADs are not supported). If x is a polynomial, a (row or column) vector or a matrix, norml2(x) is defined recursively as ∑_{i} norml2(x_{i}), where (x_{i}) run through the components of x. In particular, this yields the usual ∑ |x_{i}|^{2} (resp. ∑ |x_{i,j}|^{2}) if x is a polynomial or vector (resp. matrix) with complex components.

? norml2( [ 1, 2, 3 ] )      \\ vector
%1 = 14
? norml2( [ 1, 2; 3, 4] )   \\ matrix
%2 = 30
? norml2( 2*I + x )
%3 = 5
? norml2( [ [1,2], [3,4], 5, 6 ] )   \\ recursively defined
%4 = 91

The library syntax is GEN gnorml2(GEN x).

3.8.50 normlp(x, {p}). L^{p}-norm of x; sup norm if p is omitted. More precisely, if x is a scalar, normlp(x, p) is defined to be abs(x). If x is a polynomial, a (row or column) vector or a matrix:

• if p is omitted, normlp(x) is defined recursively as max_{i} normlp(x_{i})), where (x_{i}) run through the components of x. In particular, this yields the usual sup norm if x is a polynomial or vector with complex components.

• otherwise, normlp(x, p) is defined recursively as (∑_{i} normlp^{p}(x_{i}, p))^{1/p}. In particular, this yields the usual (∑ |x_{i}|^{p})^{1/p} if x is a polynomial or vector with complex components.

? v = [1,-2,3]; normlp(v)      \\ vector
%1 = 3
? M = [1,-2;-3,4]; normlp(M)   \\ matrix
%2 = 4
? T = (1+I) + I*x^2; normlp(T)
%3 = 1.4142135623730950488016887242096980786
? normlp([[1,2], [3,4], 5, 6])   \\ recursively defined
%4 = 6
? normlp(v, 1)
%5 = 6
? normlp(M, 1)
%6 = 10
? normlp(T, 1)
%7 = 2.4142135623730950488016887242096980786
The library syntax is GEN gnormlp(GEN x, GEN p = NULL, long prec).

3.8.51 qfauto(G, {fl}). G being a square and symmetric matrix with integer entries representing a positive definite quadratic form, outputs the automorphism group of the associate lattice. Since this requires computing the minimal vectors, the computations can become very lengthy as the dimension grows. G can also be given by an qfisominit structure. See qfisominit for the meaning of fl.

The output is a two-components vector [o, g] where o is the group order and g is the list of generators (as a vector). For each generator H, the equality G = ^{t}HGH holds.

The interface of this function is experimental and will likely change in the future.

This function implements an algorithm of Plesken and Souvignier, following Souvignier’s implementation.

The library syntax is GEN qfauto0(GEN G, GEN fl = NULL). Also available is GEN

qfauto(GEN G, GEN fl) where G is a vector of zm.

3.8.52 qfautoexport(qfa, {flag}). qfa being an automorphism group as output by qfauto, export the underlying matrix group as a string suitable for (no flags or flag = 0) GAP or (flag = 1) Magma. The following example computes the size of the matrix group using GAP:

? G = qfauto([2,1;1,2])
%1 = [12, [[-1, 0; 0, -1], [0, -1; 1, 1], [1, 1; 0, -1]]]
? s = qfautoexport(G)
%2 = "Group([[-1, 0], [0, -1]], [[0, -1], [1, 1]], [[1, 1], [0, -1]])"
? extern("echo \"Order("s");\" | gap -q")
%3 = 12
The library syntax is GEN qfautoexport(GEN qfa, long flag).

3.8.53 qfbil(x, y, {q}). Evaluate the bilinear form q (symmetric matrix) at the vectors (x, y); if q omitted, use the standard Euclidean scalar product, corresponding to the identity matrix.

Roughly equivalent to x~* q * y, but a little faster and more convenient (does not distinguish between column and row vectors):

? x = [1,2,3]~; y = [-1,0,1]~; qfbil(x,y)
%1 = 2
? q = [1,2,3;2,2,-1;3,-1,0]; qfbil(x,y, q)
%2 = -13
? for(i=1,10^6, qfbil(x,y,q))
%3 = 568ms
? for(i=1,10^6, x~*q*y)
%4 = 717ms

The associated quadratic form is also available, as qfnorm, slightly faster:

? for(i=1,10^6, qfnorm(x,q))
time = 444ms
? for(i=1,10^6, qfnorm(x))
time = 176 ms.
? for(i=1,10^6, qfbil(x,y))
time = 208 ms.
The library syntax is GEN qfbil(GEN x, GEN y, GEN q = NULL).

3.8.54 qfgaussred(q). decomposition into squares of the quadratic form represented by the symmetric matrix q. The result is a matrix whose diagonal entries are the coefficients of the squares, and the off-diagonal entries on each line represent the bilinear forms. More precisely, if (a_{ij}) denotes the output, one has

q(x) = ∑_{i} a_{ii}(x_{i} + ∑_{j≠i} a_{ij}x_{j})^{2}

? qfgaussred([0,1;1,0])
%1 =
[1/2 1]
[-1 -1/2]

This means that 2xy = (1/2)(x + y)^{2} − (1/2)(x − y)^{2}.

The library syntax is GEN qfgaussred(GEN q). GEN qfgaussred_positive(GEN q) assumes

that q is positive definite and is a little faster; returns NULL if a vector with negative norm occurs (non positive matrix or too many rounding errors).

3.8.55 qfisom(G, H, {fl}). G, H being square and symmetric matrices with integer entries representing positive definite quadratic forms, return an invertible matrix S such that G = ^{t}SHS. This defines a isomorphism between the corresponding lattices. Since this requires computing the minimal vectors, the computations can become very lengthy as the dimension grows. See qfisominit for the meaning of fl.

G can also be given by an qfisominit structure which is preferable if several forms H need to be compared to G.

This function implements an algorithm of Plesken and Souvignier, following Souvignier’s implementation.

The library syntax is GEN qfisom0(GEN G, GEN H, GEN fl = NULL). Also available is GEN
qfisom(GEN G, GEN H, GEN fl) where G is a vector of zm, and H is a zm.

3.8.56 qfisominit(G, {fl}). G being a square and symmetric matrix with integer entries representing a positive definite quadratic form, return an isom structure allowing to compute isomorphisms between G and other quadratic forms faster.

The interface of this function is experimental and will likely change in future release.

If present, the optional parameter fl must be a t_VEC with two components. It allows to specify the invariants used, which can make the computation faster or slower. The components are

• fl[1] Depth of scalar product combination to use.

• fl[2] Maximum level of Bacher polynomials to use.

Since this function computes the minimal vectors, it can become very lengthy as the dimension of G grows.

The library syntax is GEN qfisominit0(GEN G, GEN fl = NULL). Also available is GEN qfi-
sominit(GEN F, GEN fl) where F is a vector of zm.

3.8.57 qfjacobi(A). Apply Jacobi’s eigenvalue algorithm to the real symmetric matrix A. This returns [L, V], where

• L is the vector of (real) eigenvalues of A, sorted in increasing order,

• V is the corresponding orthogonal matrix of eigenvectors of A.

? \p19
? A = [1,2;2,1]; mateigen(A)
%1 =
[-1 1]
[ 1 1]
? [L, H] = qfjacobi(A);
? L
%3 = [-1.000000000000000000, 3.000000000000000000]~
? H
%4 =

    [ 0.7071067811865475245 0.7071067811865475244]
    [-0.7071067811865475244 0.7071067811865475245]
    ? norml2( (A-L[1])*H[,1] )       \\ approximate eigenvector
    %5 = 9.403954806578300064 E-38
    ? norml2(H*H~ - 1)
    %6 = 2.350988701644575016 E-38   \\ close to orthogonal
    The library syntax is GEN jacobi(GEN A, long prec).

3.8.58 qflll(x, {flag = 0}). LLL algorithm applied to the columns of the matrix x. The columns of x may be linearly dependent. The result is a unimodular transformation matrix T such that x · T is an LLL-reduced basis of the lattice generated by the column vectors of x. Note that if x is not of maximal rank T will not be square. The LLL parameters are (0.51, 0.99), meaning that the Gram-Schmidt coefficients for the final basis satisfy μ_{i,j} ≤ |0.51|, and the Lovász’s constant is 0.99.

If flag = 0 (default), assume that x has either exact (integral or rational) or real floating point entries. The matrix is rescaled, converted to integers and the behavior is then as in flag = 1.

If flag = 1, assume that x is integral. Computations involving Gram-Schmidt vectors are approximate, with precision varying as needed (Lehmer’s trick, as generalized by Schnorr). Adapted from Nguyen and Stehlé’s algorithm and Stehlé’s code (fplll-1.3).

If flag = 2, x should be an integer matrix whose columns are linearly independent. Returns a partially reduced basis for x, using an unpublished algorithm by Peter Montgomery: a basis is said to be partially reduced if |v_{i} ± v_{j}| ≥ |v_{i}| for any two distinct basis vectors v_{i}, v_{j}.

This is faster than flag = 1, esp. when one row is huge compared to the other rows (knapsackstyle), and should quickly produce relatively short vectors. The resulting basis is not LLL-reduced in general. If LLL reduction is eventually desired, avoid this partial reduction: applying LLL to the partially reduced matrix is significantly slower than starting from a knapsack-type lattice.

If flag = 4, as flag = 1, returning a vector [K, T] of matrices: the columns of K represent a basis of the integer kernel of x (not LLL-reduced in general) and T is the transformation matrix such that x · T is an LLL-reduced Z-basis of the image of the matrix x.

If flag = 5, case as case 4, but x may have polynomial coefficients.

If flag = 8, same as case 0, but x may have polynomial coefficients.

    The library syntax is GEN qflll0(GEN x, long flag). Also available are GEN lll(GEN x)
(flag = 0), GEN lllint(GEN x) (flag = 1), and GEN lllkerim(GEN x) (flag = 4).

3.8.59 qflllgram(G, {flag = 0}). Same as qflll, except that the matrix G = x~ ∗ x is the Gram matrix of some lattice vectors x, and not the coordinates of the vectors themselves. In particular, G must now be a square symmetric real matrix, corresponding to a positive quadratic form (not necessarily definite: x needs not have maximal rank). The result is a unimodular transformation matrix T such that x · T is an LLL-reduced basis of the lattice generated by the column vectors of x. See qflll for further details about the LLL implementation.

If flag = 0 (default), assume that G has either exact (integral or rational) or real floating point entries. The matrix is rescaled, converted to integers and the behavior is then as in flag = 1.

If flag = 1, assume that G is integral. Computations involving Gram-Schmidt vectors are approximate, with precision varying as needed (Lehmer’s trick, as generalized by Schnorr). Adapted from Nguyen and Stehlé’s algorithm and Stehlé’s code (fplll-1.3). flag = 4: G has integer entries, gives the kernel and reduced image of x.

flag = 5: same as 4, but G may have polynomial coefficients.

    The library syntax is GEN qflllgram0(GEN G, long flag). Also available are GEN lll-
gram(GEN G) (flag = 0), GEN lllgramint(GEN G) (flag = 1), and GEN lllgramkerim(GEN G)

(flag = 4).

3.8.60 qfminim(x, {b}, {m}, {flag = 0}). x being a square and symmetric matrix representing a positive definite quadratic form, this function deals with the vectors of x whose norm is less than or equal to b, enumerated using the Fincke-Pohst algorithm, storing at most m vectors (no limit if m is omitted). The function searches for the minimal non-zero vectors if b is omitted. The behavior is undefined if x is not positive definite (a “precision too low” error is most likely, although more precise error messages are possible). The precise behavior depends on flag.

If flag = 0 (default), seeks at most 2m vectors. The result is a three-component vector, the first component being the number of vectors found, the second being the maximum norm found, and the last vector is a matrix whose columns are the vectors found, only one being given for each pair ±v (at most m such pairs, unless m was omitted). The vectors are returned in no particular order.

If flag = 1, ignores m and returns [N, v], where v is a non-zero vector of length N ≤ b, or [] if no non-zero vector has length ≤ b. If no explicit b is provided, return a vector of smallish norm (smallest vector in an LLL-reduced basis).

In these two cases, x must have integral entries. The implementation uses low precision floating point computations for maximal speed, which gives incorrect result when x has large entries. (The condition is checked in the code and the routine raises an error if large rounding errors occur.) A more robust, but much slower, implementation is chosen if the following flag is used:

If flag = 2, x can have non integral real entries. In this case, if b is omitted, the “minimal” vectors only have approximately the same norm. If b is omitted, m is an upper bound for the number of vectors that will be stored and returned, but all minimal vectors are nevertheless enumerated. If m is omitted, all vectors found are stored and returned; note that this may be a huge vector!

    ? x = matid(2);

? qfminim(x) \\ 4 minimal vectors of norm 1: ±[0, 1], ±[1, 0]

    %2 = [4, 1, [0, 1; 1, 0]]
    ? { x =
    [4, 2, 0, 0, 0,-2, 0, 0, 0, 0, 0, 0, 1,-1, 0, 0, 0, 1, 0,-1, 0, 0, 0,-2;
     2, 4,-2,-2, 0,-2, 0, 0, 0, 0, 0, 0, 0,-1, 0, 0, 0, 0, 0,-1, 0, 1,-1,-1;
     0,-2, 4, 0,-2, 0, 0, 0, 0, 0, 0, 0,-1, 1, 0, 0, 1, 0, 0, 1,-1,-1, 0, 0;
     0,-2, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 1,-1, 0, 0, 0, 1,-1, 0, 1,-1, 1, 0;
     0, 0,-2, 0, 4, 0, 0, 0, 1,-1, 0, 0, 1, 0, 0, 0,-2, 0, 0,-1, 1, 1, 0, 0;
    -2, -2,0, 0, 0, 4,-2, 0,-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,-1, 1, 1;
     0, 0, 0, 0, 0,-2, 4,-2, 0, 0, 0, 0, 0, 1, 0, 0, 0,-1, 0, 0, 0, 1,-1, 0;
     0, 0, 0, 0, 0, 0,-2, 4, 0, 0, 0, 0,-1, 0, 0, 0, 0, 0,-1,-1,-1, 0, 1, 0;
     0, 0, 0, 0, 1,-1, 0, 0, 4, 0,-2, 0, 1, 1, 0,-1, 0, 1, 0, 0, 0, 0, 0, 0;
     0, 0, 0, 0,-1, 0, 0, 0, 0, 4, 0, 0, 1, 1,-1, 1, 0, 0, 0, 1, 0, 0, 1, 0;
     0, 0, 0, 0, 0, 0, 0, 0,-2, 0, 4,-2, 0,-1, 0, 0, 0,-1, 0,-1, 0, 0, 0, 0;
     0, 0, 0, 0, 0, 0, 0, 0, 0, 0,-2, 4,-1, 1, 0, 0,-1, 1, 0, 1, 1, 1,-1, 0;
     1, 0,-1, 1, 1, 0, 0,-1, 1, 1, 0,-1, 4, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1,-1;

-1,-1, 1,-1, 0, 0, 1, 0, 1, 1,-1, 1, 0, 4, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1;
 0, 0, 0, 0, 0, 0, 0, 0, 0,-1, 0, 0, 0, 1, 4, 0, 0, 0, 1, 0, 0, 0, 0, 0;
 0, 0, 0, 0, 0, 0, 0, 0,-1, 1, 0, 0, 1, 1, 0, 4, 0, 0, 0, 0, 1, 1, 0, 0;
 0, 0, 1, 0,-2, 0, 0, 0, 0, 0, 0,-1, 0, 0, 0, 0, 4, 1, 1, 1, 0, 0, 1, 1;
 1, 0, 0, 1, 0, 0,-1, 0, 1, 0,-1, 1, 1, 0, 0, 0, 1, 4, 0, 1, 1, 0, 1, 0;
 0, 0, 0,-1, 0, 1, 0,-1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 4, 0, 1, 1, 0, 1;
-1, -1,1, 0,-1, 1, 0,-1, 0, 1,-1, 1, 0, 1, 0, 0, 1, 1, 0, 4, 0, 0, 1, 1;
 0, 0,-1, 1, 1, 0, 0,-1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 4, 1, 0, 1;
 0, 1,-1,-1, 1,-1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 4, 0, 1;
 0,-1, 0, 1, 0, 1,-1, 1, 0, 1, 0,-1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 4, 1;
-2,-1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,-1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 4]; }
? qfminim(x,,0)  \\ the Leech lattice has 196560 minimal vectors of norm 4
time = 648 ms.
%4 = [196560, 4, [;]]
? qfminim(x,,0,2); \\ safe algorithm. Slower and unnecessary here.
time = 18,161 ms.
%5 = [196560, 4.000061035156250000, [;]]

In the last example, we store 0 vectors to limit memory use. All minimal vectors are nevertheless enumerated. Provided parisize is about 50MB, qfminim(x) succeeds in 2.5 seconds.

The library syntax is GEN qfminim0(GEN x, GEN b = NULL, GEN m = NULL, long flag,
long prec). Also available are GEN minim(GEN x, GEN b = NULL, GEN m = NULL) (flag = 0),
GEN minim2(GEN x, GEN b = NULL, GEN m = NULL) (flag = 1). GEN minim_raw(GEN x, GEN b

= NULL, GEN m = NULL) (do not perform LLL reduction on x).

3.8.61 qfnorm(x, {q}). Evaluate the binary quadratic form q (symmetric matrix) at the vector x. If q omitted, use the standard Euclidean form, corresponding to the identity matrix.

Equivalent to x~* q * x, but about twice faster and more convenient (does not distinguish between column and row vectors):

? x = [1,2,3]~; qfnorm(x)
%1 = 14
? q = [1,2,3;2,2,-1;3,-1,0]; qfnorm(x, q)
%2 = 23
? for(i=1,10^6, qfnorm(x,q))
time = 384ms.
? for(i=1,10^6, x~*q*x)
time = 729ms.

We also allow t_MATs of compatible dimensions for x, and return x~* q * x in this case as well:

? M = [1,2,3;4,5,6;7,8,9]; qfnorm(M) \\ Gram matrix
%5 =
[66  78  90]
[78  93 108]
[90 108 126]
? for(i=1,10^6, qfnorm(M,q))
time = 2,144 ms.
? for(i=1,10^6, M~*q*M)

time = 2,793 ms.

The polar form is also available, as qfbil.

The library syntax is GEN qfnorm(GEN x, GEN q = NULL).

3.8.62 qfperfection(G). G being a square and symmetric matrix with integer entries representing a positive definite quadratic form, outputs the perfection rank of the form. That is, gives the rank of the family of the s symmetric matrices v_{i}v_{i}^{t}, where s is half the number of minimal vectors and the v_{i} (1 ≤ i ≤ s) are the minimal vectors.

Since this requires computing the minimal vectors, the computations can become very lengthy as the dimension of x grows.

The library syntax is GEN perf(GEN G).

3.8.63 qfrep(q, B, {flag = 0}). q being a square and symmetric matrix with integer entries representing a positive definite quadratic form, count the vectors representing successive integers.

• If flag = 0, count all vectors. Outputs the vector whose i-th entry, 1 ≤ i ≤ B is half the number of vectors v such that q(v) = i.

• If flag = 1, count vectors of even norm. Outputs the vector whose i-th entry, 1 ≤ i ≤ B is half the number of vectors such that q(v) = 2i.

? q = [2, 1; 1, 3];
? qfrep(q, 5)
%2 = Vecsmall([0, 1, 2, 0, 0]) \\ 1 vector of norm 2, 2 of norm 3, etc.
? qfrep(q, 5, 1)
%3 = Vecsmall([1, 0, 0, 1, 0]) \\ 1 vector of norm 2, 0 of norm 4, etc.

This routine uses a naive algorithm based on qfminim, and will fail if any entry becomes larger than 2^{31} (or 2^{63}).

The library syntax is GEN qfrep0(GEN q, GEN B, long flag).

3.8.64 qfsign(x). Returns [p, m] the signature of the quadratic form represented by the symmetric matrix x. Namely, p (resp. m) is the number of positive (resp. negative) eigenvalues of x.The result is computed using Gaussian reduction.

The library syntax is GEN qfsign(GEN x).

3.8.65 seralgdep(s, p, r). finds a linear relation between powers (1, s, …, s^{p}) of the series s, with polynomial coefficients of degree ≤ r. In case no relation is found, return 0.

? s = 1 + 10*y - 46*y^2 + 460*y^3 - 5658*y^4 + 77740*y^5 + O(y^6);
? seralgdep(s, 2, 2)
%2 = -x^2 + (8*y^2 + 20*y + 1)
? subst(%, x, s)
%3 = O(y^6)
? seralgdep(s, 1, 3)
%4 = (-77*y^2 - 20*y - 1)*x + (310*y^3 + 231*y^2 + 30*y + 1)
? seralgdep(s, 1, 2)
%5 = 0

The series main variable must not be x, so as to be able to express the result as a polynomial in x.

The library syntax is GEN seralgdep(GEN s, long p, long r).

3.8.66 setbinop(f, X, {Y }). The set whose elements are the f(x,y), where x,y run through X,Y. respectively. If Y is omitted, assume that X = Y and that f is symmetric: f(x, y) = f(y, x) for all x, y in X.

? X = [1,2,3]; Y = [2,3,4];
? setbinop((x,y)->x+y, X,Y) \\ set X + Y
%2 = [3, 4, 5, 6, 7]
? setbinop((x,y)->x-y, X,Y) \\ set X - Y
%3 = [-3, -2, -1, 0, 1]
? setbinop((x,y)->x+y, X)   \\ set 2X = X + X
%2 = [2, 3, 4, 5, 6]
The library syntax is GEN setbinop(GEN f, GEN X, GEN Y = NULL).

3.8.67 setintersect(x, y). Intersection of the two sets x and y (see setisset). If x or y is not a set, the result is undefined.

The library syntax is GEN setintersect(GEN x, GEN y).

3.8.68 setisset(x). Returns true (1) if x is a set, false (0) if not. In PARI, a set is a row vector whose entries are strictly increasing with respect to a (somewhat arbitrary) universal comparison function. To convert any object into a set (this is most useful for vectors, of course), use the function Set.

? a = [3, 1, 1, 2];
? setisset(a)
%2 = 0
? Set(a)
%3 = [1, 2, 3]

The library syntax is long setisset(GEN x).

3.8.69 setminus(x, y). Difference of the two sets x and y (see setisset), i.e. set of elements of x which do not belong to y. If x or y is not a set, the result is undefined.

The library syntax is GEN setminus(GEN x, GEN y).

3.8.70 setsearch(S, x, {flag = 0}). Determines whether x belongs to the set S (see setisset).

We first describe the default behaviour, when flag is zero or omitted. If x belongs to the set S, returns the index j such that S[j] = x, otherwise returns 0.

? T = [7,2,3,5]; S = Set(T);
? setsearch(S, 2)
%2 = 1
? setsearch(S, 4)      \\ not found
%3 = 0
? setsearch(T, 7)      \\ search in a randomly sorted vector
%4 = 0 \\ WRONG !

If S is not a set, we also allow sorted lists with respect to the cmp sorting function, without repeated entries, as per listsort(L, 1); otherwise the result is undefined.

? L = List([1,4,2,3,2]); setsearch(L, 4)

%1 = 0 \\ WRONG !
? listsort(L, 1); L    \\ sort L first
%2 = List([1, 2, 3, 4])
? setsearch(L, 4)
%3 = 4                 \\ now correct

If flag is non-zero, this function returns the index j where x should be inserted, and 0 if it already belongs to S. This is meant to be used for dynamically growing (sorted) lists, in conjunction with

listinsert.
? L = List([1,5,2,3,2]); listsort(L,1); L
%1 = List([1,2,3,5])
? j = setsearch(L, 4, 1)  \\ 4 should have been inserted at index j
%2 = 4
? listinsert(L, 4, j); L
%3 = List([1, 2, 3, 4, 5])
The library syntax is long setsearch(GEN S, GEN x, long flag).

3.8.71 setunion(x, y). Union of the two sets x and y (see setisset). If x or y is not a set, the result is undefined.

The library syntax is GEN setunion(GEN x, GEN y).

3.8.72 trace(x). This applies to quite general x. If x is not a matrix, it is equal to the sum of x and its conjugate, except for polmods where it is the trace as an algebraic number.

For x a square matrix, it is the ordinary trace. If x is a non-square matrix (but not a vector), an error occurs.

The library syntax is GEN gtrace(GEN x).

3.8.73 vecextract(x, y, {z}). Extraction of components of the vector or matrix x according to y. In case x is a matrix, its components are the columns of x. The parameter y is a component specifier, which is either an integer, a string describing a range, or a vector.

If y is an integer, it is considered as a mask: the binary bits of y are read from right to left, but correspond to taking the components from left to right. For example, if y = 13 = (1101)_{2} then the components 1,3 and 4 are extracted.

If y is a vector (t_VEC, t_COL or t_VECSMALL), which must have integer entries, these entries correspond to the component numbers to be extracted, in the order specified.

If y is a string, it can be

• a single (non-zero) index giving a component number (a negative index means we start counting from the end).

• a range of the form "a..b", where a and b are indexes as above. Any of a and b can be omitted; in this case, we take as default values a = 1 and b = −1, i.e. the first and last components respectively. We then extract all components in the interval [a, b], in reverse order if b < a.

In addition, if the first character in the string is ^, the complement of the given set of indices is taken.

If z is not omitted, x must be a matrix. y is then the row specifier, and z the column specifier, where the component specifier is as explained above.

? v = [a, b, c, d, e];
? vecextract(v, 5)         \\ mask
%1 = [a, c]
? vecextract(v, [4, 2, 1]) \\ component list
%2 = [d, b, a]
? vecextract(v, "2..4")    \\ interval
%3 = [b, c, d]
? vecextract(v, "-1..-3")  \\ interval + reverse order
%4 = [e, d, c]
? vecextract(v, "^2")      \\ complement
%5 = [a, c, d, e]
? vecextract(matid(3), "2..", "..")
%6 =
[0 1 0]
[0 0 1]

The range notations v[i..j] and v[^i] (for t_VEC or t_COL) and M[i..j, k..l] and friends (for t_MAT) implement a subset of the above, in a simpler and faster way, hence should be preferred in most common situations. The following features are not implemented in the range notation:

• reverse order,

• omitting either a or b in a..b.

The library syntax is GEN extract0(GEN x, GEN y, GEN z = NULL).

3.8.74 vecsearch(v, x, {cmpf }). Determines whether x belongs to the sorted vector or list v: return the (positive) index where x was found, or 0 if it does not belong to v.

If the comparison function cmpf is omitted, we assume that v is sorted in increasing order, according to the standard comparison function <, thereby restricting the possible types for x and the elements of v (integers, fractions or reals).

If cmpf is present, it is understood as a comparison function and we assume that v is sorted according to it, see vecsort for how to encode comparison functions.

? v = [1,3,4,5,7];
? vecsearch(v, 3)
%2 = 2
? vecsearch(v, 6)
%3 = 0 \\ not in the list
? vecsearch([7,6,5], 5) \\ unsorted vector: result undefined
%4 = 0

By abuse of notation, x is also allowed to be a matrix, seen as a vector of its columns; again by abuse of notation, a t_VEC is considered as part of the matrix, if its transpose is one of the matrix columns.

? v = vecsort([3,0,2; 1,0,2]) \\ sort matrix columns according to lex order
%1 =
[0 2 3]

[0 2 1]
? vecsearch(v, [3,1]~)
%2 = 3
? vecsearch(v, [3,1])  \\ can search for x or x~
%3 = 3
? vecsearch(v, [1,2])
%4 = 0 \\ not in the list
The library syntax is long vecsearch(GEN v, GEN x, GEN cmpf = NULL).

3.8.75 vecsort(x, {cmpf }, {flag = 0}). Sorts the vector x in ascending order, using a mergesort method. x must be a list, vector or matrix (seen as a vector of its columns). Note that mergesort is stable, hence the initial ordering of “equal” entries (with respect to the sorting criterion) is not changed.

If cmpf is omitted, we use the standard comparison function lex, thereby restricting the possible types for the elements of x (integers, fractions or reals and vectors of those). If cmpf is present, it is understood as a comparison function and we sort according to it. The following possibilities exist:

• an integer k: sort according to the value of the k-th subcomponents of the components of x.

• a vector: sort lexicographically according to the components listed in the vector. For example, if cmpf = [2, 1, 3], sort with respect to the second component, and when these are equal, with respect to the first, and when these are equal, with respect to the third.

• a comparison function (t_CLOSURE), with two arguments x and y, and returning an integer which is < 0, > 0 or = 0 if x < y, x > y or x = y respectively. The sign function is very useful in this context:

? vecsort([3,0,2; 1,0,2]) \\ sort columns according to lex order
%1 =
[0 2 3]
[0 2 1]
? vecsort(v, (x,y)->sign(y-x))            \\ reverse sort
? vecsort(v, (x,y)->sign(abs(x)-abs(y)))  \\ sort by increasing absolute value
? cmpf(x,y) = my(dx = poldisc(x), dy = poldisc(y)); sign(abs(dx) - abs(dy))
? vecsort([x^2+1, x^3-2, x^4+5*x+1], cmpf)

The last example used the named cmpf instead of an anonymous function, and sorts polynomials with respect to the absolute value of their discriminant. A more efficient approach would use precomputations to ensure a given discriminant is computed only once:

? DISC = vector(#v, i, abs(poldisc(v[i])));
? perm = vecsort(vector(#v,i,i), (x,y)->sign(DISC[x]-DISC[y]))
? vecextract(v, perm)

Similar ideas apply whenever we sort according to the values of a function which is expensive to compute.

The binary digits of flag mean:

• 1: indirect sorting of the vector x, i.e. if x is an n-component vector, returns a permutation of [1, 2, …, n] which applied to the components of x sorts x in increasing order. For example,

vecextract(x, vecsort(x,,1)) is equivalent to vecsort(x).

• 4: use descending instead of ascending order.

• 8: remove “duplicate” entries with respect to the sorting function (keep the first occurring entry). For example:

      ? vecsort([Pi,Mod(1,2),z], (x,y)->0, 8)   \\ make everything compare equal
      %1 = [3.141592653589793238462643383]
      ? vecsort([[2,3],[0,1],[0,3]], 2, 8)
      %2 = [[0, 1], [2, 3]]
    The library syntax is GEN vecsort0(GEN x, GEN cmpf = NULL, long flag).

3.8.76 vecsum(v). Return the sum of the component of the vector v

The library syntax is GEN vecsum(GEN v).

3.8.77 vector(n, {X}, {expr = 0}). Creates a row vector (type t_VEC) with n components whose components are the expression expr evaluated at the integer points between 1 and n. If one of the last two arguments is omitted, fill the vector with zeroes.

Avoid modifying X within expr; if you do, the formal variable still runs from 1 to n. In particular, vector(n,i,expr) is not equivalent to

    v = vector(n)
    for (i = 1, n, v[i] = expr)

as the following example shows:

    n = 3
    v = vector(n); vector(n, i, i++)            ----> [2, 3, 4]
    v = vector(n); for (i = 1, n, v[i] = i++)   ----> [2, 0, 4]

3.8.78 vectorsmall(n, {X}, {expr = 0}). Creates a row vector of small integers (type t_VECSMALL) with n components whose components are the expression expr evaluated at the integer points between 1 and n. If one of the last two arguments is omitted, fill the vector with zeroes.

3.8.79 vectorv(n, {X}, {expr = 0}). As vector, but returns a column vector (type t_COL).

3.9 Sums, products, integrals and similar functions.

Although the gp calculator is programmable, it is useful to have a number of preprogrammed loops, including sums, products, and a certain number of recursions. Also, a number of functions from numerical analysis like numerical integration and summation of series will be described here.

One of the parameters in these loops must be the control variable, hence a simple variable name. In the descriptions, the letter X will always denote any simple variable name, and represents the formal parameter used in the function. The expression to be summed, integrated, etc. is any legal PARI expression, including of course expressions using loops.

Library mode. Since it is easier to program directly the loops in library mode, these functions are mainly useful for GP programming. On the other hand, numerical routines code a function (to be integrated, summed, etc.) with two parameters named

      GEN (*eval)(void*,GEN)
      void *E;  \\ context: eval(E, x) must evaluate your function at x.

see the Libpari manual for details.

Numerical integration. Starting with version 2.2.9 the “double exponential” univariate integration method is implemented in intnum and its variants. Romberg integration is still available under the name intnumromb, but superseded. It is possible to compute numerically integrals to thousands of decimal places in reasonable time, as long as the integrand is regular. It is also reasonable to compute numerically integrals in several variables, although more than two becomes lengthy. The integration domain may be non-compact, and the integrand may have reasonable singularities at endpoints. To use intnum, you must split the integral into a sum of subintegrals where the function has no singularities except at the endpoints. Polynomials in logarithms are not considered singular, and neglecting these logs, singularities are assumed to be algebraic (asymptotic to C(x − a)^{−α} for some α > −1 when x is close to a), or to correspond to simple discontinuities of some (higher) derivative of the function. For instance, the point 0 is a singularity of abs(x).

See also the discrete summation methods below, sharing the prefix sum.

3.9.1 derivnum(X = a, expr). Numerical derivation of expr with respect to X at X = a.

    ? derivnum(x=0,sin(exp(x))) - cos(1)
    %1 = -1.262177448 E-29

A clumsier approach, which would not work in library mode, is

    ? f(x) = sin(exp(x))
    ? f'(0) - cos(1)
    %1 = -1.262177448 E-29

When a is a power series, compute derivnum(t=a,f) as f′(a) = (f(a))′/a′.

    The library syntax is derivnum(void *E, GEN (*eval)(void*,GEN), GEN a, long prec).
Also available is GEN derivfun(void *E, GEN (*eval)(void *, GEN), GEN a, long prec),

which also allows power series for a.

3.9.2 intcirc(X = a, R, expr, {tab}). Numerical integration of (2iπ)^{−1}expr with respect to X on the circle |X − a| = R. In other words, when expr is a meromorphic function, sum of the residues in the corresponding disk. tab is as in intnum, except that if computed with intnuminit it should be with the endpoints [-1, 1].

    ? \p105
    ? intcirc(s=1, 0.5, zeta(s)) - 1
    %1 = -2.398082982 E-104 - 7.94487211 E-107*I
    The library syntax is intcirc(void *E, GEN (*eval)(void*,GEN), GEN a,GEN R,GEN tab,
long prec).

3.9.3 intfouriercos(X = a, b, z, expr, {tab}). Numerical integration of expr(X) cos(2πzX) from a to b, in other words Fourier cosine transform (from a to b) of the function represented by expr. Endpoints a and b are coded as in intnum, and are not necessarily at infinity, but if they are, oscillations (i.e. [[±1], αI]) are forbidden.

    The library syntax is intfouriercos(void *E, GEN (*eval)(void*,GEN), GEN a, GEN b,
GEN z, GEN tab, long prec).

3.9.4 intfourierexp(X = a, b, z, expr, {tab}). Numerical integration of expr(X) exp(−2iπzX) from a to b, in other words Fourier transform (from a to b) of the function represented by expr. Note the minus sign. Endpoints a and b are coded as in intnum, and are not necessarily at infinity but if they are, oscillations (i.e. [[±1], αI]) are forbidden.

    The library syntax is intfourierexp(void *E, GEN (*eval)(void*,GEN), GEN a, GEN b,
GEN z, GEN tab, long prec).

3.9.5 intfouriersin(X = a, b, z, expr, {tab}). Numerical integration of expr(X) sin(2πzX) from a to b, in other words Fourier sine transform (from a to b) of the function represented by expr. Endpoints a and b are coded as in intnum, and are not necessarily at infinity but if they are, oscillations (i.e. [[±1], αI]) are forbidden.

    The library syntax is intfouriersin(void *E, GEN (*eval)(void*,GEN), GEN a, GEN b,
GEN z, GEN tab, long prec).

3.9.6 intfuncinit(X = a, b, expr, {flag = 0}, {m = 0}). Initialize tables for use with integral transforms such as intmellininv, etc., where a and b are coded as in intnum, expr is the function s(X) to which the integral transform is to be applied (which will multiply the weights of integration) and m is as in intnuminit. If flag is nonzero, assumes that s(−X) = \overline{s(X)}, which makes the computation twice as fast. See intmellininvshort for examples of the use of this function, which is particularly useful when the function s(X) is lengthy to compute, such as a gamma product.

    The library syntax is intfuncinit(void *E, GEN (*eval)(void*,GEN), GEN a,GEN b,long

m, long flag, long prec). Note that the order of m and flag are reversed compared to the GP syntax.

3.9.7 intlaplaceinv(X = sig, z, expr, {tab}). Numerical integration of (2iπ)^{−1}expr(X)e^{Xz} with respect to X on the line ℜ(X) = sig. In other words, inverse Laplace transform of the function corresponding to expr at the value z.

sig is coded as follows. Either it is a real number σ, equal to the abscissa of integration, and then the integrand is assumed to be slowly decreasing when the imaginary part of the variable tends to ±∞. Or it is a two component vector [σ, α], where σ is as before, and either α = 0 for slowly decreasing functions, or α > 0 for functions decreasing like exp(−αt). Note that it is not necessary to choose the exact value of α. tab is as in intnum.

It is often a good idea to use this function with a value of m one or two higher than the one chosen by default (which can be viewed thanks to the function intnumstep), or to increase the abscissa of integration σ. For example:

? \p 105
? intlaplaceinv(x=2, 1, 1/x) - 1
time = 350 ms.
%1 = 7.37... E-55 + 1.72... E-54*I \\ not so good
? m = intnumstep()
%2 = 7
? intlaplaceinv(x=2, 1, 1/x, m+1) - 1
time = 700 ms.
%3 = 3.95... E-97 + 4.76... E-98*I \\ better
? intlaplaceinv(x=2, 1, 1/x, m+2) - 1
time = 1400 ms.
%4 = 0.E-105 + 0.E-106*I \\ perfect but slow.
? intlaplaceinv(x=5, 1, 1/x) - 1
time = 340 ms.
%5 = -5.98... E-85 + 8.08... E-85*I \\ better than %1
? intlaplaceinv(x=5, 1, 1/x, m+1) - 1
time = 680 ms.
%6 = -1.09... E-106 + 0.E-104*I \\ perfect, fast.
? intlaplaceinv(x=10, 1, 1/x) - 1
time = 340 ms.

%7 = -4.36... E-106 + 0.E-102*I \\ perfect, fastest, but why sig = 10?

? intlaplaceinv(x=100, 1, 1/x) - 1
time = 330 ms.
%7 = 1.07... E-72 + 3.2... E-72*I \\ too far now...
The library syntax is intlaplaceinv(void *E, GEN (*eval)(void*,GEN), GEN sig,GEN z,
GEN tab, long prec).

3.9.8 intmellininv(X = sig, z, expr, {tab}). Numerical integration of (2iπ)^{−1}expr(X)z^{−X} with respect to X on the line ℜ(X) = sig, in other words, inverse Mellin transform of the function corresponding to expr at the value z.

sig is coded as follows. Either it is a real number σ, equal to the abscissa of integration, and then the integrated is assumed to decrease exponentially fast, of the order of exp(−t) when the imaginary part of the variable tends to ±∞. Or it is a two component vector [σ, α], where σ is as before, and either α = 0 for slowly decreasing functions, or α > 0 for functions decreasing like exp(−αt), such as gamma products. Note that it is not necessary to choose the exact value of α, and that α = 1 (equivalent to sig alone) is usually sufficient. tab is as in intnum.

As all similar functions, this function is provided for the convenience of the user, who could use intnum directly. However it is in general better to use intmellininvshort.

? \p 105
? intmellininv(s=2,4, gamma(s)^3);
time = 1,190 ms. \\ reasonable.
? \p 308
? intmellininv(s=2,4, gamma(s)^3);

time = 51,300 ms. \\ slow because of Γ(s)^{3}.

The library syntax is intmellininv(void *E, GEN (*eval)(void*,GEN), GEN sig, GEN z,
GEN tab, long prec).

3.9.9 intmellininvshort(sig, z, tab). Numerical integration of (2iπ)^{−1}s(X)z^{−X} with respect to X on the line ℜ(X) = sig. In other words, inverse Mellin transform of s(X) at the value z. Here s(X) is implicitly contained in tab in intfuncinit format, typically

tab = intfuncinit(T = [-1], [1], s(sig + I*T))

or similar commands. Take the example of the inverse Mellin transform of Γ(s)^{3} given in int-

mellininv:
? \p 105
? oo = [1]; \\ for clarity
? A = intmellininv(s=2,4, gamma(s)^3);

time = 2,500 ms. \\ not too fast because of Γ(s)^{3}. \\ function of real type, decreasing as exp(−3π/2 · |t|)

? tab = intfuncinit(t=[-oo, 3*Pi/2],[oo, 3*Pi/2], gamma(2+I*t)^3, 1);
time = 1,370 ms.
? intmellininvshort(2,4, tab) - A
time = 50 ms.
%4 = -1.26... - 3.25...E-109*I \\ 50 times faster than A and perfect.
? tab2 = intfuncinit(t=-oo, oo, gamma(2+I*t)^3, 1);
? intmellininvshort(2,4, tab2)
%6 = -1.2...E-42 - 3.2...E-109*I  \\ 63 digits lost

In the computation of tab, it was not essential to include the exact exponential decrease of Γ(2+it)^{3}. But as the last example shows, a rough indication must be given, otherwise slow decrease is assumed, resulting in catastrophic loss of accuracy.

The library syntax is GEN intmellininvshort(GEN sig, GEN z, GEN tab, long prec).

3.9.10 intnum(X = a, b, expr, {tab}). Numerical integration of expr on ]a, b[ with respect to X. The integrand may have values belonging to a vector space over the real numbers; in particular, it can be complex-valued or vector-valued. But it is assumed that the function is regular on ]a, b[. If the endpoints a and b are finite and the function is regular there, the situation is simple:

    ? intnum(x = 0,1, x^2)
    %1 = 0.3333333333333333333333333333
    ? intnum(x = 0,Pi/2, [cos(x), sin(x)])
    %2 = [1.000000000000000000000000000, 1.000000000000000000000000000]

An endpoint equal to ±∞ is coded as the single-component vector [±1]. You are welcome to set, e.g oo = [1] or INFINITY = [1], then using +oo, -oo, -INFINITY, etc. will have the expected behavior.

    ? oo = [1];  \\ for clarity
    ? intnum(x = 1,+oo, 1/x^2)
    %2 = 1.000000000000000000000000000

In basic usage, it is assumed that the function does not decrease exponentially fast at infinity:

    ? intnum(x=0,+oo, exp(-x))
      ***   at top-level: intnum(x=0,+oo,exp(-
      ***                 ^--------------------
      *** exp: exponent (expo) overflow

We shall see in a moment how to avoid the last problem, after describing the last argument tab, which is both optional and technical. The routine uses weights, which are mostly independent of the function being integrated, evaluated at many sampling points. If tab is

• a positive integer m, we use 2^{m} sampling points, hopefully increasing accuracy. But note that the running time is roughly proportional to 2^{m}. One may try consecutive values of m until they give the same value up to an accepted error. If tab is omitted, the algorithm guesses a reasonable value for m depending on the current precision only, which should be sufficient for regular functions. That value may be obtained from intnumstep, and increased in case of difficulties.

• a set of integration tables as output by intnuminit, they are used directly. This is useful if several integrations of the same type are performed (on the same kind of interval and functions, for a given accuracy), in particular for multivariate integrals, since we then skip expensive precomputations.

Specifying the behavior at endpoints. This is done as follows. An endpoint a is either given as such (a scalar, real or complex, or [±1] for ±∞), or as a two component vector [a, α], to indicate the behavior of the integrand in a neighborhood of a.

If a is finite, the code [a, α] means the function has a singularity of the form (x − a)^{α}, up to logarithms. (If α ≥ 0, we only assume the function is regular, which is the default assumption.) If a wrong singularity exponent is used, the result will lose a catastrophic number of decimals:

    ? intnum(x=0, 1, x^(-1/2))         \\ assume x^{−1/2} is regular at 0
    %1 = 1.999999999999999999990291881
    ? intnum(x=[0,-1/2], 1, x^(-1/2))  \\ no, it’s not
    %2 = 2.000000000000000000000000000
    ? intnum(x=[0,-1/10], 1, x^(-1/2))
    %3 = 1.999999999999999999999946438 \\ using a wrong exponent is bad

If a is ±∞, which is coded as [±1], the situation is more complicated, and [[±1], α] means:

• α = 0 (or no α at all, i.e. simply [±1]) assumes that the integrand tends to zero, but not exponentially fast, and not oscillating such as sin(x)/x.

• α > 0 assumes that the function tends to zero exponentially fast approximately as exp(−αx). This includes oscillating but quickly decreasing functions such as exp(−x) sin(x).

    ? oo = [1];
    ? intnum(x=0, +oo, exp(-2*x))
      ***   at top-level: intnum(x=0,+oo,exp(-
      ***                 ^--------------------
      *** exp: exponent (expo) overflow
    ? intnum(x=0, [+oo, 2], exp(-2*x))
    %1 = 0.5000000000000000000000000000 \\ OK!
    ? intnum(x=0, [+oo, 4], exp(-2*x))
    %2 = 0.4999999999999999999961990984 \\ wrong exponent ⇒ imprecise result
    ? intnum(x=0, [+oo, 20], exp(-2*x))
    %2 = 0.4999524997739071283804510227 \\ disaster

• α < −1 assumes that the function tends to 0 slowly, like x^{α}. Here it is essential to give the correct α, if possible, but on the other hand α ≤ −2 is equivalent to α = 0, in other words to no α at all.

The last two codes are reserved for oscillating functions. Let k > 0 real, and g(x) a nonoscillating function tending slowly to 0 (e.g. like a negative power of x), then

• α = k ∗ I assumes that the function behaves like cos(kx)g(x).

• α = −k ∗ I assumes that the function behaves like sin(kx)g(x).

Here it is critical to give the exact value of k. If the oscillating part is not a pure sine or cosine, one must expand it into a Fourier series, use the above codings, and sum the resulting contributions. Otherwise you will get nonsense. Note that cos(kx), and similarly sin(kx), means that very function, and not a translated version such as cos(kx + a).

Note. If f(x) = cos(kx)g(x) where g(x) tends to zero exponentially fast as exp(−αx), it is up to the user to choose between [[±1], α] and [[±1], k∗I], but a good rule of thumb is that if the oscillations are much weaker than the exponential decrease, choose [[±1], α], otherwise choose [[±1], k∗I], although the latter can reasonably be used in all cases, while the former cannot. To take a specific example, in the inverse Mellin transform, the integrand is almost always a product of an exponentially decreasing and an oscillating factor. If we choose the oscillating type of integral we perhaps obtain the best results, at the expense of having to recompute our functions for a different value of the variable z giving the transform, preventing us to use a function such as intmellininvshort. On the other hand using the exponential type of integral, we obtain less accurate results, but we skip expensive recomputations. See intmellininvshort and intfuncinit for more explanations.

We shall now see many examples to get a feeling for what the various parameters achieve. All examples below assume precision is set to 105 decimal digits. We first type

    ? \p 105

? oo = [1] \\ for clarity Apparent singularities. Even if the function f(x) represented by expr has no singularities, it may be important to define the function differently near special points. For instance, if f(x) = 1/(exp(x) − 1) − exp(−x)/x, then ∫_{0}^{∞} f(x) dx = γ, Euler’s constant Euler. But

? f(x) = 1/(exp(x)-1) - exp(-x)/x
? intnum(x = 0, [oo,1],  f(x)) - Euler
%1 = 6.00... E-67

thus only correct to 67 decimal digits. This is because close to 0 the function f is computed with an enormous loss of accuracy. A better solution is

? f(x) = 1/(exp(x)-1)-exp(-x)/x
? F = truncate( f(t + O(t^7)) ); \\ expansion around t = 0
? g(x) = if (x > 1e-18, f(x), subst(F,t,x))  \\ note that 6 · 18 > 105
? intnum(x = 0, [oo,1],  g(x)) - Euler
%2 = 0.E-106 \\ perfect

It is up to the user to determine constants such as the 10^{−18} and 7 used above.

True singularities. With true singularities the result is worse. For instance

? intnum(x = 0, 1,  1/sqrt(x)) - 2

%1 = -1.92... E-59 \\ only 59 correct decimals

? intnum(x = [0,-1/2], 1,  1/sqrt(x)) - 2
%2 = 0.E-105 \\ better

Oscillating functions.

? intnum(x = 0, oo, sin(x) / x) - Pi/2
%1 = 20.78.. \\ nonsense
? intnum(x = 0, [oo,1], sin(x)/x) - Pi/2
%2 = 0.004.. \\ bad
? intnum(x = 0, [oo,-I], sin(x)/x) - Pi/2
%3 = 0.E-105 \\ perfect
? intnum(x = 0, [oo,-I], sin(2*x)/x) - Pi/2  \\ oops, wrong k
%4 = 0.07...
? intnum(x = 0, [oo,-2*I], sin(2*x)/x) - Pi/2
%5 = 0.E-105 \\ perfect
? intnum(x = 0, [oo,-I], sin(x)^3/x) - Pi/4
%6 = 0.0092... \\ bad
? sin(x)^3 - (3*sin(x)-sin(3*x))/4
%7 = O(x^17)

We may use the above linearization and compute two oscillating integrals with “infinite endpoints” [oo, -I] and [oo, -3*I] respectively, or notice the obvious change of variable, and reduce to the single integral \frac{1}{2} ∫_{0}^{∞} sin(x)/x dx. We finish with some more complicated examples:

? intnum(x = 0, [oo,-I], (1-cos(x))/x^2) - Pi/2
%1 = -0.0004... \\ bad
? intnum(x = 0, 1, (1-cos(x))/x^2) \
+ intnum(x = 1, oo, 1/x^2) - intnum(x = 1, [oo,I], cos(x)/x^2) - Pi/2
%2 = -2.18... E-106 \\ OK

? intnum(x = 0, [oo, 1], sin(x)^3*exp(-x)) - 0.3
%3 = 5.45... E-107 \\ OK
? intnum(x = 0, [oo,-I], sin(x)^3*exp(-x)) - 0.3

%4 = -1.33... E-89 \\ lost 16 decimals. Try higher m:

? m = intnumstep()

%5 = 7 \\ the value of m actually used above.

? tab = intnuminit(0,[oo,-I], m+1); \\ try m one higher.
? intnum(x = 0, oo, sin(x)^3*exp(-x), tab) - 0.3
%6 = 5.45... E-107 \\ OK this time.

Warning. Like sumalt, intnum often assigns a reasonable value to diverging integrals. Use these values at your own risk! For example:

? intnum(x = 0, [oo, -I], x^2*sin(x))
%1 = -2.0000000000...

Note the formula

∫_{0}^{∞} sin(x)/x^{s} dx = cos(πs/2)Γ(1 − s) ,

a priori valid only for 0 < ℜ(s) < 2, but the right hand side provides an analytic continuation which may be evaluated at s = −2…

Multivariate integration. Using successive univariate integration with respect to different formal parameters, it is immediate to do naive multivariate integration. But it is important to use a suitable intnuminit to precompute data for the internal integrations at least!

For example, to compute the double integral on the unit disc x^{2} + y^{2} ≤ 1 of the function x^{2} + y^{2}, we can write

? tab = intnuminit(-1,1);
? intnum(x=-1,1, intnum(y=-sqrt(1-x^2),sqrt(1-x^2), x^2+y^2, tab), tab)

The first tab is essential, the second optional. Compare:

? tab = intnuminit(-1,1);
time = 30 ms.
? intnum(x=-1,1, intnum(y=-sqrt(1-x^2),sqrt(1-x^2), x^2+y^2));
time = 54,410 ms. \\ slow
? intnum(x=-1,1, intnum(y=-sqrt(1-x^2),sqrt(1-x^2), x^2+y^2, tab), tab);
time = 7,210 ms.  \\ faster

However, the intnuminit program is usually pessimistic when it comes to choosing the integration step 2^{−m}. It is often possible to improve the speed by trial and error. Continuing the above example:

? test(M) =
{
tab = intnuminit(-1,1, M);
intnum(x=-1,1, intnum(y=-sqrt(1-x^2),sqrt(1-x^2), x^2+y^2,tab), tab) - Pi/2
}

? m = intnumstep() \\ what value of m did it take?

%1 = 7
? test(m - 1)

    time = 1,790 ms.

%2 = -2.05... E-104 \\ 4 = 2^{2} times faster and still OK.

    ? test(m - 2)
    time = 430 ms.

%3 = -1.11... E-104 \\ 16 = 2^{4} times faster and still OK.

    ? test(m - 3)
    time = 120 ms.

%3 = -7.23... E-60 \\ 64 = 2^{6} times faster, lost 45 decimals.

    The library syntax is intnum(void *E, GEN (*eval)(void*,GEN), GEN a,GEN b,GEN tab,

long prec), where an omitted tab is coded as NULL.

3.9.11 intnuminit(a, b, {m = 0}). Initialize tables for integration from a to b, where a and b are coded as in intnum. Only the compactness, the possible existence of singularities, the speed of decrease or the oscillations at infinity are taken into account, and not the values. For instance

intnuminit(-1,1) is equivalent to intnuminit(0,Pi), and intnuminit([0,-1/2],[1]) is equiv-

alent to intnuminit([-1],[-1,-1/2]). If m is not given, it is computed according to the current precision. Otherwise the integration step is 1/2^{m}. Reasonable values of m are m = 6 or m = 7 for 100 decimal digits, and m = 9 for 1000 decimal digits.

The result is technical, but in some cases it is useful to know the output. Let x = ϕ(t) be the change of variable which is used. tab[1] contains the integer m as above, either given by the user or computed from the default precision, and can be recomputed directly using the function intnumstep. tab[2] and tab[3] contain respectively the abscissa and weight corresponding to t = 0 (ϕ(0) and ϕ′(0)). tab[4] and tab[5] contain the abscissas and weights corresponding to positive t = nh for 1 ≤ n ≤ N and h = 1/2^{m} (ϕ(nh) and ϕ′(nh)). Finally tab[6] and tab[7] contain either the abscissas and weights corresponding to negative t = nh for −N ≤ n ≤ −1, or may be empty (but not always) if ϕ(t) is an odd function (implicitly we would have tab[6] = −tab[4] and tab[7] = tab[5]).

    The library syntax is GEN intnuminit(GEN a, GEN b, long m, long prec).

3.9.12 intnuminitgen(t, a, b, ph, {m = 0}, {flag = 0}). Initialize tables for integrations from a to b using abscissas ph(t) and weights ph′(t). Note that there is no equal sign after the variable name t since t always goes from −∞ to +∞, but it is ph(t) which goes from a to b, and this is not checked. If flag= 1 or 2, multiply the reserved table length by 4flag , to avoid corresponding error.

    The library syntax is intnuminitgen(void *E, GEN (*eval)(void*,GEN), GEN a, GEN b,
long m, long flag, long prec)

3.9.13 intnumromb(X = a, b, expr, {flag = 0}). Numerical integration of expr (smooth in ]a, b[), with respect to X. Suitable for low accuracy; if expr is very regular (e.g. analytic in a large region) and high accuracy is desired, try intnum first.

Set flag = 0 (or omit it altogether) when a and b are not too large, the function is smooth, and can be evaluated exactly everywhere on the interval [a, b].

If flag = 1, uses a general driver routine for doing numerical integration, making no particular assumption (slow).

flag = 2 is tailored for being used when a or b are infinite. One must have ab > 0, and in fact if for example b = +∞, then it is preferable to have a as large as possible, at least a ≥ 1.

If flag = 3, the function is allowed to be undefined (but continuous) at a or b, for example the function sin(x)/x at x = 0.

The user should not require too much accuracy: 18 or 28 decimal digits is OK, but not much more. In addition, analytical cleanup of the integral must have been done: there must be no singularities in the interval or at the boundaries. In practice this can be accomplished with a simple change of variable. Furthermore, for improper integrals, where one or both of the limits of integration are plus or minus infinity, the function must decrease sufficiently rapidly at infinity. This can often be accomplished through integration by parts. Finally, the function to be integrated should not be very small (compared to the current precision) on the entire interval. This can of course be accomplished by just multiplying by an appropriate constant.

Note that infinity can be represented with essentially no loss of accuracy by an appropriate huge number. However beware of real underflow when dealing with rapidly decreasing functions. For example, in order to compute the ∫_{0}^{∞} e^{−x^{2}} dx to 28 decimal digits, then one can set infinity equal to 10 for example, and certainly not to 1e1000.

    The library syntax is intnumromb(void *E, GEN (*eval)(void*,GEN), GEN a, GEN b,

long flag, long prec), where eval(x, E) returns the value of the function at x. You may store any additional information required by eval in E, or set it to NULL.

3.9.14 intnumstep(). Give the value of m used in all the intnum and sumnum programs, hence such that the integration step is equal to 1/2^{m}.

    The library syntax is long intnumstep(long prec).

3.9.15 prod(X = a, b, expr, {x = 1}). Product of expression expr, initialized at x, the formal parameter X going from a to b. As for sum, the main purpose of the initialization parameter x is to force the type of the operations being performed. For example if it is set equal to the integer 1, operations will start being done exactly. If it is set equal to the real 1., they will be done using real numbers having the default precision. If it is set equal to the power series 1 + O(X^{k}) for a certain k, they will be done using power series of precision at most k. These are the three most common initializations.

As an extreme example, compare

    ? prod(i=1, 100, 1 - X^i);  \\ this has degree 5050 !!
    time = 128 ms.
    ? prod(i=1, 100, 1 - X^i, 1 + O(X^101))
    time = 8 ms.
    %2 = 1 - X - X^2 + X^5 + X^7 - X^12 - X^15 + X^22 + X^26 - X^35 - X^40 + \
    X^51 + X^57 - X^70 - X^77 + X^92 + X^100 + O(X^101)

Of course, in this specific case, it is faster to use eta, which is computed using Euler’s formula.

    ? prod(i=1, 1000, 1 - X^i, 1 + O(X^1001));
    time = 589 ms.
    ? \ps1000
    seriesprecision = 1000 significant terms
    ? eta(X) - %
    time = 8ms.
    %4 = O(X^1001)

The library syntax is produit(GEN a, GEN b, char *expr, GEN x).

3.9.16 prodeuler(X = a, b, expr). Product of expression expr, initialized at 1. (i.e. to a real number equal to 1 to the current realprecision), the formal parameter X ranging over the prime numbers between a and b.

    The library syntax is prodeuler(void *E, GEN (*eval)(void*,GEN), GEN a,GEN b, long
prec).

3.9.17 prodinf(X = a, expr, {flag = 0}). infinite product of expression expr, the formal parameter X starting at a. The evaluation stops when the relative error of the expression minus 1 is less than the default precision. In particular, non-convergent products result in infinite loops. The expressions must always evaluate to an element of C.

If flag = 1, do the product of the (1 + expr) instead.

    The library syntax is prodinf(void *E, GEN (*eval)(void*,GEN), GEN a, long prec)

(flag = 0), or prodinf1 with the same arguments (flag = 1).

3.9.18 solve(X = a, b, expr). Find a real root of expression expr between a and b, under the condition expr(X = a) ∗ expr(X = b) ≤ 0. (You will get an error message roots must be bracketed in solve if this does not hold.) This routine uses Brent’s method and can fail miserably if expr is not defined in the whole of [a, b] (try solve(x=1, 2, tan(x))).

    The library syntax is zbrent(void *E,GEN (*eval)(void*,GEN),GEN a,GEN b,long prec).

3.9.19 sum(X = a, b, expr, {x = 0}). Sum of expression expr, initialized at x, the formal parameter going from a to b. As for prod, the initialization parameter x may be given to force the type of the operations being performed.

As an extreme example, compare

? sum(i=1, 10^4, 1/i); \\ rational number: denominator has 4345 digits.

    time = 236 ms.
    ? sum(i=1, 5000, 1/i, 0.)
    time = 8 ms.
    %2 = 9.787606036044382264178477904

The library syntax is somme(GEN a, GEN b, char *expr, GEN x).

3.9.20 sumalt(X = a, expr, {flag = 0}). Numerical summation of the series expr, which should be an alternating series, the formal variable X starting at a. Use an algorithm of Cohen, Villegas and Zagier (Experiment. Math. 9 (2000), no. 1, 3–12).

If flag = 1, use a variant with slightly different polynomials. Sometimes faster.

The routine is heuristic and a rigorous proof assumes that the values of expr are the moments of a positive measure on [0, 1]. Divergent alternating series can sometimes be summed by this method, as well as series which are not exactly alternating (see for example Section 2.7). It should be used to try and guess the value of an infinite sum. (However, see the example at the end of Section 2.7.1.)

If the series already converges geometrically, suminf is often a better choice:

    ? \p28
    ? sumalt(i = 1, -(-1)^i / i)  - log(2)
    time = 0 ms.

%1 = -2.524354897 E-29
? suminf(i = 1, -(-1)^i / i)   \\ Had to hit ¡C-C¿
  ***   at top-level: suminf(i=1,-(-1)^i/i)
  ***                                ^------
  *** suminf: user interrupt after 10min, 20,100 ms.
? \p1000
? sumalt(i = 1, -(-1)^i / i)  - log(2)
time = 90 ms.
%2 = 4.459597722 E-1002
? sumalt(i = 0, (-1)^i / i!) - exp(-1)
time = 670 ms.
%3 = -4.03698781490633483156497361352190615794353338591897830587 E-944
? suminf(i = 0, (-1)^i / i!) - exp(-1)
time = 110 ms.
%4 = -8.39147638 E-1000   \\ faster and more accurate
The library syntax is sumalt(void *E, GEN (*eval)(void*,GEN),GEN a,long prec). Also

available is sumalt2 with the same arguments (flag = 1).

3.9.21 sumdiv(n, X, expr). Sum of expression expr over the positive divisors of n. This function is a trivial wrapper essentially equivalent to

  D = divisors(n);
  for (i = 1, #D, X = D[i]; eval(expr))

(except that X is lexically scoped to the sumdiv loop). If expr is a multiplicative function, use

sumdivmult.

3.9.22 sumdivmult(n, d, expr). Sum of multiplicative expression expr over the positive divisors d of n. Assume that expr evaluates to f(d) where f is multiplicative: f(1) = 1 and f(ab) = f(a)f(b) for coprime a and b.

3.9.23 suminf(X = a, expr). infinite sum of expression expr, the formal parameter X starting at a. The evaluation stops when the relative error of the expression is less than the default precision for 3 consecutive evaluations. The expressions must always evaluate to a complex number.

If the series converges slowly, make sure realprecision is low (even 28 digits may be too much). In this case, if the series is alternating or the terms have a constant sign, sumalt and sumpos should be used instead.

? \p28
? suminf(i = 1, -(-1)^i / i)   \\ Had to hit ¡C-C¿
  ***   at top-level: suminf(i=1,-(-1)^i/i)
  ***                                ^------
  *** suminf: user interrupt after 10min, 20,100 ms.
? sumalt(i = 1, -(-1)^i / i) - log(2)
time = 0 ms.
%1 = -2.524354897 E-29
The library syntax is suminf(void *E, GEN (*eval)(void*,GEN), GEN a, long prec).

3.9.24 sumnum(X = a, sig, expr, {tab}, {flag = 0}). Numerical summation of expr, the variable X taking integer values from ceiling of a to +∞, where expr is assumed to be a holomorphic function f(X) for ℜ(X) ≥ σ.

The parameter σ ∈ R is coded in the argument sig as follows: it is either

• a real number σ. Then the function f is assumed to decrease at least as 1/X^{2} at infinity, but not exponentially;

• a two-component vector [σ, α], where σ is as before, α < −1. The function f is assumed to decrease like X^{α}. In particular, α ≤ −2 is equivalent to no α at all.

• a two-component vector [σ, α], where σ is as before, α > 0. The function f is assumed to decrease like exp(−αX). In this case it is essential that α be exactly the rate of exponential decrease, and it is usually a good idea to increase the default value of m used for the integration step. In practice, if the function is exponentially decreasing sumnum is slower and less accurate than sumpos or suminf, so should not be used.

The function uses the intnum routines and integration on the line ℜ(s) = σ. The optional argument tab is as in intnum, except it must be initialized with sumnuminit instead of intnuminit.

When tab is not precomputed, sumnum can be slower than sumpos, when the latter is applicable. It is in general faster for slowly decreasing functions.

Finally, if flag is nonzero, we assume that the function f to be summed is of real type, i.e. satisfies \overline{f(z)} = f(\overline{z}), which speeds up the computation.

? \p 308
? a = sumpos(n=1, 1/(n^3+n+1));
time = 1,410 ms.
? tab = sumnuminit(2);

time = 1,620 ms. \\ slower but done once and for all.

? b = sumnum(n=1, 2, 1/(n^3+n+1), tab);
time = 460 ms. \\ 3 times as fast as sumpos
? a - b
%4 = -1.0... E-306 + 0.E-320*I \\ perfect.
? sumnum(n=1, 2, 1/(n^3+n+1), tab, 1) - a; \\ function of real type
time = 240 ms.

%2 = -1.0... E-306 \\ twice as fast, no imaginary part.

? c = sumnum(n=1, 2, 1/(n^2+1), tab, 1);
time = 170 ms. \\ fast
? d = sumpos(n=1, 1 / (n^2+1));
time = 2,700 ms. \\ slow.
? d - c
time = 0 ms.
%5 = 1.97... E-306 \\ perfect.

For slowly decreasing function, we must indicate singularities:

? \p 308
? a = sumnum(n=1, 2, n^(-4/3));

time = 9,930 ms. \\ slow because of the computation of n^{−4/3}.

? a - zeta(4/3)
time = 110 ms.

%1 = -2.42... E-107 \\ lost 200 decimals because of singularity at ∞

? b = sumnum(n=1, [2,-4/3], n^(-4/3), /*omitted*/, 1); \\ of real type
time = 12,210 ms.
? b - zeta(4/3)
%3 = 1.05... E-300 \\ better

Since the complex values of the function are used, beware of determination problems. For instance:

? \p 308
? tab = sumnuminit([2,-3/2]);
time = 1,870 ms.
? sumnum(n=1,[2,-3/2], 1/(n*sqrt(n)), tab,1) - zeta(3/2)
time = 690 ms.
%1 = -1.19... E-305 \\ fast and correct
? sumnum(n=1,[2,-3/2], 1/sqrt(n^3), tab,1) - zeta(3/2)
time = 730 ms.

%2 = -1.55... \\ nonsense. However

? sumnum(n=1,[2,-3/2], 1/n^(3/2), tab,1) - zeta(3/2)
time = 8,990 ms.

%3 = -1.19... E-305 \\ perfect, as 1/(n ∗ \sqrt{n}) above but much slower

For exponentially decreasing functions, sumnum is given for completeness, but one of suminf or sumpos should always be preferred. If you experiment with such functions and sumnum anyway, indicate the exact rate of decrease and increase m by 1 or 2:

? suminf(n=1, 2^(-n)) - 1
time = 10 ms.
%1 = -1.11... E-308 \\ fast and perfect
? sumpos(n=1, 2^(-n)) - 1
time = 10 ms.
%2 = -2.78... E-308 \\ also fast and perfect
? sumnum(n=1,2, 2^(-n)) - 1
%3 = -1.321115060 E320 + 0.E311*I \\ nonsense
? sumnum(n=1, [2,log(2)], 2^(-n), /*omitted*/, 1) - 1 \\ of real type
time = 5,860 ms.

%4 = -1.5... E-236 \\ slow and lost 70 decimals

? m = intnumstep()
%5 = 9
? sumnum(n=1,[2,log(2)], 2^(-n), m+1, 1) - 1
time = 11,770 ms.

%6 = -1.9... E-305 \\ now perfect, but slow.

The library syntax is sumnum(void *E, GEN (*eval)(void*,GEN), GEN a,GEN sig,GEN
tab,long flag, long prec).

3.9.25 sumnumalt(X = a, sig, expr, {tab}, {flag = 0}). Numerical summation of (−1)^{X}expr(X), the variable X taking integer values from ceiling of a to +∞, where expr is assumed to be a holomorphic function for ℜ(X) ≥ sig (or sig[1]).

Warning. This function uses the intnum routines and is orders of magnitude slower than sumalt. It is only given for completeness and should not be used in practice.

Warning 2. The expression expr must not include the (−1)^{X} coefficient. Thus sumalt(n = a, (−1)^{n}f(n)) is (approximately) equal to sumnumalt(n = a, sig, f(n)).

sig is coded as in sumnum. However for slowly decreasing functions (where sig is coded as [σ, α] with α < −1), it is not really important to indicate α. In fact, as for sumalt, the program will often give meaningful results (usually analytic continuations) even for divergent series. On the other hand the exponential decrease must be indicated. tab

is as in intnum, but if used must be initialized with sumnuminit. If flag is nonzero, assumes that the function f to be summed is of real type, i.e. satisfies \overline{f(z)} = f(\overline{z}), and then twice faster when tab is precomputed.

    ? \p 308
    ? tab = sumnuminit(2, /*omitted*/, -1); \\ abscissa σ = 2, alternating sums.

time = 1,620 ms. \\ slow, but done once and for all.

    ? a = sumnumalt(n=1, 2, 1/(n^3+n+1), tab, 1);
    time = 230 ms. \\ similar speed to sumnum
    ? b = sumalt(n=1, (-1)^n/(n^3+n+1));

time = 0 ms. \\ infinitely faster!

    ? a - b
    time = 0 ms.
    %1 = -1.66... E-308 \\ perfect
    The library syntax is sumnumalt(void *E, GEN (*eval)(void*,GEN), GEN a, GEN sig,
GEN tab, long flag, long prec).

3.9.26 sumnuminit(sig, {m = 0}, {sgn = 1}). Initialize tables for numerical summation using sumnum (with sgn = 1) or sumnumalt (with sgn = −1), sig is the abscissa of integration coded as

in sumnum, and m is as in intnuminit.
    The library syntax is GEN sumnuminit(GEN sig, long m, long sgn, long prec).

3.9.27 sumpos(X = a, expr, {flag = 0}). Numerical summation of the series expr, which must be a series of terms having the same sign, the formal variable X starting at a. The algorithm used is Van Wijngaarden’s trick for converting such a series into an alternating one, then we use sumalt. For regular functions, the function sumnum is in general much faster once the initializations have been made using sumnuminit.

The routine is heuristic and assumes that expr is more or less a decreasing function of X. In particular, the result will be completely wrong if expr is 0 too often. We do not check either that all terms have the same sign. As sumalt, this function should be used to try and guess the value of an infinite sum.

If flag = 1, use slightly different polynomials. Sometimes faster.

    The library syntax is sumpos(void *E, GEN (*eval)(void*,GEN),GEN a,long prec). Also

available is sumpos2 with the same arguments (flag = 1).

3.10 Plotting functions.

Although plotting is not even a side purpose of PARI, a number of plotting functions are provided. Moreover, a lot of people * suggested ideas or submitted patches for this section of the code. There are three types of graphic functions.

3.10.1 High-level plotting functions. (all the functions starting with ploth) in which the user has little to do but explain what type of plot he wants, and whose syntax is similar to the one used in the preceding section.

3.10.2 Low-level plotting functions. (called rectplot functions, sharing the prefix plot), where every drawing primitive (point, line, box, etc.) is specified by the user. These low-level functions work as follows. You have at your disposal 16 virtual windows which are filled independently, and can then be physically ORed on a single window at user-defined positions. These windows are numbered from 0 to 15, and must be initialized before being used by the function plotinit, which specifies the height and width of the virtual window (called a rectwindow in the sequel). At all times, a virtual cursor (initialized at [0, 0]) is associated to the window, and its current value can be obtained using the function plotcursor.

A number of primitive graphic objects (called rect objects) can then be drawn in these windows, using a default color associated to that window (which can be changed using the plotcolor function) and only the part of the object which is inside the window will be drawn, with the exception of polygons and strings which are drawn entirely. The ones sharing the prefix plotr draw relatively to the current position of the virtual cursor, the others use absolute coordinates. Those having the prefix plotrecth put in the rectwindow a large batch of rect objects corresponding to the output of the related ploth function.

Finally, the actual physical drawing is done using plotdraw. The rectwindows are preserved so that further drawings using the same windows at different positions or different windows can be done without extra work. To erase a window, use plotkill. It is not possible to partially erase a window: erase it completely, initialize it again, then fill it with the graphic objects that you want to keep.

In addition to initializing the window, you may use a scaled window to avoid unnecessary conversions. For this, use plotscale. As long as this function is not called, the scaling is simply the number of pixels, the origin being at the upper left and the y-coordinates going downwards.

Plotting functions are platform independent, but a number of graphical drivers are available for screen output: X11-windows (hence also for GUI’s based on X11 such as Openwindows and Motif), and the Qt and FLTK graphical libraries. The physical window opened by plotdraw or any of the ploth* functions is completely separated from gp (technically, a fork is done, and the non-graphical memory is immediately freed in the child process), which means you can go on working in the current gp session, without having to kill the window first. This window can be closed, enlarged or reduced using the standard window manager functions. No zooming procedure is implemented though (yet).

* Among these, special thanks go to Klaus-Peter Nischke who suggested the recursive plotting and forking/resizing stuff the graphical window, and Ilya Zakharevich who rewrote the graphic code from scratch implementing many new primitives (splines, clipping). Nils Skoruppa and Bill Allombert wrote the Qt and fltk graphic drivers respectively.

3.10.3 Functions for PostScript output. in the same way that printtex allows you to have a TEX output corresponding to printed results, the functions starting with ps allow you to have PostScript output of the plots. This will not be identical with the screen output, but sufficiently close. Note that you can use PostScript output even if you do not have the plotting routines enabled. The PostScript output is written in a file whose name is derived from the psfile default (./pari.ps if you did not tamper with it). Each time a new PostScript output is asked for, the PostScript output is appended to that file. Hence you probably want to remove this file, or change the value of psfile, in between plots. On the other hand, in this manner, as many plots as desired can be kept in a single file.

3.10.4 Library mode. None of the graphic functions are available within the PARI library, you must be under gp to use them. The reason for that is that you really should not use PARI for heavy-duty graphical work, there are better specialized alternatives around. This whole set of routines was only meant as a convenient, but simple-minded, visual aid. If you really insist on using these in your program (we warned you), the source (plot*.c) should be readable enough for you to achieve something.

3.10.5 plot(X = a, b, expr, {Ymin}, {Ymax}). Crude ASCII plot of the function represented by expression expr from a to b, with Y ranging from Ymin to Ymax. If Ymin (resp. Ymax) is not given, the minimum (resp. the maximum) of the computed values of the expression is used instead.

3.10.6 plotbox(w, x2 , y2 ). Let (x1, y1) be the current position of the virtual cursor. Draw in the rectwindow w the outline of the rectangle which is such that the points (x1, y1) and (x2, y2) are opposite corners. Only the part of the rectangle which is in w is drawn. The virtual cursor does not move.

3.10.7 plotclip(w). ‘clips’ the content of rectwindow w, i.e remove all parts of the drawing that would not be visible on the screen. Together with plotcopy this function enables you to draw on a scratchpad before committing the part you’re interested in to the final picture.

3.10.8 plotcolor(w, c). Set default color to c in rectwindow w. This is only implemented for the X-windows, fltk and Qt graphing engines. Possible values for c are given by the graphcolormap default, factory setting are

1=black, 2=blue, 3=violetred, 4=red, 5=green, 6=grey, 7=gainsborough.

but this can be considerably extended.

3.10.9 plotcopy(sourcew, destw, dx, dy, {flag = 0}). Copy the contents of rectwindow sourcew to rectwindow destw with offset (dx,dy). If flag’s bit 1 is set, dx and dy express fractions of the size of the current output device, otherwise dx and dy are in pixels. dx and dy are relative positions of northwest corners if other bits of flag vanish, otherwise of: 2: southwest, 4: southeast, 6: northeast corners

3.10.10 plotcursor(w). Give as a 2-component vector the current (scaled) position of the virtual cursor corresponding to the rectwindow w.

3.10.11 plotdraw(list, {flag = 0}). Physically draw the rectwindows given in list which must be a vector whose number of components is divisible by 3. If list = [w1, x1, y1, w2, x2, y2, …], the windows w1, w2, etc. are physically placed with their upper left corner at physical position (x1, y1), (x2, y2),… respectively, and are then drawn together. Overlapping regions will thus be drawn twice, and the windows are considered transparent. Then display the whole drawing in a special window on your screen. If flag ≠ 0, x1, y1 etc. express fractions of the size of the current output device

3.10.12 ploth(X = a, b, expr, {flags = 0}, {n = 0}). High precision plot of the function y = f(x) represented by the expression expr, x going from a to b. This opens a specific window (which is killed whenever you click on it), and returns a four-component vector giving the coordinates of the bounding box in the form [xmin, xmax, ymin, ymax].

Important note. ploth may evaluate expr thousands of times; given the relatively low resolution of plotting devices, few significant digits of the result will be meaningful. Hence you should keep the current precision to a minimum (e.g. 9) before calling this function.

n specifies the number of reference point on the graph, where a value of 0 means we use the hardwired default values (1000 for general plot, 1500 for parametric plot, and 8 for recursive plot).

If no flag is given, expr is either a scalar expression f(X), in which case the plane curve y = f(X) will be drawn, or a vector [f_{1}(X), …, f_{k}(X)], and then all the curves y = f_{i}(X) will be drawn in the same window.

The binary digits of flag mean:

• 1 = Parametric: parametric plot. Here expr must be a vector with an even number of components. Successive pairs are then understood as the parametric coordinates of a plane curve. Each of these are then drawn.

For instance:

    ploth(X=0,2*Pi,[sin(X),cos(X)], "Parametric")
    ploth(X=0,2*Pi,[sin(X),cos(X)])
    ploth(X=0,2*Pi,[X,X,sin(X),cos(X)], "Parametric")

draw successively a circle, two entwined sinusoidal curves and a circle cut by the line y = x.

• 2 = Recursive: recursive plot. If this flag is set, only one curve can be drawn at a time, i.e. expr must be either a two-component vector (for a single parametric curve, and the parametric flag has to be set), or a scalar function. The idea is to choose pairs of successive reference points, and if their middle point is not too far away from the segment joining them, draw this as a local approximation to the curve. Otherwise, add the middle point to the reference points. This is fast, and usually more precise than usual plot. Compare the results of

    ploth(X=-1,1, sin(1/X), "Recursive")
    ploth(X=-1,1, sin(1/X))

for instance. But beware that if you are extremely unlucky, or choose too few reference points, you may draw some nice polygon bearing little resemblance to the original curve. For instance you should never plot recursively an odd function in a symmetric interval around 0. Try

    ploth(x = -20, 20, sin(x), "Recursive")

to see why. Hence, it’s usually a good idea to try and plot the same curve with slightly different parameters.

The other values toggle various display options:

• 4 = no Rescale: do not rescale plot according to the computed extrema. This is used in conjunction with plotscale when graphing multiple functions on a rectwindow (as a plotrecth call):

  s = plothsizes();
  plotinit(0, s[2]-1, s[2]-1);
  plotscale(0, -1,1, -1,1);
  plotrecth(0, t=0,2*Pi, [cos(t),sin(t)], "Parametric|no_Rescale")
  plotdraw([0, -1,1]);

This way we get a proper circle instead of the distorted ellipse produced by

  ploth(t=0,2*Pi, [cos(t),sin(t)], "Parametric")

• 8 = no X axis: do not print the x-axis.

• 16 = no Y axis: do not print the y-axis.

• 32 = no Frame: do not print frame.

• 64 = no Lines: only plot reference points, do not join them.

• 128 = Points too: plot both lines and points.

• 256 = Splines: use splines to interpolate the points.

• 512 = no X ticks: plot no x-ticks.

• 1024 = no Y ticks: plot no y-ticks.

• 2048 = Same ticks: plot all ticks with the same length.

• 4096 = Complex: is a parametric plot but where each member of expr is considered a complex number encoding the two coordinates of a point. For instance:

ploth(X=0,2*Pi,exp(I*X), "Complex")
ploth(X=0,2*Pi,[(1+I)*X,exp(I*X)], "Complex")

will draw respectively a circle and a circle cut by the line y = x.

3.10.13 plothraw(listx, listy, {flag = 0}). Given listx and listy two vectors of equal length, plots (in high precision) the points whose (x, y)-coordinates are given in listx and listy. Automatic positioning and scaling is done, but with the same scaling factor on x and y. If flag is 1, join points, other non-0 flags toggle display options and should be combinations of bits 2^{k}, k ≥ 3 as in

ploth.

3.10.14 plothsizes({flag = 0}). Return data corresponding to the output window in the form of a 6-component vector: window width and height, sizes for ticks in horizontal and vertical directions (this is intended for the gnuplot interface and is currently not significant), width and height of characters.

If flag = 0, sizes of ticks and characters are in pixels, otherwise are fractions of the screen size 3.10.15 plotinit(w, {x}, {y}, {flag = 0}). Initialize the rectwindow w, destroying any rect objects you may have already drawn in w. The virtual cursor is set to (0, 0). The rectwindow size is set to width x and height y; omitting either x or y means we use the full size of the device in that direction. If flag = 0, x and y represent pixel units. Otherwise, x and y are understood as fractions of the size of the current output device (hence must be between 0 and 1) and internally converted to pixels.

The plotting device imposes an upper bound for x and y, for instance the number of pixels for screen output. These bounds are available through the plothsizes function. The following sequence initializes in a portable way (i.e independent of the output device) a window of maximal size, accessed through coordinates in the [0, 1000] × [0, 1000] range:

    s = plothsizes();
    plotinit(0, s[1]-1, s[2]-1);
    plotscale(0, 0,1000, 0,1000);

3.10.16 plotkill(w). Erase rectwindow w and free the corresponding memory. Note that if you want to use the rectwindow w again, you have to use plotinit first to specify the new size. So it’s better in this case to use plotinit directly as this throws away any previous work in the given rectwindow.

3.10.17 plotlines(w, X, Y, {flag = 0}). Draw on the rectwindow w the polygon such that the (x,y)-coordinates of the vertices are in the vectors of equal length X and Y. For simplicity, the whole polygon is drawn, not only the part of the polygon which is inside the rectwindow. If flag is non-zero, close the polygon. In any case, the virtual cursor does not move.

X and Y are allowed to be scalars (in this case, both have to). There, a single segment will be drawn, between the virtual cursor current position and the point (X, Y). And only the part thereof which actually lies within the boundary of w. Then move the virtual cursor to (X, Y), even if it is outside the window. If you want to draw a line from (x1, y1) to (x2, y2) where (x1, y1) is not necessarily the position of the virtual cursor, use plotmove(w,x1,y1) before using this function.

3.10.18 plotlinetype(w, type). Change the type of lines subsequently plotted in rectwindow w. type −2 corresponds to frames, −1 to axes, larger values may correspond to something else. w = −1 changes highlevel plotting. This is only taken into account by the gnuplot interface.

3.10.19 plotmove(w, x, y). Move the virtual cursor of the rectwindow w to position (x, y).

3.10.20 plotpoints(w, X, Y). Draw on the rectwindow w the points whose (x, y)-coordinates are in the vectors of equal length X and Y and which are inside w. The virtual cursor does not move. This is basically the same function as plothraw, but either with no scaling factor or with a scale chosen using the function plotscale.

As was the case with the plotlines function, X and Y are allowed to be (simultaneously) scalar. In this case, draw the single point (X, Y) on the rectwindow w (if it is actually inside w), and in any case move the virtual cursor to position (x, y).

3.10.21 plotpointsize(w, size). Changes the “size” of following points in rectwindow w. If w = −1, change it in all rectwindows. This only works in the gnuplot interface.

3.10.22 plotpointtype(w, type). Change the type of points subsequently plotted in rectwindow w. type = −1 corresponds to a dot, larger values may correspond to something else. w = −1 changes highlevel plotting. This is only taken into account by the gnuplot interface.

3.10.23 plotrbox(w, dx, dy). Draw in the rectwindow w the outline of the rectangle which is such that the points (x1, y1) and (x1 + dx, y1 + dy) are opposite corners, where (x1, y1) is the current position of the cursor. Only the part of the rectangle which is in w is drawn. The virtual cursor does not move.

3.10.24 plotrecth(w, X = a, b, expr, {flag = 0}, {n = 0}). Writes to rectwindow w the curve output of ploth(w, X = a, b, expr, flag, n). Returns a vector for the bounding box.

3.10.25 plotrecthraw(w, data, {flags = 0}). Plot graph(s) for data in rectwindow w. flag has the same significance here as in ploth, though recursive plot is no more significant. data

is a vector of vectors, each corresponding to a list a coordinates. If parametric plot is set, there must be an even number of vectors, each successive pair corresponding to a curve. Otherwise, the first one contains the x coordinates, and the other ones contain the y-coordinates of curves to plot.

3.10.26 plotrline(w, dx, dy). Draw in the rectwindow w the part of the segment (x1, y1) − (x1 + dx, y1 + dy) which is inside w, where (x1, y1) is the current position of the virtual cursor, and move the virtual cursor to (x1 + dx, y1 + dy) (even if it is outside the window).

3.10.27 plotrmove(w, dx, dy). Move the virtual cursor of the rectwindow w to position (x1 + dx, y1 + dy), where (x1, y1) is the initial position of the cursor (i.e. to position (dx, dy) relative to the initial cursor).

3.10.28 plotrpoint(w, dx, dy). Draw the point (x1 + dx, y1 + dy) on the rectwindow w (if it is inside w), where (x1, y1) is the current position of the cursor, and in any case move the virtual cursor to position (x1 + dx, y1 + dy).

3.10.29 plotscale(w, x1 , x2 , y1 , y2 ). Scale the local coordinates of the rectwindow w so that x goes from x1 to x2 and y goes from y1 to y2 (x2 < x1 and y2 < y1 being allowed). Initially, after the initialization of the rectwindow w using the function plotinit, the default scaling is the graphic pixel count, and in particular the y axis is oriented downwards since the origin is at the upper left. The function plotscale allows to change all these defaults and should be used whenever functions are graphed.

3.10.30 plotstring(w, x, {flags = 0}). Draw on the rectwindow w the String x (see Section 2.9), at the current position of the cursor. flag

is used for justification: bits 1 and 2 regulate horizontal alignment: left if 0, right if 2, center if 1. Bits 4 and 8 regulate vertical alignment: bottom if 0, top if 8, v-center if 4. Can insert additional small gap between point and string: horizontal if bit 16 is set, vertical if bit 32 is set (see the tutorial for an example).

3.10.31 psdraw(list, {flag = 0}). Same as plotdraw, except that the output is a PostScript program appended to the psfile, and flag!=0 scales the plot from size of the current output device to the standard PostScript plotting size 3.10.32 psploth(X = a, b, expr, {flags = 0}, {n = 0}). Same as ploth, except that the output is a PostScript program appended to the psfile.

3.10.33 psplothraw(listx, listy, {flag = 0}). Same as plothraw, except that the output is a PostScript program appended to the psfile.

3.11 Programming in GP: control statements.

A number of control statements are available in GP. They are simpler and have a syntax slightly different from their C counterparts, but are quite powerful enough to write any kind of program. Some of them are specific to GP, since they are made for number theorists. As usual, X will denote any simple variable name, and seq will always denote a sequence of expressions, including the empty sequence.

Caveat. In constructs like

        for (X = a,b, seq)

the variable X is lexically scoped to the loop, leading to possibly unexpected behavior:

        n = 5;
        for (n = 1, 10,
          if (something_nice(), break);
        );

\\ at this point n is 5 !

If the sequence seq modifies the loop index, then the loop is modified accordingly:

        ? for (n = 1, 10, n += 2; print(n))
        3
        6
        9
        12

3.11.1 break({n = 1}). Interrupts execution of current seq, and immediately exits from the n innermost enclosing loops, within the current function call (or the top level loop); the integer n must be positive. If n is greater than the number of enclosing loops, all enclosing loops are exited.

3.11.2 breakpoint(). Interrupt the program and enter the breakloop. The program continues when the breakloop is exited.

    ? f(N,x)=my(z=x^2+1);breakpoint();gcd(N,z^2+1-z);
    ? f(221,3)
      ***   at top-level: f(221,3)
      ***                 ^--------
      ***   in function f: my(z=x^2+1);breakpoint();gcd(N,z
      ***                              ^--------------------
      ***   Break loop: type <Return> to continue; 'break' to go back to GP
    break> z
    10
    break>
    %2 = 13

3.11.3 dbg down({n = 1}). (In the break loop) go down n frames. This allows to cancel a previous call to dbg up.

3.11.4 dbg err(). In the break loop, return the error data of the current error, if any. See iferr for details about error data. Compare:

    ? iferr(1/(Mod(2,12019)^(6!)-1),E,Vec(E))
    %1 = ["e_INV", "Fp_inv", Mod(119, 12019)]
    ? 1/(Mod(2,12019)^(6!)-1)
      ***   at top-level: 1/(Mod(2,12019)^(6!)-
      ***                  ^--------------------
      *** _/_: impossible inverse in Fp_inv: Mod(119, 12019).
      ***   Break loop: type 'break' to go back to GP prompt
    break> Vec(dbg_err())
    ["e_INV", "Fp_inv", Mod(119, 12019)]

3.11.5 dbg up({n = 1}). (In the break loop) go up n frames. This allows to inspect data of the parent function. To cancel a dbg_up call, use dbg_down

3.11.6 dbg x(A{, n}). Print the inner structure of A, complete if n is omitted, up to level n otherwise. This is useful for debugging. This is similar to \x but does not require A to be an history entry. In particular, it can be used in the break loop.

3.11.7 for(X = a, b, seq). Evaluates seq, where the formal variable X goes from a to b. Nothing is done if a > b. a and b must be in R.

3.11.8 forcomposite(n = a, {b}, seq). Evaluates seq, where the formal variable n ranges over the composite numbers between the non-negative real numbers a to b, including a and b if they are composite. Nothing is done if a > b.

    ? forcomposite(n = 0, 10, print(n))
    4
    6
    8
    9
    10

Omitting b means we will run through all composites ≥ a, starting an infinite loop; it is expected that the user will break out of the loop himself at some point, using break or return.

Note that the value of n cannot be modified within seq:

    ? forcomposite(n = 2, 10, n = [])
     ***   at top-level: forcomposite(n=2,10,n=[])
     ***                                      ^---
     ***   index read-only: was changed to [].

3.11.9 fordiv(n, X, seq). Evaluates seq, where the formal variable X ranges through the divisors of n (see divisors, which is used as a subroutine). It is assumed that factor can handle n, without negative exponents. Instead of n, it is possible to input a factorization matrix, i.e. the output of

factor(n).

This routine uses divisors as a subroutine, then loops over the divisors. In particular, if n is an integer, divisors are sorted by increasing size.

To avoid storing all divisors, possibly using a lot of memory, the following (much slower) routine loops over the divisors using essentially constant space:

FORDIV(N)=
{ my(P, E);
  P = factor(N); E = P[,2]; P = P[,1];
  forvec( v = vector(#E, i, [0,E[i]]),
  X = factorback(P, v)
  \\ ...
);
}
? for(i=1,10^5, FORDIV(i))
time = 3,445 ms.
? for(i=1,10^5, fordiv(i, d, ))
time = 490 ms.

3.11.10 forell(E, a, b, seq). Evaluates seq, where the formal variable E = [name, M, G] ranges through all elliptic curves of conductors from a to b. In this notation name is the curve name in Cremona’s elliptic curve database, M is the minimal model, G is a Z-basis of the free part of the Mordell-Weil group E(Q).

? forell(E, 1, 500, my([name,M,G] = E); \
    if (#G > 1, print(name)))
389a1
433a1
446d1

The elldata database must be installed and contain data for the specified conductors.

The library syntax is forell(void *data, long (*call)(void*,GEN), long a, long b).

3.11.11 forpart(X = k, seq, {a = k}, {n = k}). Evaluate seq over the partitions X = [x_{1}, … x_{n}] of the integer k, i.e. increasing sequences x_{1} ≤ x_{2} … ≤ x_{n} of sum x_{1} + … + x_{n} = k. By convention, 0 admits only the empty partition and negative numbers have no partitions. A partition is given by a t_VECSMALL, where parts are sorted in nondecreasing order:

? forpart(X=3, print(X))
Vecsmall([3])
Vecsmall([1, 2])
Vecsmall([1, 1, 1])

Optional parameters n and a are as follows:

• n = nmax (resp. n = [nmin, nmax]) restricts partitions to length less than nmax (resp. length between nmin and nmax), where the length is the number of nonzero entries.

• a = amax (resp. a = [amin, amax]) restricts the parts to integers less than amax (resp. between amin and amax).

By default, parts are positive and we remove zero entries unless amin ≤ 0, in which case X is of constant length nmax.

\\ at most 3 non-zero parts, all <= 4
? forpart(v=5,print(Vec(v)),4,3)
[1, 4]
[2, 3]
[1, 1, 3]
[1, 2, 2]
\\ between 2 and 4 parts less than 5, fill with zeros
? forpart(v=5,print(Vec(v)),[0,5],[2,4])
[0, 0, 1, 4]
[0, 0, 2, 3]
[0, 1, 1, 3]
[0, 1, 2, 2]
[1, 1, 1, 2]

The behavior is unspecified if X is modified inside the loop.

The library syntax is forpart(void *data, long (*call)(void*,GEN), long k, GEN a,
GEN n).

3.11.12 forprime(p = a, {b}, seq). Evaluates seq, where the formal variable p ranges over the prime numbers between the real numbers a to b, including a and b if they are prime. More precisely, the value of p is incremented to nextprime(p + 1), the smallest prime strictly larger than p, at the end of each iteration. Nothing is done if a > b.

? forprime(p = 4, 10, print(p))
5
7

Omitting b means we will run through all primes ≥ a, starting an infinite loop; it is expected that the user will break out of the loop himself at some point, using break or return.

Note that the value of p cannot be modified within seq:

? forprime(p = 2, 10, p = [])
 ***   at top-level: forprime(p=2,10,p=[])
 ***                                   ^---
 ***   prime index read-only: was changed to [].

3.11.13 forstep(X = a, b, s, seq). Evaluates seq, where the formal variable X goes from a to b, in increments of s. Nothing is done if s > 0 and a > b or if s < 0 and a < b. s must be in R^{∗} or a vector of steps [s_{1}, …, s_{n}]. In the latter case, the successive steps are used in the order they appear in s.

? forstep(x=5, 20, [2,4], print(x))
5
7
11
13
17
19

3.11.14 forsubgroup(H = G, {bound}, seq). Evaluates seq for each subgroup H of the abelian group G (given in SNF form or as a vector of elementary divisors).

If bound is present, and is a positive integer, restrict the output to subgroups of index less than bound. If bound is a vector containing a single positive integer B, then only subgroups of index exactly equal to B are computed

The subgroups are not ordered in any obvious way, unless G is a p-group in which case Birkhoff’s algorithm produces them by decreasing index. A subgroup is given as a matrix whose columns give its generators on the implicit generators of G. For example, the following prints all subgroups of index less than 2 in G = Z/2Zg_{1} × Z/2Zg_{2}:

? G = [2,2]; forsubgroup(H=G, 2, print(H))
[1; 1]
[1; 2]
[2; 1]
[1, 0; 1, 1]

The last one, for instance is generated by (g_{1}, g_{1} + g_{2}). This routine is intended to treat huge groups, when subgrouplist is not an option due to the sheer size of the output.

For maximal speed the subgroups have been left as produced by the algorithm. To print them in canonical form (as left divisors of G in HNF form), one can for instance use

? G = matdiagonal([2,2]); forsubgroup(H=G, 2, print(mathnf(concat(G,H))))
[2, 1; 0, 1]
[1, 0; 0, 2]
[2, 0; 0, 1]
[1, 0; 0, 1]

Note that in this last representation, the index [G : H] is given by the determinant. See galoissubcyclo and galoisfixedfield for applications to Galois theory.

The library syntax is forsubgroup(void *data, long (*call)(void*,GEN), GEN G, GEN
bound).

3.11.15 forvec(X = v, seq, {flag = 0}). Let v be an n-component vector (where n is arbitrary) of two-component vectors [a_{i}, b_{i}] for 1 ≤ i ≤ n. This routine evaluates seq, where the formal variables X[1], …, X[n] go from a_{1} to b_{1},…, from a_{n} to b_{n}, i.e. X goes from [a_{1}, …, a_{n}] to [b_{1}, …, b_{n}] with respect to the lexicographic ordering. (The formal variable with the highest index moves the fastest.) If flag = 1, generate only nondecreasing vectors X, and if flag = 2, generate only strictly increasing vectors X.

The type of X is the same as the type of v: t_VEC or t_COL.

3.11.16 if(a, {seq1 }, {seq2 }). Evaluates the expression sequence seq1 if a is non-zero, otherwise the expression seq2 . Of course, seq1 or seq2 may be empty:

if (a,seq) evaluates seq if a is not equal to zero (you don’t have to write the second comma), and does nothing otherwise,

if (a,,seq) evaluates seq if a is equal to zero, and does nothing otherwise. You could get the same result using the ! (not) operator: if (!a,seq).

The value of an if statement is the value of the branch that gets evaluated: for instance

    x = if(n % 4 == 1, y, z);

sets x to y if n is 1 modulo 4, and to z otherwise.

Successive ’else’ blocks can be abbreviated in a single compound if as follows:

    if (test1, seq1,
        test2, seq2,
        ...
        testn, seqn,
        seqdefault);

is equivalent to

    if (test1, seq1
             , if (test2, seq2
                        , ...
                          if (testn, seqn, seqdefault)...));

For instance, this allows to write traditional switch / case constructions:

    if (x == 0, do0(),
        x == 1, do1(),
        x == 2, do2(),
        dodefault());

Remark. The boolean operators && and || are evaluated according to operator precedence as explained in Section 2.4, but, contrary to other operators, the evaluation of the arguments is stopped as soon as the final truth value has been determined. For instance

    if (x != 0 && f(1/x), ...)

is a perfectly safe statement.

Remark. Functions such as break and next operate on loops, such as forxxx, while, until. The if statement is not a loop. (Obviously!)

3.11.17 iferr(seq1 , E, seq2 {, pred}). Evaluates the expression sequence seq1 . If an error occurs, set the formal parameter E set to the error data. If pred is not present or evaluates to true, catch the error and evaluate seq2 . Both pred and seq2 can reference E. The error type is given by errname(E), and other data can be accessed using the component function. The code seq2 should check whether the error is the one expected. In the negative the error can be rethrown using error(E) (and possibly caught by an higher iferr instance). The following uses iferr to implement Lenstra’s ECM factoring method

    ? ecm(N, B = 1000!, nb = 100)=
      {
        for(a = 1, nb,
          iferr(ellmul(ellinit([a,1]*Mod(1,N)), [0,1]*Mod(1,N), B),
            E, return(gcd(lift(component(E,2)),N)),
            errname(E)=="e_INV" && type(component(E,2)) == "t_INTMOD"))
      }
    ? ecm(2^101-1)
    %2 = 7432339208719

The return value of iferr itself is the value of seq2 if an error occurs, and the value of seq1 otherwise. We now describe the list of valid error types, and the associated error data E; in each case, we list in order the components of E, accessed via component(E,1), component(E,2), etc.

Internal errors, “system” errors.

• "e ARCH". A requested feature s is not available on this architecture or operating system. E has one component (t_STR): the missing feature name s.

• "e BUG". A bug in the PARI library, in function s. E has one component (t_STR): the function name s.

• "e FILE". Error while trying to open a file. E has two components, 1 (t_STR): the file type (input, output, etc.), 2 (t_STR): the file name.

• "e IMPL". A requested feature s is not implemented. E has one component, 1 (t_STR): the feature name s.

• "e PACKAGE". Missing optional package s. E has one component, 1 (t_STR): the package name s.

Syntax errors, type errors.

• "e DIM". The dimensions of arguments x and y submitted to function s does not match up. E.g., multiplying matrices of inconsistent dimension, adding vectors of different lengths,… E has three component, 1 (t_STR): the function name s, 2: the argument x, 3: the argument y.

• "e FLAG". A flag argument is out of bounds in function s. E has one component, 1 (t_STR): the function name s.

• "e NOTFUNC". Generated by the PARI evaluator; tried to use a GEN x which is not a t_CLOSURE in a function call syntax (as in f = 1; f(2);). E has one component, 1: the offending

GEN x.

• "e OP". Impossible operation between two objects than cannot be typecast to a sensible common domain for deeper reasons than a type mismatch, usually for arithmetic reasons. As in O(2) + O(3): it is valid to add two t_PADICs, provided the underlying prime is the same; so the addition is not forbidden a priori for type reasons, it only becomes so when inspecting the objects and trying to perform the operation. E has three components, 1 (t_STR): the operator name op, 2: first argument, 3: second argument.

• "e TYPE". An argument x of function s had an unexpected type. (As in factor("blah").) E has two components, 1 (t_STR): the function name s, 2: the offending argument x.

• "e TYPE2". Forbidden operation between two objects than cannot be typecast to a sensible common domain, because their types do not match up. (As in Mod(1,2) + Pi.) E has three components, 1 (t_STR): the operator name op, 2: first argument, 3: second argument.

• "e PRIORITY". Object o in function s contains variables whose priority is incompatible with the expected operation. E.g. Pol([x,1], 'y): this raises an error because it’s not possible to create a polynomial whose coefficients involve variables with higher priority than the main variable. E has four components: 1 (t_STR): the function name s, 2: the offending argument o, 3 (t_STR): an operator op describing the priority error, 4 (t_POL): the variable v describing the priority error. The argument satisfies variable(x) opvariable(v).

• "e VAR". The variables of arguments x and y submitted to function s does not match up. E.g., considering the algebraic number Mod(t,t^2+1) in nfinit(x^2+1). E has three component, 1 (t_STR): the function name s, 2 (t_POL): the argument x, 3 (t_POL): the argument y.

Overflows.

• "e COMPONENT". Trying to access an inexistent component in a vector/matrix/list in a function: the index is less than 1 or greater than the allowed length. E has four components, 1 (t_STR): the function name 2 (t_STR): an operator op (< or >), 2 (t_GEN): a numerical limit l bounding the allowed range, 3 (GEN): the index x. It satisfies x op l.

• "e DOMAIN". An argument is not in the function’s domain. E has five components, 1 (t_STR): the function name, 2 (t_STR): the mathematical name of the out-of-domain argument 3 (t_STR): an operator op describing the domain error, 4 (t_GEN): the numerical limit l describing the domain error, 5 (GEN): the out-of-domain argument x. The argument satisfies x op l, which prevents it from belonging to the function’s domain.

• "e MAXPRIME". A function using the precomputed list of prime numbers ran out of primes. E has one component, 1 (t_INT): the requested prime bound, which overflowed primelimit or 0 (bound is unknown).

• "e MEM". A call to pari_malloc or pari_realloc failed. E has no component.

• "e OVERFLOW". An object in function s becomes too large to be represented within PARI’s hardcoded limits. (As in 2^2^2^10 or exp(1e100), which overflow in lg and expo.) E has one component, 1 (t_STR): the function name s.

• "e PREC". Function s fails because input accuracy is too low. (As in floor(1e100) at default accuracy.) E has one component, 1 (t_STR): the function name s.

• "e STACK". The PARI stack overflows. E has no component.

Errors triggered intentionally.

• "e ALARM". A timeout, generated by the alarm function. E has one component (t_STR): the error message to print.

• "e USER". A user error, as triggered by error(g_{1}, …, g_{n}). E has one component, 1 (t_VEC): the vector of n arguments given to error.

Mathematical errors.

• "e CONSTPOL". An argument of function s is a constant polynomial, which does not make sense. (As in galoisinit(Pol(1)).) E has one component, 1 (t_STR): the function name s.

• "e COPRIME". Function s expected coprime arguments, and did receive x, y, which were not. E has three component, 1 (t_STR): the function name s, 2: the argument x, 3: the argument y.

• "e INV". Tried to invert a non-invertible object x in function s. E has two components, 1 (t_STR): the function name s, 2: the non-invertible x. If x = Mod(a, b) is a t_INTMOD and a is not 0 mod b, this allows to factor the modulus, as gcd(a, b) is a non-trivial divisor of b.

• "e IRREDPOL". Function s expected an irreducible polynomial, and did receive T, which was not. (As in nfinit(x^2-1).) E has two component, 1 (t_STR): the function name s, 2 (t_POL): the polynomial x.

• "e MISC". Generic uncategorized error. E has one component (t_STR): the error message to print.

• "e MODULUS". moduli x and y submitted to function s are inconsistent. As in

       nfalgtobasis(nfinit(t^3-2), Mod(t,t^2+1)

E has three component, 1 (t_STR): the function s, 2: the argument x, 3: the argument x.

• "e NEGVAL". An argument of function s is a power series with negative valuation, which does not make sense. (As in cos(1/x).) E has one component, 1 (t_STR): the function name s.

• "e PRIME". Function s expected a prime number, and did receive p, which was not. (As in idealprimedec(nf, 4).) E has two component, 1 (t_STR): the function name s, 2: the argument p.

• "e ROOTS0". An argument of function s is a zero polynomial, and we need to consider its roots. (As in polroots(0).) E has one component, 1 (t_STR): the function name s.

• "e SQRTN". Trying to compute an n-th root of x, which does not exist, in function s. (As in sqrt(Mod(-1,3)).) E has two components, 1 (t_STR): the function name s, 2: the argument x.

3.11.18 next({n = 1}). Interrupts execution of current seq, resume the next iteration of the innermost enclosing loop, within the current function call (or top level loop). If n is specified, resume at the n-th enclosing loop. If n is bigger than the number of enclosing loops, all enclosing loops are exited.

3.11.19 return({x = 0}). Returns from current subroutine, with result x. If x is omitted, return the (void) value (return no result, like print).

3.11.20 until(a, seq). Evaluates seq until a is not equal to 0 (i.e. until a is true). If a is initially not equal to 0, seq is evaluated once (more generally, the condition on a is tested after execution of the seq, not before as in while).

3.11.21 while(a, seq). While a is non-zero, evaluates the expression sequence seq. The test is made before evaluating the seq, hence in particular if a is initially equal to zero the seq will not be evaluated at all.

3.12 Programming in GP: other specific functions.

In addition to the general PARI functions, it is necessary to have some functions which will be of use specifically for gp, though a few of these can be accessed under library mode. Before we start describing these, we recall the difference between strings and keywords (see Section 2.9): the latter don’t get expanded at all, and you can type them without any enclosing quotes. The former are dynamic objects, where everything outside quotes gets immediately expanded.

3.12.1 Strprintf(fmt, {x}∗). Returns a string built from the remaining arguments according to the format fmt. The format consists of ordinary characters (not %), printed unchanged, and conversions specifications. See printf.

3.12.2 addhelp(sym, str). Changes the help message for the symbol sym. The string str is expanded on the spot and stored as the online help for sym. It is recommended to document global variables and user functions in this way, although gp will not protest if you don’t.

You can attach a help text to an alias, but it will never be shown: aliases are expanded by the ? help operator and we get the help of the symbol the alias points to. Nothing prevents you from modifying the help of built-in PARI functions. But if you do, we would like to hear why you needed it!

Without addhelp, the standard help for user functions consists of its name and definition.

    gp> f(x) = x^2;
    gp> ?f
    f =
      (x)->x^2

Once addhelp is applied to f, the function code is no longer included. It can still be consulted by typing the function name:

    gp> addhelp(f, "Square")
    gp> ?f
    Square
    gp> f
    %2 = (x)->x^2
    The library syntax is void addhelp(const char *sym, const char *str).

3.12.3 alarm({s = 0}, {code}). If code is omitted, trigger an e ALARM exception after s seconds, cancelling any previously set alarm; stop a pending alarm if s = 0 or is omitted.

Otherwise, if s is positive, the function evaluates code, aborting after s seconds. The return value is the value of code if it ran to completion before the alarm timeout, and a t_ERROR object otherwise.

      ? p = nextprime(10^25); q = nextprime(10^26); N = p*q;
      ? E = alarm(1, factor(N));
      ? type(E)
      %3 = "t_ERROR"
      ? print(E)
      %4 = error("alarm interrupt after 964 ms.")
      ? alarm(10, factor(N));   \\ enough time
      %5 =
      [ 10000000000000000000000013 1]
      [100000000000000000000000067 1]

Here is a more involved example: the function timefact(N,sec) below tries to factor N and gives up after sec seconds, returning a partial factorisation.

    \\ Time-bounded partial factorization
    default(factor_add_primes,1);
    timefact(N,sec)=
    {
      F = alarm(sec, factor(N));
      if (type(F) == "t_ERROR", factor(N, 2^24), F);
    }

We either return the factorization directly, or replace the t_ERROR result by a simple bounded factorization factor(N, 2^24). Note the factor_add_primes trick: any prime larger than 2^{24} discovered while attempting the initial factorization is stored and remembered. When the alarm rings, the subsequent bounded factorization finds it right away.

Caveat. It is not possible to set a new alarm within another alarm code: the new timer erases the parent one.

3.12.4 alias(newsym, sym). Defines the symbol newsym as an alias for the the symbol sym:

    ? alias("det", "matdet");
    ? det([1,2;3,4])
    %1 = -2

You are not restricted to ordinary functions, as in the above example: to alias (from/to) member functions, prefix them with ‘ .’; to alias operators, use their internal name, obtained by writing in lieu of the operators argument: for instance, ! and ! are the internal names of the factorial and the logical negation, respectively.

    ? alias("mod", "_.mod");
    ? alias("add", "_+_");
    ? alias("_.sin", "sin");
    ? mod(Mod(x,x^4+1))
    %2 = x^4 + 1

? add(4,6)
%3 = 10
? Pi.sin
%4 = 0.E-37

Alias expansion is performed directly by the internal GP compiler. Note that since alias is performed at compilation-time, it does not require any run-time processing, however it only affects GP code compiled after the alias command is evaluated. A slower but more flexible alternative is to use variables. Compare

? fun = sin;
? g(a,b) = intnum(t=a,b,fun(t));
? g(0, Pi)
%3 = 2.0000000000000000000000000000000000000
? fun = cos;
? g(0, Pi)
%5 = 1.8830410776607851098 E-39

with

? alias(fun, sin);
? g(a,b) = intnum(t=a,b,fun(t));
? g(0,Pi)
%2 = 2.0000000000000000000000000000000000000
? alias(fun, cos);  \\ Oops. Does not affect *previous* definition!
? g(0,Pi)
%3 = 2.0000000000000000000000000000000000000
? g(a,b) = intnum(t=a,b,fun(t)); \\ Redefine, taking new alias into account
? g(0,Pi)
%5 = 1.8830410776607851098 E-39

A sample alias file misc/gpalias is provided with the standard distribution.

The library syntax is void alias0(const char *newsym, const char *sym).

3.12.5 allocatemem({s = 0}). This special operation changes the stack size after initialization. x must be a non-negative integer. If x > 0, a new stack of at least x bytes is allocated. We may allocate more than x bytes if x is way too small, or for alignment reasons: the current formula is max(16 ∗ ⌈x/16⌉ , 500032) bytes.

If x = 0, the size of the new stack is twice the size of the old one. The old stack is discarded.

Warning. This function should be typed at the gp prompt in interactive usage, or left by itself at the start of batch files. It cannot be used meaningfully in loop-like constructs, or as part of a larger expression sequence, e.g

       allocatemem(); x = 1;   \\ This will not set x!

In fact, all loops are immediately exited, user functions terminated, and the rest of the sequence following allocatemem() is silently discarded, as well as all pending sequences of instructions. We just go on reading the next instruction sequence from the file we’re in (or from the user). In particular, we have the following possibly unexpected behavior: in

       read("file.gp"); x = 1

were file.gp contains an allocatemem statement, the x = 1 is never executed, since all pending instructions in the current sequence are discarded.

The technical reason is that this routine moves the stack, so temporary objects created during the current expression evaluation are not correct anymore. (In particular byte-compiled expressions, which are allocated on the stack.) To avoid accessing obsolete pointers to the old stack, this routine ends by a longjmp.

Remark. If the operating system cannot allocate the desired x bytes, a loop halves the allocation size until it succeeds:

    ? allocatemem(5*10^10)
     ***   Warning: not enough memory, new stack 50000000000.
     ***   Warning: not enough memory, new stack 25000000000.
     ***   Warning: not enough memory, new stack 12500000000.
     ***   Warning: new stack size = 6250000000 (5960.464 Mbytes).

3.12.6 apply(f, A). Apply the t_CLOSURE f to the entries of A. If A is a scalar, return f(A). If A is a polynomial or power series, apply f on all coefficients. If A is a vector or list, return the elements f(x) where x runs through A. If A is a matrix, return the matrix whose entries are the f(A[i, j]).

    ? apply(x->x^2, [1,2,3,4])
    %1 = [1, 4, 9, 16]
    ? apply(x->x^2, [1,2;3,4])
    %2 =
    [1 4]
    [9 16]
    ? apply(x->x^2, 4*x^2 + 3*x+ 2)
    %3 = 16*x^2 + 9*x + 4

Note that many functions already act componentwise on vectors or matrices, but they almost never act on lists; in this case, apply is a good solution:

    ? L = List([Mod(1,3), Mod(2,4)]);
    ? lift(L)
      ***   at top-level: lift(L)
      ***                 ^-------
      *** lift: incorrect type in lift.
    ? apply(lift, L);
    %2 = List([1, 2])

Remark. For v a t_VEC, t_COL, t_LIST or t_MAT, the alternative set-notations

    [g(x) | x <- v, f(x)]
    [x | x <- v, f(x)]
    [g(x) | x <- v]

are available as shortcuts for

    apply(g, select(f, Vec(v)))
    select(f, Vec(v))
    apply(g, Vec(v))

respectively:

    ? L = List([Mod(1,3), Mod(2,4)]);
    ? [ lift(x) | x<-L ]
    %2 = [1, 2]
    The library syntax is genapply(void *E, GEN (*fun)(void*,GEN), GEN a).

3.12.7 default({key}, {val}). Returns the default corresponding to keyword key. If val is present, sets the default to val first (which is subject to string expansion first). Typing default() (or \d) yields the complete default list as well as their current values. See Section 2.12 for an introduction to GP defaults, Section 3.14 for a list of available defaults, and Section 2.13 for some shortcut alternatives. Note that the shortcuts are meant for interactive use and usually display more information

than default.
    The library syntax is GEN default0(const char *key = NULL, const char *val = NULL).

3.12.8 errname(E). Returns the type of the error message E as a string.

The library syntax is GEN errname(GEN E).

3.12.9 error({str}∗). Outputs its argument list (each of them interpreted as a string), then interrupts the running gp program, returning to the input prompt. For instance

    error("n = ", n, " is not squarefree!")

3.12.10 extern(str). The string str is the name of an external command (i.e. one you would type from your UNIX shell prompt). This command is immediately run and its output fed into gp, just as if read from a file.

3.12.11 externstr(str). The string str is the name of an external command (i.e. one you would type from your UNIX shell prompt). This command is immediately run and its output is returned as a vector of GP strings, one component per output line.

3.12.12 getabstime(). Returns the time (in milliseconds) elapsed since gp startup. This provides a reentrant version of gettime:

    my (t = getabstime());
    ...
    print("Time: ", getabstime() - t);

The library syntax is long getabstime().

3.12.13 getenv(s). Return the value of the environment variable s if it is defined, otherwise return 0.

    The library syntax is GEN gp_getenv(const char *s).

3.12.14 getheap(). Returns a two-component row vector giving the number of objects on the heap and the amount of memory they occupy in long words. Useful mainly for debugging purposes.

The library syntax is GEN getheap().

3.12.15 getrand(). Returns the current value of the seed used by the pseudo-random number generator random. Useful mainly for debugging purposes, to reproduce a specific chain of computations. The returned value is technical (reproduces an internal state array), and can only be used as an argument to setrand.

The library syntax is GEN getrand().

3.12.16 getstack(). Returns the current value of top − avma, i.e. the number of bytes used up to now on the stack. Useful mainly for debugging purposes.

The library syntax is long getstack().

3.12.17 gettime(). Returns the time (in milliseconds) elapsed since either the last call to gettime, or to the beginning of the containing GP instruction (if inside gp), whichever came last.

For a reentrant version, see getabstime.

The library syntax is long gettime().

3.12.18 global(listof variables). Obsolete. Scheduled for deletion.

3.12.19 inline(x, ..., z). (Experimental) declare x, …, z as inline variables. Such variables behave like lexically scoped variable (see my()) but with unlimited scope. It is however possible to exit the scope by using uninline(). When used in a GP script, it is recommended to call uninline() before the script’s end to avoid inline variables leaking outside the script.

3.12.20 input(). Reads a string, interpreted as a GP expression, from the input file, usually standard input (i.e. the keyboard). If a sequence of expressions is given, the result is the result of the last expression of the sequence. When using this instruction, it is useful to prompt for the string by using the print1 function. Note that in the present version 2.19 of pari.el, when using gp under GNU Emacs (see Section 2.16) one must prompt for the string, with a string which ends with the same prompt as any of the previous ones (a "? " will do for instance).

3.12.21 install(name, code, {gpname}, {lib}). Loads from dynamic library lib the function name. Assigns to it the name gpname in this gp session, with prototype code (see below). If gpname is omitted, uses name. If lib is omitted, all symbols known to gp are available: this includes the whole of libpari.so and possibly others (such as libc.so).

Most importantly, install gives you access to all non-static functions defined in the PARI library. For instance, the function GEN addii(GEN x, GEN y) adds two PARI integers, and is not directly accessible under gp (it is eventually called by the + operator of course):

    ? install("addii", "GG")
    ? addii(1, 2)
    %1 = 3

It also allows to add external functions to the gp interpreter. For instance, it makes the function system obsolete:

    ? install(system, vs, sys,/*omitted*/)
    ? sys("ls gp*")
    gp.c            gp.h            gp_rl.c

This works because system is part of libc.so, which is linked to gp. It is also possible to compile a shared library yourself and provide it to gp in this way: use gp2c, or do it manually (see the

modules build variable in pari.cfg for hints).

Re-installing a function will print a warning and update the prototype code if needed. However, it will not reload a symbol from the library, even if the latter has been recompiled. Prototype. We only give a simplified description here, covering most functions, but there are many more possibilities. The full documentation is available in libpari.dvi, see

      ??prototype

• First character i, l, v : return type int / long / void. (Default: GEN)

• One letter for each mandatory argument, in the same order as they appear in the argument list: G (GEN), & (GEN*), L (long), s (char *), n (variable).

• p to supply realprecision (usually long prec in the argument list), P to supply series-

precision (usually long precdl).

We also have special constructs for optional arguments and default values:

• DG (optional GEN, NULL if omitted),

• D& (optional GEN*, NULL if omitted),

• Dn (optional variable, −1 if omitted), For instance the prototype corresponding to

      long issquareall(GEN x, GEN *n = NULL)
is lGD&.

Caution. This function may not work on all systems, especially when gp has been compiled statically. In that case, the first use of an installed function will provoke a Segmentation Fault (this should never happen with a dynamically linked executable). If you intend to use this function, please check first on some harmless example such as the one above that it works properly on your machine.

The library syntax is void gpinstall(const char *name, const char *code, const
char *gpname, const char *lib).

3.12.22 kill(sym). Restores the symbol sym to its “undefined” status, and deletes any help messages associated to sym using addhelp. Variable names remain known to the interpreter and keep their former priority: you cannot make a variable “less important” by killing it!

? z = y = 1; y
%1 = 1
? kill(y)
? y            \\ restored to ``undefined'' status
%2 = y
? variable()
%3 = [x, y, z] \\ but the variable name y is still known, with y > z !

For the same reason, killing a user function (which is an ordinary variable holding a t_CLOSURE) does not remove its name from the list of variable names.

If the symbol is associated to a variable — user functions being an important special case —, one may use the quote operator a = 'a to reset variables to their starting values. However, this will not delete a help message associated to a, and is also slightly slower than kill(a).

? x = 1; addhelp(x, "foo"); x
%1 = 1
? x = 'x; x   \\ same as 'kill', except we don't delete help.
%2 = x
? ?x
foo

On the other hand, kill is the only way to remove aliases and installed functions.

? alias(fun, sin);
? kill(fun);
? install(addii, GG);
? kill(addii);
The library syntax is void kill0(const char *sym).

3.12.23 print({str}∗). Outputs its (string) arguments in raw format, ending with a newline.

3.12.24 print1({str}∗). Outputs its (string) arguments in raw format, without ending with a newline. Note that you can still embed newlines within your strings, using the \n notation !

3.12.25 printf(fmt, {x}∗). This function is based on the C library command of the same name. It prints its arguments according to the format fmt, which specifies how subsequent arguments are converted for output. The format is a character string composed of zero or more directives:

• ordinary characters (not %), printed unchanged,

• conversions specifications (% followed by some characters) which fetch one argument from the list and prints it according to the specification.

More precisely, a conversion specification consists in a %, one or more optional flags (among #, 0, -, +, ‘ ’), an optional decimal digit string specifying a minimal field width, an optional precision in the form of a period (‘.’) followed by a decimal digit string, and the conversion specifier (among d,i, o, u, x,X, p, e,E, f, g,G, s).

The flag characters. The character % is followed by zero or more of the following flags:

• #: The value is converted to an “alternate form”. For o conversion (octal), a 0 is prefixed to the string. For x and X conversions (hexa), respectively 0x and 0X are prepended. For other conversions, the flag is ignored.

• 0: The value should be zero padded. For d, i, o, u, x, X e, E, f, F, g, and G conversions, the value is padded on the left with zeros rather than blanks. (If the 0 and - flags both appear, the 0 flag is ignored.)

• -: The value is left adjusted on the field boundary. (The default is right justification.) The value is padded on the right with blanks, rather than on the left with blanks or zeros. A - overrides a 0 if both are given.

• ` ' (a space): A blank is left before a positive number produced by a signed conversion.

• +: A sign (+ or -) is placed before a number produced by a signed conversion. A + overrides a space if both are used.

The field width. An optional decimal digit string (whose first digit is non-zero) specifying a minimum field width. If the value has fewer characters than the field width, it is padded with spaces on the left (or right, if the left-adjustment flag has been given). In no case does a small field width cause truncation of a field; if the value is wider than the field width, the field is expanded to contain the conversion result. Instead of a decimal digit string, one may write * to specify that the field width is given in the next argument.

The precision. An optional precision in the form of a period (‘.’) followed by a decimal digit string. This gives the number of digits to appear after the radix character for e, E, f, and F conversions, the maximum number of significant digits for g and G conversions, and the maximum number of characters to be printed from an s conversion. Instead of a decimal digit string, one may write * to specify that the field width is given in the next argument.

The length modifier. This is ignored under gp, but necessary for libpari programming. Description given here for completeness:

• l: argument is a long integer.

• P: argument is a GEN.

The conversion specifier. A character that specifies the type of conversion to be applied.

• d, i: A signed integer.

• o, u, x, X: An unsigned integer, converted to unsigned octal (o), decimal (u) or hexadecimal (x or X) notation. The letters abcdef are used for x conversions; the letters ABCDEF are used for X conversions.

• e, E: The (real) argument is converted in the style [ -]d.ddd e[ -]dd, where there is one digit before the decimal point, and the number of digits after it is equal to the precision; if the precision is missing, use the current realprecision for the total number of printed digits. If the precision is explicitly 0, no decimal-point character appears. An E conversion uses the letter E rather than e to introduce the exponent.

• f, F: The (real) argument is converted in the style [ -]ddd.ddd, where the number of digits after the decimal point is equal to the precision; if the precision is missing, use the current realprecision for the total number of printed digits. If the precision is explicitly 0, no decimalpoint character appears. If a decimal point appears, at least one digit appears before it.

• g, G: The (real) argument is converted in style e or f (or E or F for G conversions) [ - ]ddd.ddd, where the total number of digits printed is equal to the precision; if the precision is missing, use the current realprecision. If the precision is explicitly 0, it is treated as 1. Style e is used when the decimal exponent is < −4, to print 0., or when the integer part cannot be decided given the known significant digits, and the f format otherwise.

• c: The integer argument is converted to an unsigned char, and the resulting character is written.

• s: Convert to a character string. If a precision is given, no more than the specified number of characters are written.

• p: Print the address of the argument in hexadecimal (as if by %#x).

• %: A % is written. No argument is converted. The complete conversion specification is %%.

Examples:

? printf("floor: %d, field width 3: %3d, with sign: %+3d\n", Pi, 1, 2);
floor: 3, field width 3:   1, with sign:  +2
? printf("%.5g %.5g %.5g\n",123,123/456,123456789);
123.00 0.26974 1.2346 e8
? printf("%-2.5s:%2.5s:%2.5s\n", "P", "PARI", "PARIGP");
P :PARI:PARIG
\\ min field width and precision given by arguments
? x = 23; y=-1/x; printf("x=%+06.2f y=%+0*.*f\n", x, 6, 2, y);
x=+23.00 y=-00.04
\\ minimum fields width 5, pad left with zeroes
? for (i = 2, 5, printf("%05d\n", 10^i))
00100
01000
10000

100000 \\ don’t truncate fields whose length is larger than the minimum width

? printf("%.2f  |%06.2f|", Pi,Pi)

3.14  |  3.14|

All numerical conversions apply recursively to the entries of vectors and matrices:

? printf("%4d", [1,2,3]);
[   1,   2,   3]
? printf("%5.2f", mathilbert(3));
[ 1.00  0.50  0.33]
[ 0.50  0.33  0.25]
[ 0.33  0.25  0.20]

Technical note. Our implementation of printf deviates from the C89 and C99 standards in a few places:

• whenever a precision is missing, the current realprecision is used to determine the number of printed digits (C89: use 6 decimals after the radix character).

• in conversion style e, we do not impose that the exponent has at least two digits; we never write a + sign in the exponent; 0 is printed in a special way, always as 0.Eexp.

• in conversion style f, we switch to style e if the exponent is greater or equal to the precision.

• in conversion g and G, we do not remove trailing zeros from the fractional part of the result; nor a trailing decimal point; 0 is printed in a special way, always as 0.Eexp.

3.12.26 printsep(sep, {str}∗). Outputs its (string) arguments in raw format, ending with a newline. Successive entries are separated by sep:

? printsep(":", 1,2,3,4)
1:2:3:4

3.12.27 printsep1(sep, {str}∗). Outputs its (string) arguments in raw format, without ending with a newline. Successive entries are separated by sep:

? printsep1(":", 1,2,3,4);print("|")
1:2:3:4

3.12.28 printtex({str}∗). Outputs its (string) arguments in TEX format. This output can then be used in a TEX manuscript. The printing is done on the standard output. If you want to print it to a file you should use writetex (see there).

Another possibility is to enable the log default (see Section 2.12). You could for instance do:

default(logfile, "new.tex");
default(log, 1);
printtex(result);

3.12.29 quit({status = 0}). Exits gp and return to the system with exit status status, a small integer. A non-zero exit status normally indicates abnormal termination. (Note: the system actually sees only status mod 256, see your man pages for exit(3) or wait(2)).

3.12.30 read({filename}). Reads in the file filename (subject to string expansion). If filename is omitted, re-reads the last file that was fed into gp. The return value is the result of the last expression evaluated.

If a GP binary file is read using this command (see Section 3.12.44), the file is loaded and the last object in the file is returned.

In case the file you read in contains an allocatemem statement (to be generally avoided), you should leave read instructions by themselves, and not part of larger instruction sequences.

3.12.31 readstr({filename}). Reads in the file filename and return a vector of GP strings, each component containing one line from the file. If filename is omitted, re-reads the last file that was fed into gp.

3.12.32 readvec({filename}). Reads in the file filename (subject to string expansion). If filename is omitted, re-reads the last file that was fed into gp. The return value is a vector whose components are the evaluation of all sequences of instructions contained in the file. For instance, if file contains

    1
    2
    3

then we will get:

    ? \r a
    %1 = 1
    %2 = 2
    %3 = 3
    ? read(a)
    %4 = 3
    ? readvec(a)
    %5 = [1, 2, 3]

In general a sequence is just a single line, but as usual braces and \ may be used to enter multiline sequences.

    The library syntax is GEN gp_readvec_file(const char *filename). The underlying li-

brary function GEN gp_readvec_stream(FILE *f) is usually more flexible.

3.12.33 select(f, A, {flag = 0}). We first describe the default behavior, when flag is 0 or omitted. Given a vector or list A and a t_CLOSURE f, select returns the elements x of A such that f(x) is non-zero. In other words, f is seen as a selection function returning a boolean value.

    ? select(x->isprime(x), vector(50,i,i^2+1))
    %1 = [2, 5, 17, 37, 101, 197, 257, 401, 577, 677, 1297, 1601]
    ? select(x->(x<100), %)
    %2 = [2, 5, 17, 37]

returns the primes of the form i^{2} + 1 for some i ≤ 50, then the elements less than 100 in the preceding result. The select function also applies to a matrix A, seen as a vector of columns, i.e. it selects columns instead of entries, and returns the matrix whose columns are the selected ones.

Remark. For v a t_VEC, t_COL, t_LIST or t_MAT, the alternative set-notations

    [g(x) | x <- v, f(x)]
    [x | x <- v, f(x)]
    [g(x) | x <- v]

are available as shortcuts for

    apply(g, select(f, Vec(v)))
    select(f, Vec(v))
    apply(g, Vec(v))

respectively:

    ? [ x | x <- vector(50,i,i^2+1), isprime(x) ]
    %1 = [2, 5, 17, 37, 101, 197, 257, 401, 577, 677, 1297, 1601]

If flag = 1, this function returns instead the indices of the selected elements, and not the elements themselves (indirect selection):

    ? V = vector(50,i,i^2+1);
    ? select(x->isprime(x), V, 1)
    %2 = Vecsmall([1, 2, 4, 6, 10, 14, 16, 20, 24, 26, 36, 40])
    ? vecextract(V, %)
    %3 = [2, 5, 17, 37, 101, 197, 257, 401, 577, 677, 1297, 1601]

The following function lists the elements in (Z/NZ)^{∗}:

    ? invertibles(N) = select(x->gcd(x,N) == 1, [1..N])

Finally

    ? select(x->x, M)

selects the non-0 entries in M. If the latter is a t_MAT, we extract the matrix of non-0 columns. Note that removing entries instead of selecting them just involves replacing the selection function f with its negation:

    ? select(x->!isprime(x), vector(50,i,i^2+1))

The library syntax is genselect(void *E, long (*fun)(void*,GEN), GEN a). Also avail-

able is GEN genindexselect(void *E, long (*fun)(void*, GEN), GEN a), corresponding to

flag = 1.

3.12.34 setrand(n). Reseeds the random number generator using the seed n. No value is returned. The seed is either a technical array output by getrand, or a small positive integer, used to generate deterministically a suitable state array. For instance, running a randomized computation starting by setrand(1) twice will generate the exact same output.

The library syntax is void setrand(GEN n).

3.12.35 system(str). str is a string representing a system command. This command is executed, its output written to the standard output (this won’t get into your logfile), and control returns to the PARI system. This simply calls the C system command.

3.12.36 trap({e}, {rec}, seq). THIS FUNCTION IS OBSOLETE: use iferr, which has a nicer and much more powerful interface. For compatibility’s sake we now describe the obsolete function

trap.

This function tries to evaluate seq, trapping runtime error e, that is effectively preventing it from aborting computations in the usual way; the recovery sequence rec is executed if the error occurs and the evaluation of rec becomes the result of the command. If e is omitted, all exceptions are trapped. See Section 2.10.2 for an introduction to error recovery under gp.

? \\ trap division by 0

? inv(x) = trap (e_INV, INFINITY, 1/x)
? inv(2)
%1 = 1/2
? inv(0)
%2 = INFINITY

Note that seq is effectively evaluated up to the point that produced the error, and the recovery sequence is evaluated starting from that same context, it does not ”undo” whatever happened in the other branch (restore the evaluation context):

? x = 1; trap (, /* recover: */ x, /* try: */ x = 0; 1/x)
%1 = 0

Note. The interface is currently not adequate for trapping individual exceptions. In the current version 2.7.2, the following keywords are recognized, but the name list will be expanded and changed in the future (all library mode errors can be trapped: it’s a matter of defining the keywords to gp):

e ALARM: alarm time-out

e ARCH: not available on this architecture or operating system

e STACK: the PARI stack overflows

e INV: impossible inverse

e IMPL: not yet implemented

e OVERFLOW: all forms of arithmetic overflow, including length or exponent overflow (when a larger value is supplied than the implementation can handle).

e SYNTAX: syntax error

e MISC: miscellaneous error

e TYPE: wrong type

e USER: user error (from the error function)

The library syntax is GEN trap0(const char *e = NULL, GEN rec = NULL, GEN seq =
NULL).

3.12.37 type(x). This is useful only under gp. Returns the internal type name of the PARI object x as a string. Check out existing type names with the metacommand \t. For example type(1) will return ”t_INT”.

The library syntax is GEN type0(GEN x). The macro typ is usually simpler to use since it returns a long that can easily be matched with the symbols t_*. The name type was avoided since it is a reserved identifier for some compilers.

3.12.38 uninline(). (Experimental) Exit the scope of all current inline variables.

3.12.39 version(). Returns the current version number as a t_VEC with three integer components (major version number, minor version number and patchlevel); if your sources were obtained through our version control system, this will be followed by further more precise arguments, including e.g. a git commit hash.

This function is present in all versions of PARI following releases 2.3.4 (stable) and 2.4.3 (testing).

Unless you are working with multiple development versions, you probably only care about the 3 first numeric components. In any case, the lex function offers a clever way to check against a particular version number, since it will compare each successive vector entry, numerically or as strings, and will not mind if the vectors it compares have different lengths:

       if (lex(version(), [2,3,5]) >= 0,
         \\ code to be executed if we are running 2.3.5 or more recent.
       ,
         \\ compatibility code
       );

On a number of different machines, version() could return either of

     %1 = [2, 3, 4]    \\ released version, stable branch
     %1 = [2, 4, 3]    \\ released version, testing branch
     %1 = [2, 6, 1, 15174, ""505ab9b"] \\ development

In particular, if you are only working with released versions, the first line of the gp introductory message can be emulated by

       [M,m,p] = version();
       printf("GP/PARI CALCULATOR Version %s.%s.%s", M,m,p);

If you are working with many development versions of PARI/GP, the 4th and/or 5th components can be profitably included in the name of your logfiles, for instance.

Technical note. For development versions obtained via git, the 4th and 5th components are liable to change eventually, but we document their current meaning for completeness. The 4th component counts the number of reachable commits in the branch (analogous to svn’s revision number), and the 5th is the git commit hash. In particular, lex comparison still orders correctly development versions with respect to each others or to released versions (provided we stay within a given branch, e.g. master)!

The library syntax is GEN pari_version().

3.12.40 warning({str}∗). Outputs the message “user warning” and the argument list (each of them interpreted as a string). If colors are enabled, this warning will be in a different color, making it easy to distinguish.

    warning(n, " is very large, this might take a while.")

3.12.41 whatnow(key). If keyword key is the name of a function that was present in GP version 1.39.15 or lower, outputs the new function name and syntax, if it changed at all (387 out of 560 did).

3.12.42 write(filename, {str}∗). Writes (appends) to filename the remaining arguments, and appends a newline (same output as print).

3.12.43 write1(filename, {str}∗). Writes (appends) to filename the remaining arguments without a trailing newline (same output as print1).

3.12.44 writebin(filename, {x}). Writes (appends) to filename the object x in binary format. This format is not human readable, but contains the exact internal structure of x, and is much faster to save/load than a string expression, as would be produced by write. The binary file format includes a magic number, so that such a file can be recognized and correctly input by the regular read or \r function. If saved objects refer to (polynomial) variables that are not defined in the new session, they will be displayed in a funny way (see Section 3.12.22). Installed functions and history objects can not be saved via this function.

If x is omitted, saves all user variables from the session, together with their names. Reading such a “named object” back in a gp session will set the corresponding user variable to the saved value. E.g after

    x = 1; writebin("log")

reading log into a clean session will set x to 1. The relative variables priorities (see Section 2.5.3) of new variables set in this way remain the same (preset variables retain their former priority, but are set to the new value). In particular, reading such a session log into a clean session will restore all variables exactly as they were in the original one.

Just as a regular input file, a binary file can be compressed using gzip, provided the file name has the standard .gz extension.

In the present implementation, the binary files are architecture dependent and compatibility with future versions of gp is not guaranteed. Hence binary files should not be used for long term storage (also, they are larger and harder to compress than text files).

    The library syntax is void gpwritebin(const char *filename, GEN x = NULL).

3.12.45 writetex(filename, {str}∗). As write, in TEX format.

3.13 Parallel programming.

These function are only available if PARI was configured using Configure --mt=. . .. Two multithread interfaces are supported:

• POSIX threads

• Message passing interface (MPI)

As a rule, POSIX threads are well-suited for single systems, while MPI is used by most clusters. However the parallel GP interface does not depend on the chosen multithread interface: a properly written GP program will work identically with both.

3.13.1 parapply(f, x). Parallel evaluation of f on the elements of x. The function f must not access global variables or variables declared with local(), and must be free of side effects.

    parapply(factor,[2^256 + 1, 2^193 - 1])

factors 2^{256} + 1 and 2^{193} − 1 in parallel.

    {
      my(E = ellinit([1,3]), V = vector(12,i,randomprime(2^200)));
      parapply(p->ellcard(E,p), V)
    }

computes the order of E(F_{p}) for 12 random primes of 200 bits.

    The library syntax is GEN parapply(GEN f, GEN x).

3.13.2 pareval(x). Parallel evaluation of the elements of x, where x is a vector of closures. The closures must be of arity 0, must not access global variables or variables declared with local and must be free of side effects.

The library syntax is GEN pareval(GEN x).

3.13.3 parfor(i = a, {b}, expr1 , {j}, {expr2 }). Evaluates the sequence expr2 (dependent on i and j) for i between a and b, in random order, computed in parallel; in this sequence expr2, substitute the variable j by the value of expr1 (dependent on i). If b is omitted, the loop will not stop.

It is allowed for expr2 to exit the loop using break/next/return; however in that case, expr2 will still be evaluated for all remaining value of i less than the current one, unless a subsequent

break/next/return happens.

3.13.4 parforprime(p = a, {b}, expr1 , {j}, {expr2 }). Evaluates the sequence expr2 (dependent on p and j) for p prime between a and b, in random order, computed in parallel. Substitute for j the value of expr1 (dependent on p). If b is omitted, the loop will not stop.

It is allowed fo expr2 to exit the loop using break/next/return, however in that case, expr2 will still be evaluated for all remaining value of p less than the current one, unless a subsequent

break/next/return happens.

3.13.5 parselect(f, A, {flag = 0}). Selects elements of A according to the selection function f, done in parallel. If flagis 1, return the indices of those elements (indirect selection) The function f must not access global variables or variables declared with local(), and must be free of side effects.

    The library syntax is GEN parselect(GEN f, GEN A, long flag ).

3.13.6 parsum(i = a, b, expr, {x}). Sum of expression expr, initialized at x, the formal parameter going from a to b, evaluated in parallel in random order. The expression expr must not access global variables or variables declared with local(), and must be free of side effects.

    parsum(i=1,1000,ispseudoprime(2^prime(i)-1))

returns the numbers of prime numbers among the first 1000 Mersenne numbers.

3.13.7 parvector(N, i, expr). As vector(N,i,expr) but the evaluations of expr are done in parallel. The expression expr must not access global variables or variables declared with local(), and must be free of side effects.

    parvector(10,i,quadclassunit(2^(100+i)+1).no)

computes the class numbers in parallel.

3.14 GP defaults.

This section documents the GP defaults

3.14.1 TeXstyle. The bits of this default allow gp to use less rigid TeX formatting commands in the logfile. This default is only taken into account when log = 3. The bits of TeXstyle have the following meaning

2: insert \right / \left pairs where appropriate.

4: insert discretionary breaks in polynomials, to enhance the probability of a good line break.

The default value is 0.

3.14.2 breakloop. If true, enables the “break loop” debugging mode, see Section 2.10.3.

The default value is 1 if we are running an interactive gp session, and 0 otherwise.

3.14.3 colors. This default is only usable if gp is running within certain color-capable terminals. For instance rxvt, color xterm and modern versions of xterm under X Windows, or standard Linux/DOS text consoles. It causes gp to use a small palette of colors for its output. With xterms, the colormap used corresponds to the resources Xterm*colorn where n ranges from 0 to 15 (see the file misc/color.dft for an example). Accepted values for this default are strings "a_{1},…,a_{k}" where k ≤ 7 and each a_{i} is either

• the keyword no (use the default color, usually black on transparent background)

• an integer between 0 and 15 corresponding to the aforementioned colormap

• a triple [c_{0}, c_{1}, c_{2}] where c_{0} stands for foreground color, c_{1} for background color, and c_{2} for attributes (0 is default, 1 is bold, 4 is underline).

The output objects thus affected are respectively error messages, history numbers, prompt, input line, output, help messages, timer (that’s seven of them). If k < 7, the remaining a_{i} are assumed to be no. For instance

    default(colors, "9, 5, no, no, 4")

typesets error messages in color 9, history numbers in color 5, output in color 4, and does not affect the rest.

A set of default colors for dark (reverse video or PC console) and light backgrounds respectively is activated when colors is set to darkbg, resp. lightbg (or any proper prefix: d is recognized as an abbreviation for darkbg). A bold variant of darkbg, called boldfg, is provided if you find the former too pale.

EMACS: In the present version, this default is incompatible with PariEmacs. Changing it will just fail silently (the alternative would be to display escape sequences as is, since Emacs will refuse to interpret them). You must customize color highlighting from the PariEmacs side, see its documentation.

The default value is "" (no colors).

3.14.4 compatible. The GP function names and syntax have changed tremendously between versions 1.xx and 2.00. To help you cope with this we provide some kind of backward compatibility, depending on the value of this default:

compatible = 0: no backward compatibility. In this mode, a very handy function, to be described in Section 3.12.41, is whatnow, which tells you what has become of your favorite functions, which gp suddenly can’t seem to remember.

compatible = 1: warn when using obsolete functions, but otherwise accept them. The output uses the new conventions though, and there may be subtle incompatibilities between the behavior of former and current functions, even when they share the same name (the current function is used in such cases, of course!). We thought of this one as a transitory help for gp old-timers. Thus, to encourage switching to compatible=0, it is not possible to disable the warning.

compatible = 2: use only the old function naming scheme (as used up to version 1.39.15), but taking case into account. Thus I ( = \sqrt{−1}) is not the same as i (user variable, unbound by default), and you won’t get an error message using i as a loop index as used to be the case.

compatible = 3: try to mimic exactly the former behavior. This is not always possible when functions have changed in a fundamental way. But these differences are usually for the better (they were meant to, anyway), and will probably not be discovered by the casual user.

One adverse side effect is that any user functions and aliases that have been defined before changing compatible will get erased if this change modifies the function list, i.e. if you move between groups {0, 1} and {2, 3} (variables are unaffected). We of course strongly encourage you to try and get used to the setting compatible=0.

Note that the default new_galois_format is another compatibility setting, which is completely independent of compatible.

The default value is 0.

3.14.5 datadir. The name of directory containing the optional data files. For now, this includes

the elldata, galdata, galpol, seadata packages.

The default value is /usr/local/share/pari(the location of installed precomputed data, can be

specified via Configure --datadir=).

3.14.6 debug. Debugging level. If it is non-zero, some extra messages may be printed, according to what is going on (see \g).

The default value is 0 (no debugging messages).

3.14.7 debugfiles. File usage debugging level. If it is non-zero, gp will print information on file descriptors in use, from PARI’s point of view (see \gf).

The default value is 0 (no debugging messages).

3.14.8 debugmem. Memory debugging level. If it is non-zero, gp will regularly print information on memory usage. If it’s greater than 2, it will indicate any important garbage collecting and the function it is taking place in (see \gm).

Important Note: As it noticeably slows down the performance, the first functionality (memory usage) is disabled if you’re not running a version compiled for debugging (see Appendix A).

The default value is 0 (no debugging messages).

3.14.9 echo. This toggle is either 1 (on) or 0 (off). When echo mode is on, each command is reprinted before being executed. This can be useful when reading a file with the \r or read commands. For example, it is turned on at the beginning of the test files used to check whether gp has been built correctly (see \e).

The default value is 0 (no echo).

3.14.10 factor add primes. This toggle is either 1 (on) or 0 (off). If on, the integer factorization machinery calls addprimes on primes factor that were difficult to find (larger than 2^{2}4), so they are automatically tried first in other factorizations. If a routine is performing (or has performed) a factorization and is interrupted by an error or via Control-C, this lets you recover the prime factors already found. The downside is that a huge addprimes table unrelated to the current computations will slow down arithmetic functions relying on integer factorization; one should then empty the table using removeprimes.

The default value is 0.

3.14.11 factor proven. This toggle is either 1 (on) or 0 (off). By default, the factors output by the integer factorization machinery are only pseudo-primes, not proven primes. If this toggle is set, a primality proof is done for each factor and all results depending on integer factorization are fully proven. This flag does not affect partial factorization when it is explicitly requested. It also does not affect the private table managed by addprimes: its entries are included as is in factorizations, without being tested for primality.

The default value is 0.

3.14.12 format. Of the form x.n, where x (conversion style) is a letter in {e, f, g}, and n (precision) is an integer; this affects the way real numbers are printed:

• If the conversion style is e, real numbers are printed in scientific format, always with an explicit exponent, e.g. 3.3 E-5.

• In style f, real numbers are generally printed in fixed floating point format without exponent, e.g. 0.000033. A large real number, whose integer part is not well defined (not enough significant digits), is printed in style e. For instance 10.^100 known to ten significant digits is always printed in style e.

• In style g, non-zero real numbers are printed in f format, except when their decimal exponent is < −4, in which case they are printed in e format. Real zeroes (of arbitrary exponent) are printed in e format.

The precision n is the number of significant digits printed for real numbers, except if n < 0 where all the significant digits will be printed (initial default 28, or 38 for 64-bit machines). For more powerful formatting possibilities, see printf and Strprintf.

The default value is "g.28" and "g.38" on 32-bit and 64-bit machines, respectively.

3.14.13 graphcolormap. A vector of colors, to be used by hi-res graphing routines. Its length is arbitrary, but it must contain at least 3 entries: the first 3 colors are used for background, frame/ticks and axes respectively. All colors in the colormap may be freely used in plotcolor calls.

A color is either given as in the default by character strings or by an RGB code. For valid character strings, see the standard rgb.txt file in X11 distributions, where we restrict to lowercase letters and remove all whitespace from color names. An RGB code is a vector with 3 integer entries between 0 and 255. For instance [250, 235, 215] and "antiquewhite" represent the same color. RGB codes are cryptic but often easier to generate.

    The default value is ["white", "black", "blue", "violetred", "red", "green", "grey",
"gainsboro"].

3.14.14 graphcolors. Entries in the graphcolormap that will be used to plot multi-curves. The successive curves are drawn in colors

    graphcolormap[graphcolors[1]], graphcolormap[graphcolors[2]], . . .

cycling when the graphcolors list is exhausted.

The default value is [4,5].

3.14.15 help. Name of the external help program to use from within gp when extended help is invoked, usually through a ?? or ??? request (see Section 2.13.1), or M-H under readline (see Section 2.15).

The default value is the path to the gphelp script we install.

3.14.16 histfile. Name of a file where gp will keep a history of all input commands (results are omitted). If this file exists when the value of histfile changes, it is read in and becomes part of the session history. Thus, setting this default in your gprc saves your readline history between sessions. Setting this default to the empty string "" changes it to <undefined>

The default value is <undefined> (no history file).

3.14.17 histsize. gp keeps a history of the last histsize results computed so far, which you can recover using the % notation (see Section 2.13.4). When this number is exceeded, the oldest values are erased. Tampering with this default is the only way to get rid of the ones you do not need anymore.

The default value is 5000.

3.14.18 lines. If set to a positive value, gp prints at most that many lines from each result, terminating the last line shown with [+++] if further material has been suppressed. The various print commands (see Section 3.12) are unaffected, so you can always type print(%) or \a to view the full result. If the actual screen width cannot be determined, a “line” is assumed to be 80 characters long.

The default value is 0.

3.14.19 linewrap. If set to a positive value, gp wraps every single line after printing that many characters.

The default value is 0 (unset).

3.14.20 log. This can be either 0 (off) or 1, 2, 3 (on, see below for the various modes). When logging mode is turned on, gp opens a log file, whose exact name is determined by the logfile default. Subsequently, all the commands and results will be written to that file (see \l). In case a file with this precise name already existed, it will not be erased: your data will be appended at the end.

The specific positive values of log have the following meaning

1: plain logfile

2: emit color codes to the logfile (if colors is set).

3: write LaTeX output to the logfile (can be further customized using TeXstyle).

The default value is 0.

3.14.21 logfile. Name of the log file to be used when the log toggle is on. Environment and time expansion are performed.

The default value is "pari.log".

3.14.22 nbthreads. Number of threads to use for parallel computing. The exact meaning an default depend on the mt engine used:

• single: not used (always one thread).

• pthread: number of threads (unlimited, default: number of core)

• mpi: number of MPI process to use (limited to the number allocated by mpirun, default: use all allocated process).

3.14.23 new galois format. This toggle is either 1 (on) or 0 (off). If on, the polgalois command will use a different, more consistent, naming scheme for Galois groups. This default is provided to ensure that scripts can control this behavior and do not break unexpectedly.

The default value is 0. This value will change to 1 (set) in the next major version.

3.14.24 output. There are three possible values: 0 (= raw), 1 (= prettymatrix), or 3 (= external prettyprint). This means that, independently of the default format for reals which we explained above, you can print results in three ways:

• raw format, i.e. a format which is equivalent to what you input, including explicit multiplication signs, and everything typed on a line instead of two dimensional boxes. This can have several advantages, for instance it allows you to pick the result with a mouse or an editor, and to paste it somewhere else.

• prettymatrix format: this is identical to raw format, except that matrices are printed as boxes instead of horizontally. This is prettier, but takes more space and cannot be used for input. Column vectors are still printed horizontally.

• external prettyprint: pipes all gp output in TeX format to an external prettyprinter, according to the value of prettyprinter. The default script (tex2mail) converts its input to readable twodimensional text.

Independently of the setting of this default, an object can be printed in any of the three formats at any time using the commands \a and \m and \B respectively.

The default value is 1 (prettymatrix).

3.14.25 parisize. gp, and in fact any program using the PARI library, needs a stack in which to do its computations. parisize is the stack size, in bytes. It is strongly recommended you increase this default (using the -s command-line switch, or a gprc) if you can afford it. Don’t increase it beyond the actual amount of RAM installed on your computer or gp will spend most of its time paging.

In case of emergency, you can use the allocatemem function to increase parisize, once the session is started.

The default value is 4M, resp. 8M on a 32-bit, resp. 64-bit machine.

3.14.26 path. This is a list of directories, separated by colons ’:’ (semicolons ’;’ in the DOS world, since colons are preempted for drive names). When asked to read a file whose name is not given by an absolute path (does not start with /, ./ or ../), gp will look for it in these directories, in the order they were written in path. Here, as usual, . means the current directory, and .. its immediate parent. Environment expansion is performed.

The default value is ".:~:~/gp" on UNIX systems, ".;C:\;C:\GP" on DOS, OS/2 and Windows, and "." otherwise.

3.14.27 prettyprinter. The name of an external prettyprinter to use when output is 3 (alternate prettyprinter). Note that the default tex2mail looks much nicer than the built-in “beautified format” (output = 2).

    The default value is "tex2mail -TeX -noindent -ragged -by par".

3.14.28 primelimit. gp precomputes a list of all primes less than primelimit at initialization time, and can build fast sieves on demand to quickly iterate over primes up to the square of primelimit. These are used by many arithmetic functions, usually for trial division purposes. The maximal value is 2^{32} − 2049 (resp 2^{64} − 2049) on a 32-bit (resp. 64-bit) machine, but values beyond 10^{8}, allowing to iterate over primes up to 10^{16}, do not seem useful.

Since almost all arithmetic functions eventually require some table of prime numbers, PARI guarantees that the first 6547 primes, up to and including 65557, are precomputed, even if prime-

limit is 1.

This default is only used on startup: changing it will not recompute a new table.

Deprecated feature. primelimit was used in some situations by algebraic number theory functions using the nf_PARTIALFACT flag (nfbasis, nfdisc, nfinit, …): this assumes that all primes p > primelimit have a certain property (the equation order is p-maximal). This is never done by default, and must be explicitly set by the user of such functions. Nevertheless, these functions now provide a more flexible interface, and their use of the global default primelimit is deprecated.

Deprecated feature. factor(N, 0) was used to partially factor integers by removing all prime factors ≤ primelimit. Don’t use this, supply an explicit bound: factor(N, bound), which avoids relying on an unpredictable global variable.

The default value is 500k.

3.14.29 prompt. A string that will be printed as prompt. Note that most usual escape sequences are available there: \e for Esc, \n for Newline, …, \\ for \. Time expansion is performed.

This string is sent through the library function strftime (on a Unix system, you can try man strftime at your shell prompt). This means that % constructs have a special meaning, usually related to the time and date. For instance, %H = hour (24-hour clock) and %M = minute [00,59] (use %% to get a real %).

If you use readline, escape sequences in your prompt will result in display bugs. If you have a relatively recent readline (see the comment at the end of Section 3.14.3), you can brace them with special sequences (\[ and \]), and you will be safe. If these just result in extra spaces in your prompt, then you’ll have to get a more recent readline. See the file misc/gprc.dft for an example.

EMACS: Caution: PariEmacs needs to know about the prompt pattern to separate your input from previous gp results, without ambiguity. It is not a trivial problem to adapt automatically this regular expression to an arbitrary prompt (which can be self-modifying!). See PariEmacs’s documentation.

The default value is "? ".

3.14.30 prompt cont. A string that will be printed to prompt for continuation lines (e.g. in between braces, or after a line-terminating backslash). Everything that applies to prompt applies

to prompt cont as well.

The default value is "".

3.14.31 psfile. Name of the default file where gp is to dump its PostScript drawings (these are appended, so that no previous data are lost). Environment and time expansion are performed.

The default value is "pari.ps".

3.14.32 readline. Switches readline line-editing facilities on and off. This may be useful if you are running gp in a Sun cmdtool, which interacts badly with readline. Of course, until readline is switched on again, advanced editing features like automatic completion and editing history are not available.

The default value is 1.

3.14.33 realprecision. The number of significant digits used to convert exact inputs given to transcendental functions (see Section 3.3), or to create absolute floating point constants (input as 1.0 or Pi for instance). Unless you tamper with the format default, this is also the number of significant digits used to print a t_REAL number; format will override this latter behaviour, and allow you to have a large internal precision while outputting few digits for instance.

Note that PARI’s internal precision works on a word basis (by increments of 32 or 64 bits), hence may be a little larger than the number of decimal digits you expected. For instance to get 2 decimal digits you need one word of precision which, on a 64-bit machine, actually gives you 19 digits (19 < log_{10}(2^{64}) < 20). The value returned when typing default(realprecision) is the internal number of significant digits, not the number of printed digits:

    ? default(realprecision, 2)
          realprecision = 19 significant digits (2 digits displayed)
    ? default(realprecision)
    %1 = 19

The default value is 38, resp. 28, on a 64-bit, resp .32-bit, machine.

3.14.34 recover. This toggle is either 1 (on) or 0 (off). If you change this to 0, any error becomes fatal and causes the gp interpreter to exit immediately. Can be useful in batch job scripts.

The default value is 1.

3.14.35 secure. This toggle is either 1 (on) or 0 (off). If on, the system and extern command are disabled. These two commands are potentially dangerous when you execute foreign scripts since they let gp execute arbitrary UNIX commands. gp will ask for confirmation before letting you (or a script) unset this toggle.

The default value is 0.

3.14.36 seriesprecision. Number of significant terms when converting a polynomial or rational function to a power series (see \ps).

The default value is 16.

3.14.37 simplify. This toggle is either 1 (on) or 0 (off). When the PARI library computes something, the type of the result is not always the simplest possible. The only type conversions which the PARI library does automatically are rational numbers to integers (when they are of type t_FRAC and equal to integers), and similarly rational functions to polynomials (when they are of type t_RFRAC and equal to polynomials). This feature is useful in many cases, and saves time, but can be annoying at times. Hence you can disable this and, whenever you feel like it, use the function simplify (see Chapter 3) which allows you to simplify objects to the simplest possible types recursively (see \y).

The default value is 1.

3.14.38 sopath. This is a list of directories, separated by colons ’:’ (semicolons ’;’ in the DOS world, since colons are preempted for drive names). When asked to install an external symbol from a shared library whose name is not given by an absolute path (does not start with /, ./ or ../), gp will look for it in these directories, in the order they were written in sopath. Here, as usual, . means the current directory, and .. its immediate parent. Environment expansion is performed.

The default value is "", corresponding to an empty list of directories: install will use the library name as input (and look in the current directory if the name is not an absolute path).

3.14.39 strictargs. This toggle is either 1 (on) or 0 (off). If on, all arguments to new user functions are mandatory unless the function supplies an explicit default value. Otherwise arguments have the default value 0.

In this example,

      fun(a,b=2)=a+b

a is mandatory, while b is optional. If strictargs is on:

    ? fun()
     ***   at top-level: fun()
     ***                 ^-----
     ***   in function fun: a,b=2
     ***                    ^-----
     ***   missing mandatory argument 'a' in user function.

This applies to functions defined while strictargs is on. Changing strictargs does not affect the behavior of previously defined functions.

The default value is 0.

3.14.40 strictmatch. This toggle is either 1 (on) or 0 (off). If on, unused characters after a sequence has been processed will produce an error. Otherwise just a warning is printed. This can be useful when you are unsure how many parentheses you have to close after complicated nested loops. Please do not use this; find a decent text-editor instead.

The default value is 1.

3.14.41 threadsize. In parallel mode, each thread needs its own private stack in which to do its computations, see parisize. This value determines the size in bytes of the stacks of each thread, so the total memory allocated will be parisize + nbthreads × threadsize.

If set to 0, the value used is the same as parisize.

The default value is 0.

3.14.42 timer. This toggle is either 1 (on) or 0 (off). Every instruction sequence in the gp calculator (anything ended by a newline in your input) is timed, to some accuracy depending on the hardware and operating system. When timer is on, each such timing is printed immediately before the output as follows:

    ? factor(2^2^7+1)
    time = 108 ms.     \\ this line omitted if 'timer' is 0
    %1 =
    [     59649589127497217 1]
    [5704689200685129054721 1]

(See also # and ##.)

The time measured is the user CPU time, not including the time for printing the results. If the time is negligible (< 1 ms.), nothing is printed: in particular, no timing should be printed when defining a user function or an alias, or installing a symbol from the library.

The default value is 0 (off).

314

Appendix A: Installation Guide for the UNIX Versions

1. Required tools.

Compiling PARI requires an ANSI C or a C++ compiler. If you do not have one, we suggest that you obtain the gcc/g++ compiler. As for all GNU software mentioned afterwards, you can find the most convenient site to fetch gcc at the address

    http://www.gnu.org/order/ftp.html

(On Mac OS X, this is also provided in the Xcode tool suite; or the lightweight “Command-line tools for Xcode”.) You can certainly compile PARI with a different compiler, but the PARI kernel takes advantage of optimizations provided by gcc. This results in at least 20% speedup on most architectures.

Optional libraries and programs. The following programs and libraries are useful in conjunction with gp, but not mandatory. In any case, get them before proceeding if you want the functionalities they provide. All of them are free. The download page on our website http://pari.math.ubordeaux.fr/download.html contains pointers on how to get these.

• GNU MP library. This provides an alternative multiprecision kernel, which is faster than PARI’s native one, but unfortunately binary incompatible, so the resulting PARI library SONAME is libpari-gmp.

• GNU readline library. This provides line editing under gp, an automatic context-dependent completion, and an editable history of commands.

• GNU emacs and the PariEmacs package. The gp calculator can be run in an Emacs buffer, with all the obvious advantages if you are familiar with this editor. Note that readline is still useful in this case since it provides a better automatic completion than is provided by Emacs’s GP-mode.

• GNU gzip/gunzip/gzcat package enables gp to read compressed data.

• perl provides extended online help (full text from the manual) about functions and concepts. The script handling this online help can be used under gp or independently.

2. Compiling the library and the gp calculator.

2.1. Basic configuration. Type

    ./Configure

in the toplevel directory. This attempts to configure PARI/GP without outside help. Note that if you want to install the end product in some nonstandard place, you can use the --prefix option, as in

    ./Configure --prefix=/an/exotic/directory

(the default prefix is /usr/local). For example, to build a package for a Linux distribution, you may want to use

    ./Configure --prefix=/usr

This phase extracts some files and creates a build directory, names Oosname-arch, where the object files and executables will be built. The osname and arch components depends on your architecture and operating system, thus you can build PARI/GP for several different machines from the same source tree (the builds are independent and can be done simultaneously).

Decide whether you agree with what Configure printed on your screen, in particular the architecture, compiler and optimization flags. Look for messages prepended by ###, which report genuine problems. Look especially for the gmp, readline and X11 libraries, and the perl and gunzip (or zcat) binaries. If anything should have been found and was not, consider that Configure failed and follow the instructions in section 3.

The Configure run creates a file config.log in the build directory, which contains debugging information — in particular, all messages from compilers — that may help diagnose problems. This file is erased and recreated from scratch each time Configure is run.

2.2. Advanced configuration. Configure accepts many other flags, and you may use any number of them to build quite a complicated configuration command. See Configure --help for a complete list. In particular, there are sets of flags related to GNU MP (--with-gmp*) and GNU readline library (--with-readline*).

Here, we focus on the non-obvious ones:

--tune: fine tunes the library for the host used for compilation. This adjusts thresholds by running a large number of comparative tests and creates a file tune.h in the build directory, that will be used from now on, overriding the ones in src/kernel/none/ and src/kernel/gmp/. It will take a while: about 30 minutes. Expect a small performance boost, perhaps a 10% speed increase compared to default settings.

If you are using GMP, tune it first, then PARI. Make sure you tune PARI on the machine that will actually run your computations. Do not use a heavily loaded machine for tunings.

You may speed up the compilation by using a parallel make:

      env MAKE="make -j4" Configure --tune

--graphic=lib: enables a particular graphic library. The default is X11 on most platforms, but PARI can use Qt, fltk, ps, or win32 (GDI).

--time=function: chooses a timing function. The default usually works fine, however you can use a different one that better fits your needs. PARI can use getrusage, clock gettime, times or ftime as timing functions. (Not all timing functions are available on all platforms.) The three first functions give timings in terms of CPU usage of the current task, approximating the complexity of the algorithm. The last one, ftime, gives timings in terms of absolute (wall-clock) time. Moreover, the clock gettime function is more precise, but much slower (at the time of this writing), than

getrusage or times.

The remaining options are specific to parallel programming. We provide an Introduction to parallel GP programming in the file doc/parallel.dvi, and to multi-threaded libpari programs in Appendix D. Beware that these options change the library ABI:

--mt=engine: specify the engine used for parallel computations. Supported value are

• single: (default) no parallellism.

• pthread: use POSIX threads. This is well-suited for multi-core systems. Setting this option also set --enable-tls, see below. This option requires the pthread library. For benchmarking, it is often useful to set --time=ftime so that GP report wall-clock instead of the sum of the time spent by each thread.

• mpi: use the MPI interface to parallelism. This allows to take advantage of clusters using MPI. This option requires a MPI library. It is usually necessary to set the environment variable CC

to mpicc.

--enable-tls: build the thread-safe version of the library. Implied by --mt=pthread. This tends to slow down the shared library libpari.so by about 15%, so you probably want to use the static library libpari.a instead.

2.3. Compilation. To compile the gp binary and build the documentation, type

    make all

To only compile the gp binary, type

    make gp

in the toplevel directory. If your make program supports parallel make, you can speed up the process by going to the build directory that Configure created and doing a parallel make here, for instance make -j4 with GNU make. It should even work from the toplevel directory.

2.4. Basic tests.

To test the binary, type make bench. This runs a quick series of tests, for a few seconds on modern machines.

In many cases, this will also build a different binary (named gp-sta or gp-dyn) linked in a slightly different way and run the tests with both. (In exotic configurations, one may pass all the tests while the other fails and we want to check for this.) To test only the default binary, use make dobench which starts the bench immediately.

If a [BUG] message shows up, something went wrong. The testing utility directs you to files containing the differences between the test output and the expected results. Have a look and decide for yourself if something is amiss. If it looks like a bug in the Pari system, we would appreciate a report, see the last section.

2.5. Cross-compiling.

When cross-compiling, you can set the environment variable RUNTEST to a program that is able to run the target binaries, e.g. an emulator. It will be used for both the Configure tests and make

bench.

3. Troubleshooting and fine tuning.

In case the default Configure run fails miserably, try

    ./Configure -a

(interactive mode) and answer all the questions: there are about 30 of them, and default answers are provided. If you accept all default answers, Configure will fail just the same, so be wary. In any case, we would appreciate a bug report (see the last section).

3.1. Installation directories. The precise default destinations are as follows: the gp binary, the scripts gphelp and tex2mail go to $prefix/bin. The pari library goes to $prefix/lib and include files to $prefix/include/pari. Other system-dependent data go to $prefix/lib/pari.

Architecture independent files go to various subdirectories of $share prefix, which defaults to $prefix/share, and can be specified via the --share-prefix argument. Man pages go into $share prefix/man, and other system-independent data under $share prefix/pari: documentation, sample GP scripts and C code, extra packages like elldata or galdata. You can also set directly --bindir (executables), --libdir (library), --includedir (include files), --mandir (manual pages), --datadir (other architecture-independent data), and finally --sysdatadir (other architecture-dependent data).

3.2. Environment variables. Configure lets the following environment variable override the defaults if set:

CC: C compiler.

DLLD: Dynamic library linker.

LD: Static linker. For instance, Configure may avoid /bin/cc on some architectures due to various problems which may have been fixed in your version of the compiler. You can try

    env CC=cc Configure

and compare the benches. Also, if you insist on using a C++ compiler and run into trouble with a

fussy g++, try to use g++ -fpermissive.

The contents of the following variables are appended to the values computed by Configure: CFLAGS: Flags for CC. CPPFLAGS: Flags for CC (preprocessor). LDFLAGS: Flags for LD.

The contents of the following variables are prepended to the values computed by Configure:

C INCLUDE PATH is prepended to the list of directories searched for include files. Note that adding -I flags to CFLAGS is not enough since Configure sometimes relies on finding the include files and parsing them, and it does not parse CFLAGS at this time.

LIBRARY PATH is prepended to the list of directories searched for libraries.

You may disable inlining by adding -DDISABLE INLINE to CFLAGS, and prevent the use of the

volatile keyword with -DDISABLE VOLATILE.

3.3. Debugging/profiling.: If you also want to debug the PARI library,

    Configure -g

creates a directory Oxxx.dbg containing a special Makefile ensuring that the gp and PARI library built there is suitable for debugging. If you want to profile gp or the library, using gprof for instance,

    Configure -pg

will create an Oxxx.prf directory where a suitable version of PARI can be built.

The gp binary built above with make all or make gp is optimized. If you have run Configure -g or -pg and want to build a special purpose binary, you can cd to the .dbg or .prf directory and type make gp there. You can also invoke make gp.dbg or make gp.prf directly from the toplevel.

3.4. Multiprecision kernel. The kernel can be specified via the

--kernel=fully qualified kernel name

switch. The PARI kernel consists of two levels: Level 0 (operation on words) and Level 1 (operation on multi-precision integers and reals), which can take the following values.

Level 0: auto (as detected), none (portable C) or one of the assembler micro-kernels

      alpha
      hppa hppa64
      ia64
      ix86 x86_64
      m68k
      ppc ppc64
      sparcv7 sparcv8_micro sparcv8_super

Level 1: auto (as detected), none (native code only), or gmp

• A fully qualified kernel name is of the form Level0 -Level1 , the default value being auto-auto.

• A name not containing a dash ’-’ is an alias for a fully qualified kernel name. An alias stands for name-none, but gmp stands for auto-gmp.

3.5. Problems related to readline. Configure does not try very hard to find the readline library and include files. If they are not in a standard place, it will not find them. You can invoke Configure with one of the following arguments:

    --with-readline[=prefix to lib/libreadline.xx and include/readline.h]
    --with-readline-lib=path to libreadline.xx
    --with-readline-include=path to readline.h

Known problems.

• on Linux: Linux distributions have separate readline and readline-devel packages. You need both of them installed to compile gp with readline support. If only readline is installed, Configure will complain. Configure may also complain about a missing libncurses.so, in which case, you have to install the ncurses-devel package (some distributions let you install readlinedevel without ncurses-devel, which is a bug in their package dependency handling).

• on OS X.4 or higher: these systems comes equipped with a fake readline, which is not sufficient for our purpose. As a result, gp is built without readline support. Since readline is not trivial to install in this environment, a step by step solution can be found in the PARI FAQ, see

      http://pari.math.u-bordeaux.fr/

3.6. Testing.

3.6.1. Known problems. if BUG shows up in make bench

• program: the GP function install may not be available on your platform, triggering an error message (“not yet available for this architecture”).

• If when running gp-dyn, you get a message of the form

    ld.so:  warning:  libpari.so.xxx has older revision than expected xxx

(possibly followed by more errors), you already have a dynamic PARI library installed and a broken local configuration. Either remove the old library or unset the LD LIBRARY PATH environment variable. Try to disable this variable in any case if anything very wrong occurs with the gp-dyn binary, like an Illegal Instruction on startup. It does not affect gp-sta.

• Some implementations of the diff utility (on HPUX for instance) output No differences encountered or some similar message instead of the expected empty input, thus producing a spurious [BUG] message.

3.6.2. Some more testing. [Optional]

You can test gp in compatibility mode with make test-compat. If you want to test the graphic routines, use make test-ploth. You will have to click on the mouse button after seeing each image. There will be eight of them, probably shown twice (try to resize at least one of them as a further test).

    The make bench, make test-compat and make test-ploth runs all produce a Postscript file

pari.ps in Oxxx which you can send to a Postscript printer. The output should bear some similarity to the screen images.

3.6.3. Heavy-duty testing. [Optional] There are a few extra tests which should be useful only for developers.

make test-kernel checks whether the low-level kernel seems to work, and provides simple diagnostics if it does not. Only useful if make bench fails horribly, e.g. things like 1+1 do not work.

make test-all runs all available test suites. Thorough, but slow. Some of the tests require extra packages (elldata, galdata, etc.) to be available. If you want to test such an extra package before make install (which would install it to its final location, where gp expects to find it), run

      env GP_DATA_DIR=$PWD/data make test-all

from the PARI toplevel directory, otherwise the test will fail.

make test-io tests writing to and reading from files. It requires a working system() command (fails on Windows + MingW).

make test-time tests absolute and relative timers. This test has a tendency to fail when the machine is heavily loaded or if the granularity of the chosen system timer is bigger than 2ms. Try it a few times before reporting a problem.

4. Installation.

When everything looks fine, type

    make install

You may have to do this with superuser privileges, depending on the target directories. (Tip for MacOS X beginners: use sudo make install.) In this case, it is advised to type make all first to avoid running unnecessary commands as root.

Caveat. Install directories are created honouring your umask settings: if your umask is too restrictive, e.g. 077, the installed files will not be world-readable. (Beware that running sudo may change your user umask.)

This installs in the directories chosen at Configure time the default gp executable (probably gp-dyn) under the name gp, the default PARI library (probably libpari.so), the necessary include files, the manual pages, the documentation and help scripts.

To save on disk space, you can manually gzip some of the documentation files if you wish: usersch*.tex and all dvi files (assuming your xdvi knows how to deal with compressed files); the online-help system can handle it.

4.1. Static binaries and libraries. By default, if a dynamic library libpari.so can be built, the gp binary we install is gp-dyn, pointing to libpari.so. On the other hand, we can build a gp binary into which the libpari is statically linked (the library code is copied into the binary); that binary is not independent of the machine it was compiled on, and may still refer to other dynamic libraries than libpari.

You may want to compile your own programs in the same way, using the static libpari.a instead of libpari.so. By default this static library libpari.a is not created. If you want it as well, use the target make install-lib-sta. You can install a statically linked gp with the target make install-bin-sta. As a rule, programs linked statically (with libpari.a) may be slightly faster (about 5% gain, possibly up to 20% when using pthreads), but use more disk space and take more time to compile. They are also harder to upgrade: you will have to recompile them all instead of just installing the new dynamic library. On the other hand, there is no risk of breaking them by installing a new pari library.

4.2. Extra packages. The following optional packages endow PARI with some extra capabilities:

• elldata: This package contains the elliptic curves in John Cremona’s database. It is needed by the functions ellidentify, ellsearch, forell and can be used by ellinit to initialize a curve given by its standard code.

• galdata: The default polgalois function can only compute Galois groups of polynomials of degree less or equal to 7. Install this package if you want to handle polynomials of degree bigger than 7 (and less than 11).

• seadata: This package contains the database of modular polynomials extracted from the ECHIDNA databases and computed by David R. Kohel. It is needed by the functions ellap and ellgroup for primes larger than 10^{20}.

• galpol: This package contains the GALPOL database of polynomials defining Galois extensions of the rationals, accessed by galoisgetpol.

To install package pack, you need to fetch the separate archive: pack.tgz which you can download from the pari server. Copy the archive in the PARI toplevel directory, then extract its contents; these will go to data/pack/. Typing make install installs all such packages.

4.3. The GPRC file. Copy the file misc/gprc.dft (or gprc.dos if you are using GP.EXE) to $HOME/.gprc. Modify it to your liking. For instance, if you are not using an ANSI terminal, remove control characters from the prompt variable. You can also enable colors.

If desired, read $datadir/misc/gpalias from the gprc file, which provides some common shortcuts to lengthy names; fix the path in gprc first. (Unless you tampered with this via Configure, datadir is $prefix/share/pari.) If you have superuser privileges and want to provide systemwide defaults, copy your customized .gprc file to /etc/gprc.

In older versions, gphelp was hidden in pari lib directory and was not meant to be used from the shell prompt, but not anymore. If gp complains it cannot find gphelp, check whether your .gprc (or the system-wide gprc) does contain explicit paths. If so, correct them according to the

current misc/gprc.dft.

5. Getting Started.

5.1. Printable Documentation. Building gp with make all also builds its documentation. You can also type directly make doc. In any case, you need a working (plain) TEX installation.

After that, the doc directory contains various dvi files: libpari.dvi (manual for the PARI library), users.dvi (manual for the gp calculator), tutorial.dvi (a tutorial), and refcard.dvi (a reference card for GP). You can send these files to your favorite printer in the usual way, probably via dvips. The reference card is also provided as a PostScript document, which may be easier to print than its dvi equivalent (it is in Landscape orientation and assumes A4 paper size).

If pdftex is part of your TEX setup, you can produce these documents in PDF format, which may be more convenient for online browsing (the manual is complete with hyperlinks); type

    make docpdf

All these documents are available online from PARI home page (see the last section).

5.2. C programming. Once all libraries and include files are installed, you can link your C programs to the PARI library. A sample makefile examples/Makefile is provided to illustrate the use of the various libraries. Type make all in the examples directory to see how they perform on the extgcd.c program, which is commented in the manual.

This should produce a statically linked binary extgcd-sta (standalone), a dynamically linked binary extgcd-dyn (loads libpari at runtime) and a shared library libextgcd, which can be used from gp to install your new extgcd command.

The standalone binary should be bulletproof, but the other two may fail for various reasons. If when running extgcd-dyn, you get a message of the form “DLL not found”, then stick to statically linked binaries or look at your system documentation to see how to indicate at linking time where the required DLLs may be found! (E.g. on Windows, you will need to move libpari.dll somewhere in your PATH.)

5.3. GP scripts. Several complete sample GP programs are also given in the examples directory, for example Shanks’s SQUFOF factoring method, the Pollard rho factoring method, the Lucas-Lehmer primality test for Mersenne numbers and a simple general class group and fundamental unit algorithm. See the file examples/EXPLAIN for some explanations.

5.4. The PARI Community. PARI’s home page at the address

      http://pari.math.u-bordeaux.fr/

maintains an archive of mailing lists dedicated to PARI, documentation (including Frequently Asked Questions), a download area and our Bug Tracking System (BTS). Bug reports should be submitted online to the BTS, which may be accessed from the navigation bar on the home page or directly at

      http://pari.math.u-bordeaux.fr/Bugs/

Further information can be found at that address but, to report a configuration problem, make sure to include the relevant *.dif files in the Oxxx directory and the file pari.cfg.

There are a number of mailing lists devoted to PARI/GP, and most feedback should be directed there. Instructions and archives can be consulted at

      http://pari.math.u-bordeaux1.fr/lists-index.html

The most important are:

• pari-announce (read-only): to announce major version changes. You cannot write to this one, but you should probably subscribe.

• pari-dev: for everything related to the development of PARI, including suggestions, technical questions or patch submissions. Bug reports can be discussed here, but as a rule it is better to submit them directly to the BTS.

• pari-users: for everything else.

You may send an email to the last two without being subscribed. To subscribe, send an message respectively to

      pari-announce-request@pari.math.u-bordeaux.fr
         pari-users-request@pari.math.u-bordeaux.fr
           pari-dev-request@pari.math.u-bordeaux.fr

with the word subscribe in the Subject:. You can also write to us at the address

      pari@math.u-bordeaux.fr

but we cannot promise you will get an individual answer.

If you have used PARI in the preparation of a paper, please cite it in the following form (BibTeX format):

    @preamble{\usepackage{url}}
    @manual{PARI2,
        organization = "{The PARI~Group}",
        title        = "{PARI/GP version 2.7.2}",
        year         = 2014,
        address      = "Bordeaux",
        note         = "available from \url{http://pari.math.u-bordeaux.fr/}"
    }

In any case, if you like this software, we would be indebted if you could send us an email message giving us some information about yourself and what you use PARI for.

Good luck and enjoy!

Index

SomeWord refers to PARI-GP concepts. SomeWord is a PARI-GP keyword. SomeWord is a generic index entry.

A

a1 . . . . . . . . . . . . . . . . . . . . . . 130 a2 . . . . . . . . . . . . . . . . . . . . . . 130 a3 . . . . . . . . . . . . . . . . . . . . . . 130 a4 . . . . . . . . . . . . . . . . . . . . . . 130 a6 . . . . . . . . . . . . . . . . . . . . . . 130 Abelian extension . . . . . . . . . . 203, 211 abs . . . . . . . . . . . . . . . . . . . . . 89 accuracy . . . . . . . . . . . . . . . . . . . . 9 acos . . . . . . . . . . . . . . . . . . . . . 90 acosh . . . . . . . . . . . . . . . . . . . . 90 addhelp . . . . . . . . . . . . . . . . . 46, 287 addprimes . 100, 110, 186, 194, 195, 199, 306 adj . . . . . . . . . . . . . . . . . . . . . 233 adjoint matrix . . . . . . . . . . . . . . . 233 adjsafe . . . . . . . . . . . . . . . . . . . 233 agm . . . . . . . . . . . . . . . . . . . . . 90 akell . . . . . . . . . . . . . . . . . . . . 133 alarm . . . . . . . . . . . . . . . . . 286, 287 algdep . . . . . . . . . . . . . . . . 228, 229 algdep0 . . . . . . . . . . . . . . . . . . . 229 algebraic dependence . . . . . . . . . 228, 251 algebraic number . . . . . . . . . . . . . . 152 algtobasis . . . . . . . . . . . . . . . . . 184 alias . . . . . . . . . . . . . . . . . . 46, 288 alias0 . . . . . . . . . . . . . . . . . . . 289 allocatemem . . . . . . . . . . 289, 297, 309 alternating series . . . . . . . . . . . . . . 267 and . . . . . . . . . . . . . . . . . . . . . 70 and . . . . . . . . . . . . . . . . . . . . . 77 anell . . . . . . . . . . . . . . . . . . . . 133 anellsmall . . . . . . . . . . . . . . . . . 133 apply . . . . . . . . . . . . . . . . . . 10, 290 area . . . . . . . . . . . . . . . . . . . . . 130 arg . . . . . . . . . . . . . . . . . . . . . 90 Artin L-function . . . . . . . . . . . . . . 166 Artin root number . . . . . . . . . . . . . 166 asin . . . . . . . . . . . . . . . . . . . . . 90 asinh . . . . . . . . . . . . . . . . . . . . 90 atan . . . . . . . . . . . . . . . . . . . . . 90 atanh . . . . . . . . . . . . . . . . . . . . 91 automatic simplification . . . . . . . . . . 311 available commands . . . . . . . . . . . . 56

B

b2 . . . . . . . . . . . . . . . . . . . . . . 130 b4 . . . . . . . . . . . . . . . . . . . . . . 130 b6 . . . . . . . . . . . . . . . . . . . . . . 130 b8 . . . . . . . . . . . . . . . . . . . . . . 130 backslash character . . . . . . . . . . . . 16 basistoalg . . . . . . . . . . . . . . . . . 187 Berlekamp . . . . . . . . . . . . . . . . . . 110 bernfrac . . . . . . . . . . . . . . . . . . 91 Bernoulli numbers . . . . . . . . . . . 91, 98 Bernoulli polynomial . . . . . . . . . . . . 91 bernpol . . . . . . . . . . . . . . . . . . . 91 bernreal . . . . . . . . . . . . . . . . . . 91 bernvec . . . . . . . . . . . . . . . . . . . 91 besselh1 . . . . . . . . . . . . . . . . . . 91 besselh2 . . . . . . . . . . . . . . . . . . 91 besseli . . . . . . . . . . . . . . . . . . . 91 besselj . . . . . . . . . . . . . . . . . . . 91 besseljh . . . . . . . . . . . . . . . . . . 92 besselk . . . . . . . . . . . . . . . . . . . 92 besseln . . . . . . . . . . . . . . . . . . . 92 bestappr . . . . . . . . . . . . . 78, 100, 101 bestapprPade . . . . . . . . . . . . . . . 101 Bezout relation . . . . . . . . . . . . . . . 114 bezout . . . . . . . . . . . . . . . . 101, 113 bezoutres . . . . . . . . . . . . . . . . . 215 bid . . . . . . . . . . . . . . . . . . . . 44, 154 bid . . . . . . . . . . . . . . . . . . . . . 155 bigomega . . . . . . . . . . . . . . . . . . 101 bilhell . . . . . . . . . . . . . . . . . . . 135 binaire . . . . . . . . . . . . . . . . . . . 77 binary file . . . . . . . . . . . . . . . . . . 302 binary file . . . . . . . . . . . . . . 56, 297 binary flag . . . . . . . . . . . . . . . . . 63 binary quadratic form . . . . . . . . . 22, 74 binary . . . . . . . . . . . . . . . . . . . 76 binomial coefficient . . . . . . . . . . . . . 101 binomial . . . . . . . . . . . . . . . 101, 102 binomialuu . . . . . . . . . . . . . . . . . 102 Birch and Swinnerton-Dyer conjecture . . 137 bitand . . . . . . . . . . . . . . . . . . . 77 bitneg . . . . . . . . . . . . . . . . . . . 77 bitnegimply . . . . . . . . . . . . . . . . 77 bitor . . . . . . . . . . . . . . . . . . . . 77 bittest . . . . . . . . . . . . . . . . . 77, 78 bitwise and . . . . . . . . . . . . . . . . . 77 bitwise exclusive or . . . . . . . . . . . . 78 bitwise inclusive or . . . . . . . . . . . . . 77 bitwise negation . . . . . . . . . . . . . . 77 bitxor . . . . . . . . . . . . . . . . . . . 78 bnf . . . . . . . . . . . . . . . . . 44, 152, 157 bnf . . . . . . . . . . . . . . . . . . . . . 155 bnfcertify . . . . . . . . . . . . . . . . . 157 bnfcertify0 . . . . . . . . . . . . . . . . 157 bnfcompress . . . . . . . . . . . . . 157, 158 bnfdecodemodule . . . . . . . . . . 158, 165 bnfinit . . . . . . . . 124, 152, 157, 158, 182 bnfinit0 . . . . . . . . . . . . . . . . . . 159 bnfisintnorm . . . . . . . . . . . . 159, 160 bnfisintnormabs . . . . . . . . . . . . . 159 bnfisnorm . . . . . . . . . . . . . . 159, 160 bnfisprincipal . . . . . . 123, 159, 160, 182 bnfisprincipal0 . . . . . . . . . . . . . 161 bnfissunit . . . . . . . . . . . . . . . . . 161 bnfisunit . . . . . . . . . . . . . . . . . 161 bnfnarrow . . . . . . . . . . . . . . 124, 161 bnfnewprec . . . . . . . . . . . . . . . . . 195 bnfsignunit . . . . . . . . . . . . . . . . 162 bnfsunit . . . . . . . . . . . . . . . . . . 162 bnr . . . . . . . . . . . . . . . . . . . . 44, 152 bnrclassno . . . . . . . . . . . . . . 163, 165 bnrclassno0 . . . . . . . . . . . . . . . . 163 bnrclassnolist . . . . . . . . 163, 164, 177 bnrconductor . . . . . . . . . . . . . . . 164 bnrconductor0 . . . . . . . . . . . . . . . 164

bnrconductorofchar . . . . . . . . . . . 164

bnrdisc . . . . . . . . . . . . . . . . 164, 165 bnrdisc0 . . . . . . . . . . . . . . . . . . 164 bnrdisclist . . . . . . . . . . . . . 165, 177 bnrdisclist0 . . . . . . . . . . . . . . . 165 bnrinit . . . . . . . . . . . . . 155, 164, 165 bnrinit0 . . . . . . . . . . . . . . . . . . 166 bnrisconductor . . . . . . . . . . . . . . 166 bnrisconductor0 . . . . . . . . . . . . . 166 bnrisprincipal . . . . . . . . . . . 159, 166 bnrL1 . . . . . . . . . . . . . . . . . 162, 163 bnrnewprec . . . . . . . . . . . . . . . . . 195 bnrrootnumber . . . . . . . . . . . . 166, 167 bnrstark . . . . . . . . . . 125, 167, 168, 214 Boolean operators . . . . . . . . . . . . . 70 boundfact . . . . . . . . . . . . . . . . . 108 brace characters . . . . . . . . . . . . . . 16 break loop . . . . . . . . . . . . . . . . . . 49 break . . . . . . . . . . . . . . . . . . 49, 278 breakloop . . . . . . . . . . . . . . . 51, 304 breakpoint . . . . . . . . . . . . . . . . . 278 Buchall . . . . . . . . . . . . . . . . . . . 159 Buchall_param . . . . . . . . . . . . . . . 159 Buchmann . . . . . . . . . . . 156, 158, 181 Buchmann-McCurley . . . . . . . . . . . . 124 buchnarrow . . . . . . . . . . . . . . . . . 162 Buchquad . . . . . . . . . . . . . . . . . . 124 Buchray . . . . . . . . . . . . . . . . . . . 166

C

c4 . . . . . . . . . . . . . . . . . . . . . . 130 c6 . . . . . . . . . . . . . . . . . . . . . . 130 Cantor-Zassenhaus . . . . . . . . . . . . . 109 caract . . . . . . . . . . . . . . . . . . . 229 caradj . . . . . . . . . . . . . . . . . . . 229 carberkowitz . . . . . . . . . . . . . . . 229 carhess . . . . . . . . . . . . . . . . . . . 229 Catalan . . . . . . . . . . . . . . . . . . . 89 ceil . . . . . . . . . . . . . . . . . . . . . 78 centerlift . . . . . . . . . . . . . . . 78, 81 character string . . . . . . . . . . . . . . . 26 character . . . . . . . . . . . . . . . . . . 153 character . . . . . . . . . . . . 162, 164, 166 characteristic polynomial . . . . . . . . . 229 characteristic . . . . . . . . . . . . . . 78 charpoly . . . . . . . . . . . . . . . . . . 229 charpoly0 . . . . . . . . . . . . . . . . . 229 Chebyshev . . . . . . . . . . . . . . . . . 219 chinese . . . . . . . . . . . . . . . . . . . 102 chinese1 . . . . . . . . . . . . . . . . . . 102 classno . . . . . . . . . . . . . . . . . . . 122 classno2 . . . . . . . . . . . . . . . . . . 122 clgp . . . . . . . . . . . . . . . . . . . . . 155 CLISP . . . . . . . . . . . . . . . . . . . . 53 cmdtool . . . . . . . . . . . . . . . . . . . 310 cmp . . . . . . . . . . . . . . 67, 75, 233, 252 cmp_universal . . . . . . . . . . . . . . . 68 code words . . . . . . . . . . . . . . . . . 78 codiff . . . . . . . . . . . . . . . . . . . 155 Col . . . . . . . . . . . . . . . . . . . 23, 70 colors . . . . . . . . . . . . . . . . . . . 304 Colrev . . . . . . . . . . . . . . . . . 23, 71 column vector . . . . . . . . . . . . . . 7, 22 comparison operators . . . . . . . . . . . 70 compatible . . . . . . . . . . . . . . . . . 304 completion . . . . . . . . . . . . . . . . . 59 complex number . . . . . . . . . . . . 7, 8, 19 compo . . . . . . . . . . . . . . . . . . . . 79 component . . . . . . . . . . . . . . . 78, 283 composition . . . . . . . . . . . . . . . . . 122 compositum . . . . . . . . . . . . . . . . . 197 compositum . . . . . . . . . . . . . . . . . 198 compositum2 . . . . . . . . . . . . . . . . 198 compress . . . . . . . . . . . . . . . . . . 57 concat . . . . . . . . . . . . . . 45, 230, 231 concat1 . . . . . . . . . . . . . . . . . . . 231 conj . . . . . . . . . . . . . . . . . . . . . 79 conjvec . . . . . . . . . . . . . . . . . . . 79 content . . . . . . . . . 32, 33, 102, 103, 113 contfrac . . . . . . . . . . . . . . . . . . 103 contfrac0 . . . . . . . . . . . . . . . . . 104 contfracpnqn . . . . . . . . . . . . . . . 104 continued fraction . . . . . . . . . . . . . 103 convol . . . . . . . . . . . . . . . . . . . 224 Coppersmith . . . . . . . . . . . . . . . . 127 core . . . . . . . . . . . . . . . . . . . . . 104 core0 . . . . . . . . . . . . . . . . . . . . 104 core2 . . . . . . . . . . . . . . . . . . . . 104 coredisc . . . . . . . . . . . . . . . . . . 105 coredisc0 . . . . . . . . . . . . . . . . . 105 coredisc2 . . . . . . . . . . . . . . . . . 105 cos . . . . . . . . . . . . . . . . . . . . . 92 cosh . . . . . . . . . . . . . . . . . . . . . 92 cotan . . . . . . . . . . . . . . . . . . . . 92 CPU time . . . . . . . . . . . . . . . . . . 313 cyc . . . . . . . . . . . . . . . . . . 132, 155

D

datadir . . . . . . . . . . . . . . . . . . . 305 dbg_down . . . . . . . . . . . . . 50, 278, 279 dbg_err . . . . . . . . . . . . . . . . . 51, 279 dbg_up . . . . . . . . . . . . . . . . . 50, 279 dbg_x . . . . . . . . . . . . . . . . 51, 57, 279 debug . . . . . . . . . . . . . . . 56, 110, 305 debugfiles . . . . . . . . . . . . . . . 56, 305 debugmem . . . . . . . . . . . . . . . . 56, 305 decodemodule . . . . . . . . . . . . . . . 158 decomposition into squares . . . . . . . . 246 Dedekind sum . . . . . . . . . . . . . . . 127 Dedekind . . . . . . . . 92, 168, 203, 214, 215 deep recursion . . . . . . . . . . . . . . . 42

def,factor_add_primes . . . . . . . . . 306
def,factor_proven . . . . . . . . . . . . 306
def,new_galois_format . . . . . . . . . 308

def,prompt_cont . . . . . . . . . . . . . 310 default precision . . . . . . . . . . . . . . . 9 default . . . . . . . . . . . . . . . . . 46, 291 default0 . . . . . . . . . . . . . . . . . . 291 defaults . . . . . . . . . . . . . . . . . 53, 56 denom . . . . . . . . . . . . . . . . . . . . 80 denominator . . . . . . . . . . . . 32, 33, 79 deplin . . . . . . . . . . . . . . . . . . . 232 deriv . . . . . . . . . . . . . . . . . . . . 215 derivfun . . . . . . . . . . . . . . . . . . 257 derivnum . . . . . . . . . . . . . . . . . . 257 det . . . . . . . . . . . . . . . . . . . . . 235 det0 . . . . . . . . . . . . . . . . . . . . . 235 det2 . . . . . . . . . . . . . . . . . . . . . 235 detint . . . . . . . . . . . . . . . . . . . 235 diagonal . . . . . . . . . . . . . . . . . . 236 diff . . . . . . . . . . . . . . . . . . . . . 155 difference . . . . . . . . . . . . . . . . . . 64 diffop . . . . . . . . . . . . . . . . . . . 216 diffop0 . . . . . . . . . . . . . . . . . . . 216 digits . . . . . . . . . . . . . . . . . . . 80 dilog . . . . . . . . . . . . . . . . . . . . 92 dirdiv . . . . . . . . . . . . . . . . . . . 105 direuler . . . . . . . . . . . . . . . . . . 105 Dirichlet series . . . . . . . . . . . . 105, 168 dirmul . . . . . . . . . . . . . . . . . . . 105 dirzetak . . . . . . . . . . . . . . . . . . 168 disc . . . . . . . . . . . . . . . . . . 130, 155 divisors . . . . . . . . . . . . . . . 105, 279 divrem . . . . . . . . . . . . . . . . . 33, 68 dvi . . . . . . . . . . . . . . . . . . . . . . 61 dynamic scoping . . . . . . . . . . . . . . 34

E

echo . . . . . . . . . . . . . . . . . . . 56, 305 ECM . . . . . . . . . . . . . . . . . . . 99, 110 editing characters . . . . . . . . . . . . . 16 Egyptian fraction . . . . . . . . . . . . . . 104 eigen . . . . . . . . . . . . . . . . . . . . 236 eint1 . . . . . . . . . . . . . . . . . . . . 92 elementary divisors . . . . . . . . . . . . . 242 ell . . . . . . . . . . . . . . . . . 44, 130, 141 ell . . . . . . . . . . . . . . . . . . . . . 140 elladd . . . . . . . . . . . . . . . . . . . 133 ellak . . . . . . . . . . . . . . . . . . . . 133 ellan . . . . . . . . . . . . . . . . . . . . 133 ellanalyticrank . . . . . . . 132, 133, 134 ellap . . . . . . . . . . . . . . . . . 134, 135 ellbil . . . . . . . . . . . . . . . . . . . 135 ellcard . . . . . . . . . . . . . . . . . . . 135 ellchangecurve . . . . . . . . . . . . . . 135 ellchangepoint . . . . . . . . . . . . . . 135

ellchangepointinv . . . . . . . . . 135, 136

ellconvertname . . . . . . . . . . . 136, 146 elldata . . . . . 132, 136, 137, 140, 146, 280 elldivpol . . . . . . . . . . . . . . . . . 136 elleisnum . . . . . . . . . . . . . . 136, 137 elleta . . . . . . . . . . . . . . . . 137, 149 ellfromj . . . . . . . . . . . . . . . . . . 137 ellgenerators . . . . . . . . . . . . 132, 137 ellglobalred . . . . . . . . . . . . 137, 138 ellgroup . . . . . . . . . . . . 135, 138, 139 ellgroup0 . . . . . . . . . . . . . . . . . 139 ellheegner . . . . . . . . . . . . . . . . . 139 ellheight . . . . . . . . . . . . . . . . . 139 ellheight0 . . . . . . . . . . . . . . . . . 139 ellheightmatrix . . . . . . . . . . . . . 139 ellidentify . . . . . . . . . . . . . 132, 140 ellinit . . . . . . . . 130, 132, 137, 140, 141 ellisoncurve . . . . . . . . . . . . . . . 141 ellj . . . . . . . . . . . . . . . . . . . . . 142 ellL1 . . . . . . . . . . . . . . . . . 132, 133 elllocalred . . . . . . . . . . . . . . . . 142 elllog . . . . . . . . . . . . . . . . . . . 142 elllseries . . . . . . . . . . . . . . . . . 142 ellminimalmodel . . . . . . . . . . 137, 143 ellmodulareqn . . . . . . . . . . . . 143, 144 ellmul . . . . . . . . . . . . . . . . 144, 146 ellneg . . . . . . . . . . . . . . . . . . . 144 ellorder . . . . . . . . . . . . . . . 144, 145 ellordinate . . . . . . . . . . . . . . 84, 145 ellperiods … 130, 136, 145, 147, 148, 149 ellpointtoz . . . . . . . . . . . . . . . . 145 ellpow . . . . . . . . . . . . . . . . . . . 146 ellrandom . . . . . . . . . . . . . . . . . 85 ellrootno . . . . . . . . . . . . . . . . . 146 ellsearch . . . . . . . . . . . 132, 146, 147 ellsearchcurve . . . . . . . . . . . . . . 147 ellsigma . . . . . . . . . . . . . . . . . . 147 ellsub . . . . . . . . . . . . . . . . 147, 148 elltaniyama . . . . . . . . . . . . . . . . 148 elltatepairing . . . . . . . . . . . . . . 148 elltors . . . . . . . . . . . . . . . . . . . 148 elltors0 . . . . . . . . . . . . . . . . . . 148 ellweilpairing . . . . . . . . . . . . . . 148 ellwp . . . . . . . . . . . . . . . . . 148, 149 ellwp0 . . . . . . . . . . . . . . . . . . . 149 ellwpseries . . . . . . . . . . . . . . . . 149 ellzeta . . . . . . . . . . . . . . . . 131, 149 ellztopoint . . . . . . . . . . . . . . . . 149 Emacs . . . . . . . . . . . . . . . . . . . . 61 Engel expansion . . . . . . . . . . . . . . 104 environment expansion . . . . . . . . . . 75

                                           environment expansion . . . . . . . . . 54

environment variable . . . . . . . . . . . . 75 erfc . . . . . . . . . . . . . . . . . . . . . 92 errname . . . . . . . . . . . . . . . . . . . 291 error recovery . . . . . . . . . . . . . . . . 48 error trapping . . . . . . . . . . . . . . . . 49 error . . . . . . . . . . . . . 46, 49, 286, 291 error(E) . . . . . . . . . . . . . . . . . . 283 eta . . . . . . . . . . . . . . . . 92, 131, 266 eta0 . . . . . . . . . . . . . . . . . . . . . 93 Euclid . . . . . . . . . . . . . . . . . . . . 113 Euclidean quotient . . . . . . . . . . . 65, 66 Euclidean remainder . . . . . . . . . . . . 66 Euler product . . . . . . . . . . 105, 121, 266 Euler totient function . . . . . . . . . 99, 106 Euler . . . . . . . . . . . . . . . . . . . . 89 Euler-Maclaurin . . . . . . . . . . . . . . 98 eulerphi . . . . . . . . . . . . . . . . . . 106 eval . . . . . . . . . . . . . . . 34, 36, 46, 216 exp . . . . . . . . . . . . . . . . . . . . . 93 expm1 . . . . . . . . . . . . . . . . . . . . 93 expression sequence . . . . . . . . . . . . 15 expression . . . . . . . . . . . . . . . . . . 15 extended gcd . . . . . . . . . . . . . . . . 114 extern . . . . . . . . . . . . . . 46, 291, 311 external prettyprint . . . . . . . . . . . . . 308 externstr . . . . . . . . . . . . . . . . . 291 extract0 . . . . . . . . . . . . . . . . . . 254

F

factcantor . . . . . . . . . . . . . . . . . 109 factor . . . . . . . . . . . . . 105, 106, 108 factor0 . . . . . . . . . . . . . . . . . . . 108 factorback . . . . . . . . . . . . . . 108, 109 factorback2 . . . . . . . . . . . . . . . . 109 factorcantor . . . . . . . . . . . . . . . 109 factorff . . . . . . . . . . . . 107, 109, 110 factorial . . . . . . . . . . . . . . . . . 110 factorint . . . . . . . . . . . . . . 106, 110 factormod . . . . . . . . . . . . . . 107, 110 factormod0 . . . . . . . . . . . . . . . . . 110 factornf . . . . . . . . . . . . . . . 107, 168 factorpadic . . . . . . . . . . . . . 217, 218

                                           factor_add_primes . . . . . . . . . . . . 288

factor_proven . . . . . . . . . 100, 106, 110 famat . . . . . . . . . . . . . . . . . . . . 152 ff . . . . . . . . . . . . . . . . . . . . . . . 44 ffgen . . . . . . . . . . 18, 19, 110, 111, 140 ffinit . . . . . . . . . . . . . . 18, 110, 111 fflog . . . . . . . . . . . . . . . . . 111, 112 ffnbirred . . . . . . . . . . . . . . . . . 112 ffnbirred0 . . . . . . . . . . . . . . . . . 112 fforder . . . . . . . . . . . . . . . . . . . 112 ffprimroot . . . . . . . . . . . 111, 112, 113 ffrandom . . . . . . . . . . . . . . . . . . 85 ffsumnbirred . . . . . . . . . . . . . . . 112 fibo . . . . . . . . . . . . . . . . . . . . . 113 fibonacci . . . . . . . . . . . . . . . . . 113 field discriminant . . . . . . . . . . . . . . 187 filename . . . . . . . . . . . . . . . . . . . 54 finite field element . . . . . . . . . . . 7, 8, 18 finite field . . . . . . . . . . . . . . . . . . 20 fixed floating point format . . . . . . . . 306 flag . . . . . . . . . . . . . . . . . . . . . . 63 floor . . . . . . . . . . . . . . . . . . . . 80 fltk . . . . . . . . . . . . . . . . . . . . . 271 for . . . . . . . . . . . . . . . . . . . . . 279 forcomposite . . . . . . . . . . . . . . . 279 Ford . . . . . . . . . . . . . . . . . . . . . 185 fordiv . . . . . . . . . . . . . . . . . . . 279 forell . . . . . . . . . . . . . . . . 132, 280 formal integration . . . . . . . . . . . . . 218 formal sum . . . . . . . . . . . . . . . . . 226 format . . . . . . . . . . . . . . . . 306, 310 forpart . . . . . . . . . . . . . . . . 280, 281 forprime . . . . . . . . . . . . . . . . . . 281 forqfvec . . . . . . . . . . . . . . . . . . 231 forqfvec0 . . . . . . . . . . . . . . . . . 231 forstep . . . . . . . . . . . . . . . . . . . 281 forsubgroup . . . . . . . . . . . . . . . . 282 forvec . . . . . . . . . . . . . . . . . . . 282 frac . . . . . . . . . . . . . . . . . . . . . 80 free variable . . . . . . . . . . . . . . . . . 31 fu . . . . . . . . . . . . . . . . . . . . . . 155 fundamental units . . . . . . . 125, 155, 158 futu . . . . . . . . . . . . . . . . . . . . . 155

G

gabs . . . . . . . . . . . . . . . . . . . . . 90 gacos . . . . . . . . . . . . . . . . . . . . 90 gacosh . . . . . . . . . . . . . . . . . . . 90 gadd . . . . . . . . . . . . . . . . . . . . . 64 galdata . . . . . . . . . . . . . . . . . . . 198 galois . . . . . . . . . . . . . . . . . . . . 44 Galois …. 160, 190, 191, 197, 198, 210, 282 galoisapply . . . . . . . . . . . . . . . . 191 galoisconj . . . . . . . . . . . . . . . . . 192 galoisconj0 . . . . . . . . . . . . . . . . 192 galoisexport . . . . . . . . . 168, 169, 170 galoisfixedfield . . . . . . . . . . 169, 282 galoisgetpol . . . . . . . . . . . . . . . 169 galoisidentify . . . . . . . . . . . 169, 170 galoisinit . . . . . . 168, 169, 170, 171, 192 galoisisabelian . . . . . . . . . . . . . 171 galoisisnormal . . . . . . . . . . . . . . 171 galoisnbpol . . . . . . . . . . . . . . . . 169 galoispermtopol . . . . . . . . . . . . . 171 galoissubcyclo … 167, 171, 172, 224, 282 galoissubfields . . . . . . . 168, 172, 197 galoissubgroups . . . . . . . . . . 172, 173 gamma . . . . . . . . . . . . . . . . . . . . 93 gamma-function . . . . . . . . . . . . . . 93 gammah . . . . . . . . . . . . . . . . . . . 94 garg . . . . . . . . . . . . . . . . . . . . . 90 gasin . . . . . . . . . . . . . . . . . . . . 90 gasinh . . . . . . . . . . . . . . . . . . . 90 gatan . . . . . . . . . . . . . . . . . . . . 90 gatanh . . . . . . . . . . . . . . . . . . . 91 gauss . . . . . . . . . . . . . . . . . . . . 243 gaussmodulo . . . . . . . . . . . . . . . . 243 gaussmodulo2 . . . . . . . . . . . . . . . 243 gbitand . . . . . . . . . . . . . . . . . . . 77 gbitneg . . . . . . . . . . . . . . . . . . . 77 gbitnegimply . . . . . . . . . . . . . . . 77 gbitor . . . . . . . . . . . . . . . . . . . 77 gbittest . . . . . . . . . . . . . . . . . . 78 gbitxor . . . . . . . . . . . . . . . . . . . 78 gboundcf . . . . . . . . . . . . . . . . . . 104 gcd . . . . . . . . . . . . . . . . . . . . . 113 gcdext . . . . . . . . . . . . . . . . 113, 114 gcdext0 . . . . . . . . . . . . . . . . 101, 114 gceil . . . . . . . . . . . . . . . . . . . . 78 gcf . . . . . . . . . . . . . . . . . . . . . 104 gcf2 . . . . . . . . . . . . . . . . . . . . . 104 gconj . . . . . . . . . . . . . . . . . . . . 79 gcos . . . . . . . . . . . . . . . . . . . . . 92 gcosh . . . . . . . . . . . . . . . . . . . . 92 gcotan . . . . . . . . . . . . . . . . . . . 92 gcvtoi . . . . . . . . . . . . . . . . . . . 86 gdeflate . . . . . . . . . . . . . . . . . . 225 gdiv . . . . . . . . . . . . . . . . . . . . . 65 gdivent . . . . . . . . . . . . . . . . . . . 66 gdiventres . . . . . . . . . . . . . . . . . 68 gdivround . . . . . . . . . . . . . . . . . 66 gen (member function) . . . . . . . . . . 155 GEN . . . . . . . . . . . . . . . . . . . . . . 8 gen . . . . . . . . . . . . . . . . . . . . . 132 genapply . . . . . . . . . . . . . . . . . . 291 generic matrix . . . . . . . . . . . . . . . 46 genindexselect . . . . . . . . . . . . . . 299 genrand . . . . . . . . . . . . . . . . . . . 85 genselect . . . . . . . . . . . . . . . . . 299 GENtostr . . . . . . . . . . . . . . . . . . 75 genus2red . . . . . . . . . . . . . . 150, 151 gen_I . . . . . . . . . . . . . . . . . . . . 89 gerfc . . . . . . . . . . . . . . . . . . . . 92 getabstime . . . . . . . . . . . . . . 291, 292 getenv . . . . . . . . . . . . . . . . . . . 291 getheap . . . . . . . . . . . . . . . . . . . 292 getrand . . . . . . . . . . . . . . . . . 84, 292 getstack . . . . . . . . . . . . . . . . . . 292 gettime . . . . . . . . . . . . . . . . . . . 292 geval . . . . . . . . . . . . . . . . . . . . 217 gexp . . . . . . . . . . . . . . . . . . . . . 93 gexpm1 . . . . . . . . . . . . . . . . . . . 93 gfloor . . . . . . . . . . . . . . . . . . . 80 gfrac . . . . . . . . . . . . . . . . . . . . 80 ggamma . . . . . . . . . . . . . . . . . . . 94 ggammah . . . . . . . . . . . . . . . . . . . 94 ggcd . . . . . . . . . . . . . . . . . . . . . 113 ggcd0 . . . . . . . . . . . . . . . . . . . . 113 ggrando . . . . . . . . . . . . . . . . . . . 215 ghell . . . . . . . . . . . . . . . . . . . . 139 gimag . . . . . . . . . . . . . . . . . . . . 80 gisanypower . . . . . . . . . . . . . . . . 115 gisprime . . . . . . . . . . . . . . . . . . 116 gispseudoprime . . . . . . . . . . . . . . 116 gissquare . . . . . . . . . . . . . . . . . 117 gissquareall . . . . . . . . . . . . . . . 117 glambdak . . . . . . . . . . . . . . . . . . 215 glambertW . . . . . . . . . . . . . . . . . 94 glcm0 . . . . . . . . . . . . . . . . . . . . 118 glength . . . . . . . . . . . . . . . . . . . 81 glngamma . . . . . . . . . . . . . . . . . . 95 global . . . . . . . . . . . . . . . . . . . 292 glog . . . . . . . . . . . . . . . . . . . . . 95 gmax . . . . . . . . . . . . . . . . . . . . . 69 gmin . . . . . . . . . . . . . . . . . . . . . 69 gmod . . . . . . . . . . . . . . . . . . . . . 66 gmodulo . . . . . . . . . . . . . . . . . . . 73 gmul . . . . . . . . . . . . . . . . . . . . . 65 gmul2n . . . . . . . . . . . . . . . . . . . 69 gneg . . . . . . . . . . . . . . . . . . . . . 64 gnorm . . . . . . . . . . . . . . . . . . . . 82 gnorml2 . . . . . . . . . . . . . . . . . . . 244 gnormlp . . . . . . . . . . . . . . . . . . . 245 GP . . . . . . . . . . . . . . . . . . . . . . . 5 gp . . . . . . . . . . . . . . . . . . . . . 5, 13 gp2c . . . . . . . . . . . . . . . . . . . . . . 5 gphelp . . . . . . . . . . . . . . . . . . . 55 gpinstall . . . . . . . . . . . . . . . . . 293 gpolvar . . . . . . . . . . . . . . . . . . . 87 gpolylog . . . . . . . . . . . . . . . . . . 96 gpow . . . . . . . . . . . . . . . . . . . 67, 89 gprc . . . . . . . . . . . . . . . 13, 32, 53, 57 GPRC . . . . . . . . . . . . . . . . . . . . . 59 gprc . . . . . . . . . . . . . . . . . . . . . 308 gprec . . . . . . . . . . . . . . . . . . . . 84 gpsi . . . . . . . . . . . . . . . . . . . . . 96 gpwritebin . . . . . . . . . . . . . . . . . 302 gp_factor0 . . . . . . . . . . . . . . . . . 108 gp_getenv . . . . . . . . . . . . . . . . . 291 gp_readvec_file . . . . . . . . . . . . . 298

                                           gp_readvec_stream . . . . . . . . . . . . 298

Graeffe . . . . . . . . . . . . . . . . . . . 220 graphcolormap . . . . . . . . . 273, 306, 307 graphcolors . . . . . . . . . . . . . . . . 307 greal . . . . . . . . . . . . . . . . . . . . 85 GRH . . . . . . . 156, 157, 159, 160, 210, 227 grndtoi . . . . . . . . . . . . . . . . . . . 85 ground . . . . . . . . . . . . . . . . . . . 85 group . . . . . . . . . . . . . . . . . . . . 132 gshift . . . . . . . . . . . . . . . . . . . 69 gsigne . . . . . . . . . . . . . . . . . . . 69 gsin . . . . . . . . . . . . . . . . . . . . . 96 gsinh . . . . . . . . . . . . . . . . . . . . 96 gsizebyte . . . . . . . . . . . . . . . . . 86 gsizeword . . . . . . . . . . . . . . . . . 86 gsqr . . . . . . . . . . . . . . . . . . . 65, 96 gsqrt . . . . . . . . . . . . . . . . . . . . 96 gsqrtn . . . . . . . . . . . . . . . . . . . 97 gsub . . . . . . . . . . . . . . . . . . . . . 65 gsubst . . . . . . . . . . . . . . . . . . . 225 gsubstpol . . . . . . . . . . . . . . . . . 225 gsubstvec . . . . . . . . . . . . . . . . . 226 gtan . . . . . . . . . . . . . . . . . . . . . 97 gtanh . . . . . . . . . . . . . . . . . . . . 97 gtocol . . . . . . . . . . . . . . . . . . . 71 gtocol0 . . . . . . . . . . . . . . . . . . . 71 gtocolrev . . . . . . . . . . . . . . . . . 71 gtocolrev0 . . . . . . . . . . . . . . . . . 71 gtolist . . . . . . . . . . . . . . . . . . . 71 gtomat . . . . . . . . . . . . . . . . . . . 72 gtopoly . . . . . . . . . . . . . . . . . . . 73 gtopolyrev . . . . . . . . . . . . . . . . . 74 gtoser . . . . . . . . . . . . . . . . . . . 75 gtoset . . . . . . . . . . . . . . . . . . . 75 gtovec . . . . . . . . . . . . . . . . . . . 76 gtovec0 . . . . . . . . . . . . . . . . . . . 76 gtovecrev . . . . . . . . . . . . . . . . . 76 gtovecrev0 . . . . . . . . . . . . . . . . . 76 gtovecsmall . . . . . . . . . . . . . . . . 76 gtovecsmall0 . . . . . . . . . . . . . . . 76 gtrace . . . . . . . . . . . . . . . . . . . 253 gtrans . . . . . . . . . . . . . . . . . . . 244 gtrunc . . . . . . . . . . . . . . . . . . . 86 gvaluation . . . . . . . . . . . . . . . . . 87 gvar . . . . . . . . . . . . . . . . . . . . . 87 gzeta . . . . . . . . . . . . . . . . . . . . 98 gzetak . . . . . . . . . . . . . . . . . . . 215 gzetakall . . . . . . . . . . . . . . . . . 215 gzip . . . . . . . . . . . . . . . . . . . 57, 302

H

Hadamard product . . . . . . . . . . . . . 224 hammingweight . . . . . . . . . . . . . 80, 127 hbessel1 . . . . . . . . . . . . . . . . . . 91 hbessel2 . . . . . . . . . . . . . . . . . . 91 hclassno . . . . . . . . . . . . . . . . . . 122 heap . . . . . . . . . . . . . . . . . . . . . 57 help . . . . . . . . . . . . . . . . . . . . . 307 Hermite normal form . 153, 176, 192, 237, 239 Hermite . . . . . . . . . . . . . . . . . . . 221 hess . . . . . . . . . . . . . . . . . . . . . 237 Hilbert class field . . . . . . . . . . . . . 125 Hilbert matrix . . . . . . . . . . . . . . . 237 Hilbert symbol . . . . . . . . . . . . 114, 192 hilbert . . . . . . . . . . . . . . . . . . . 114 histfile . . . . . . . . . . . . . . . . . . 307 history . . . . . . . . . . . . . . . . . . . . 46 histsize . . . . . . . . . . . . . . . . 16, 307 hnf . . . . . . . . . . . . . . . . . . . . . 239 hnfall . . . . . . . . . . . . . . . . . . . 239 hnfmod . . . . . . . . . . . . . . . . . . . 239 hnfmodid . . . . . . . . . . . . . . . . . . 239 Householder transform . . . . . . . . 239, 241 Hurwitz class number . . . . . . . . . . . 122 hyperu . . . . . . . . . . . . . . . . . . . 94

I

I . . . . . . . . . . . . . . . . . . . . . 19, 89 ibessel . . . . . . . . . . . . . . . . . . . 91 ibitand . . . . . . . . . . . . . . . . . . . 77 ibitnegimply . . . . . . . . . . . . . . . 77 ibitor . . . . . . . . . . . . . . . . . . . 77 ibitxor . . . . . . . . . . . . . . . . . . . 78 ideal (extended) . . . . . . . . 152, 177, 179 ideal list . . . . . . . . . . . . . . . . . . . 153 ideal . . . . . . . . . . . . . . . . . . . . . 152 idealadd . . . . . . . . . . . . . . . . . . 173 idealaddtoone . . . . . . . . . . . . . . . 173 idealaddtoone0 . . . . . . . . . . . . . . 174 idealappr . . . . . . . . . . . . . . . . . 174 idealappr0 . . . . . . . . . . . . . . . . . 174 idealchinese . . . . . . . . . . . . . . . 174 idealcoprime . . . . . . . . . . . . . . . 174 idealdiv . . . . . . . . . . . . . . . . . . 174 idealdiv0 . . . . . . . . . . . . . . . . . 174 idealdivexact . . . . . . . . . . . . . . . 174 idealfactor . . . . . . . . . . 165, 174, 182 idealfactorback . . . . . . . . . . 174, 175 idealfrobenius . . . . . . . . . . . . . . 175 idealhnf . . . . . . . . . . . . . . . 176, 177 idealhnf0 . . . . . . . . . . . . . . . . . 177 idealintersect . . . . . . . . . . . 177, 240 idealinv . . . . . . . . . . . . . . . 177, 193 ideallist . . . . . . . . . . . . . . 177, 178 ideallist0 . . . . . . . . . . . . . . . . . 178 ideallistarch . . . . . . . . . . . . . . . 178 ideallog . . . . . . . . . . 152, 178, 179, 183 idealmin . . . . . . . . . . . . . . . . . . 179 idealmul . . . . . . . . . . . . . . . . . . 179 idealmul0 . . . . . . . . . . . . . . . . . 179 idealmulred . . . . . . . . . . . . . . . . 179 idealnorm . . . . . . . . . . . . . . . . . 179 idealnumden . . . . . . . . . . . . . . . . 179 idealpow . . . . . . . . . . . . 179, 180, 182 idealpow0 . . . . . . . . . . . . . . . . . 180 idealpowred . . . . . . . . . . . . . . . . 180 idealpows . . . . . . . . . . . . . . . . . 180 idealprimedec . . . . . . . . . . . . 180, 190

idealprincipalunits . . . . . . . . 180, 181

idealramgroups . . . . . . . . . . . . . . 181 idealred . . . . . . . . . . . . 152, 175, 181 idealred0 . . . . . . . . . . . . . . . . . 182 idealstar . . . . . . . . . . . 165, 180, 182 Idealstar . . . . . . . . . . . . . . . . . 183 idealstar0 . . . . . . . . . . . . . . . . . 183 idealtwoelt . . . . . . . . . . . . . . . . 183 idealtwoelt0 . . . . . . . . . . . . . . . 183 idealtwoelt2 . . . . . . . . . . . . . . . 183 idealval . . . . . . . . . . . . . . . . . . 183 if . . . . . . . . . . . . . . . . . . . . . . 283 iferr . . . . . . . . 26, 49, 76, 279, 283, 299 imag . . . . . . . . . . . . . . . . . . . . . 80 image . . . . . . . . . . . . . . . . . . . . 239 imagecompl . . . . . . . . . . . . . . . . . 240 incgam . . . . . . . . . . . . . . . . . . . 94 incgam0 . . . . . . . . . . . . . . . . . . . 94 incgamc . . . . . . . . . . . . . . . . . . . 94 inclusive or . . . . . . . . . . . . . . . . . 70 index . . . . . . . . . . . . . . . . . . . . 155 index . . . . . . . . . . . . . . . . . . . . 155 indexrank . . . . . . . . . . . . . . . . . 240 infinite product . . . . . . . . . . . . . . . 267 infinite sum . . . . . . . . . . . . . . . . . 268 infinity . . . . . . . . . . . . . . . . . . . . 266 initzeta . . . . . . . . . . . . . . . . . . 215 inline . . . . . . . . . . . . . . . . . . . 292 input . . . . . . . . . . . . . . . . . . . . 292 install . . . . . . . . . . . . 46, 52, 292, 311 intcirc . . . . . . . . . . . . . . . . 257, 258 integ . . . . . . . . . . . . . . . . . . . . 218 integer . . . . . . . . . . . . . . . . . . 7, 8, 17 integral basis . . . . . . . . . . . . . . . . 185 integral pseudo-matrix . . . . . . . . . . . 153 internal longword format . . . . . . . . . 57 internal representation . . . . . . . . . . . 57 interpolating polynomial . . . . . . . . . . 221 intersect . . . . . . . . . . . . . . . . . 240 intformal . . . . . . . . . . . . . . . . . 218 intfouriercos . . . . . . . . . . . . . . . 258 intfourierexp . . . . . . . . . . . . . . . 258 intfouriersin . . . . . . . . . . . . . . . 258 intfuncinit . . . . . . . . . . . . . . . . 258 intlaplaceinv . . . . . . . . . . . . 258, 259 intmellininv . . . . . . . . . . . . 259, 260

intmellininvshort . . . . . . . . . . . . 260

intmod . . . . . . . . . . . . . . . . . . . . . 7 intmod . . . . . . . . . . . . . . . . . . . 8, 18 intnum . . . . . . . . . . . 257, 260, 265, 269 intnuminit . . . . . . . . . . . . . . 261, 265 intnuminitgen . . . . . . . . . . . . . . . 265 intnumromb . . . . . . . . . . . . . . 265, 266 intnumstep . . . . . . . . . . . . . . 261, 266 int_bit . . . . . . . . . . . . . . . . . . . 78 inverse . . . . . . . . . . . . . . . . . . . . 67 inverseimage . . . . . . . . . . . . . . . 240 isdiagonal . . . . . . . . . . . . . . . . . 240 isfundamental . . . . . . . . . . . . . . . 114 isideal . . . . . . . . . . . . . . . . . . . 195 isirreducible . . . . . . . . . . . . . . . 222 ispolygonal . . . . . . . . . . . . . . . . 114 ispower . . . . . . . . . . . . . . . . 114, 115 ispowerful . . . . . . . . . . . . . . . . . 115 isprime . . . . . . . . . . . . . . . . 115, 116 isprimepower . . . . . . . . . . . . . . . 116 isprincipalray . . . . . . . . . . . . . . 166 ispseudoprime . 110, 115, 116, 118, 120, 125 issquare . . . . . . . . . . . . . . . 116, 117 issquareall . . . . . . . . . . . . . . . . 117 issquarefree . . . . . . . . . . . . . 99, 117 istotient . . . . . . . . . . . . . . . . . 117

J

j . . . . . . . . . . . . . . . . . . . . . . . 130 jacobi . . . . . . . . . . . . . . . . . . . 248 jbessel . . . . . . . . . . . . . . . . . . . 91 jbesselh . . . . . . . . . . . . . . . . . . 92 jell . . . . . . . . . . . . . . . . . . . . . 142

K

kbessel . . . . . . . . . . . . . . . . . . . 92 ker . . . . . . . . . . . . . . . . . . . . . 241 keri . . . . . . . . . . . . . . . . . . . . . 241 kerint . . . . . . . . . . . . . . . . . . . 241 keyword . . . . . . . . . . . . . . . . . . . 45 kill . . . . . . . . . . . . . . . . . . . . . 294 kill0 . . . . . . . . . . . . . . . . . . . . 294 Kodaira . . . . . . . . . . . . . . . . . . . 142 Kronecker symbol . . . . . . . . . . . . . 117 kronecker . . . . . . . . . . . . . . . . . 117

L

lambertw . . . . . . . . . . . . . . . . . . 94 laplace . . . . . . . . . . . . . . . . . . . 224 lcm . . . . . . . . . . . . . . . . . . . . . 117 Leech lattice . . . . . . . . . . . . . . . . 250 Legendre polynomial . . . . . . . . . . . . 222 Legendre symbol . . . . . . . . . . . . . . 117 length . . . . . . . . . . . . . . . . . . . 80 Lenstra . . . . . . . . . . . . . . . . . . . 110 lex . . . . . . . . . . . . . . . . . . . . . 68 lexcmp . . . . . . . . . . . . . . . . . . . 69 lexical scoping . . . . . . . . . . . . . . . 34 libpari . . . . . . . . . . . . . . . . . . . . 5 LiDIA . . . . . . . . . . . . . . . . . . . . 110 lift . . . . . . . . . . . . . . . . . . . 78, 81 lift0 . . . . . . . . . . . . . . . . . . . . 81 liftall . . . . . . . . . . . . . . . . . 81, 82 liftint . . . . . . . . . . . . . . . . . 81, 82 liftpol . . . . . . . . . . . . . . . . . 81, 82 limit . . . . . . . . . . . . . . . . . . . . 42 lindep . . . . . . . . . . . . . 228, 231, 232 lindep0 . . . . . . . . . . . . . . . . . . . 232 lindep2 . . . . . . . . . . . . . . . . . . . 232 line editor . . . . . . . . . . . . . . . . . . 59 linear dependence . . . . . . . . . . . . . 231 lines . . . . . . . . . . . . . . . . . . . . 307 linewrap . . . . . . . . . . . . . . . . . . 307 Lisp . . . . . . . . . . . . . . . . . . . . . 53 list . . . . . . . . . . . . . . . . . . . . . 7, 26 List . . . . . . . . . . . . . . . . . . . . . 71 listcreate . . . . . . . . . . . . . . . 71, 232 listinsert . . . . . . . . . . . . . . . . . 232 listkill . . . . . . . . . . . . . . . . . . 232 listpop . . . . . . . . . . . . . . . . . . . 232 listput . . . . . . . . . . . . . . . . . . . 232 listsort . . . . . . . . . . . . . . . 233, 252 LLL . . . . . . . 127, 181, 191, 237, 241, 248 lll . . . . . . . . . . . . . . . . . . . . . 248 lllgram . . . . . . . . . . . . . . . . . . . 249 lllgramint . . . . . . . . . . . . . . . . . 249 lllgramkerim . . . . . . . . . . . . . . . 249 lllint . . . . . . . . . . . . . . . . . . . 248 lllkerim . . . . . . . . . . . . . . . . . . 248 lngamma . . . . . . . . . . . . . . . . . . . 94 local . . . . . . . . . . . . . . . . . . . . 34 log . . . . . . . . . . . . 55, 56, 95, 297, 307 logfile . . . . . . . . . . . . . . . . . . . . 297 logfile . . . . . . . . . . . . . . . . . . . 308 logint . . . . . . . . . . . . . . . . . . . 118 logint0 . . . . . . . . . . . . . . . . . . . 118 LONG_MAX . . . . . . . . . . . . . . 83, 87, 184 lvalue . . . . . . . . . . . . . . . . . . 27, 30 lvalue . . . . . . . . . . . . . . . . . . . 28

M

Mat . . . . . . . . . . . . 24, 25, 71, 230, 238 matadjoint . . . . . . . . . . . . . . 229, 233 matadjoint0 . . . . . . . . . . . . . . . . 233 matalgtobasis . . . . . . . . . . . . . . . 183 matbasistoalg . . . . . . . . . . . . . . . 183 matcompanion . . . . . . . . . . . . . . . 233 matconcat . . . . . . . . . . . 230, 233, 235 matdet . . . . . . . . . . . . . . . . . . . 235 matdetint . . . . . . . . . . . . . . . . . 235 matdiagonal . . . . . . . . . . . . . . . . 235 mateigen . . . . . . . . . . . . . . . . . . 236 matfrobenius . . . . . . . . . . . . . . . 237 Math::Pari . . . . . . . . . . . . . . . . . 53 mathell . . . . . . . . . . . . . . . . . . . 140 mathess . . . . . . . . . . . . . . . . . . . 237 mathilbert . . . . . . . . . . . . . . . . . 237 mathnf . . . . . . . . . . . . . . . . 228, 237 mathnf0 . . . . . . . . . . . . . . . . . . . 239 mathnfmod . . . . . . . . . . . . . . . . . 239 mathnfmodid . . . . . . . . . . . . . . . . 239 mathouseholder . . . . . . . . . . . . . . 239 matid . . . . . . . . . . . . . . . . . . . . 239 matimage . . . . . . . . . . . . . . . . . . 239 matimage0 . . . . . . . . . . . . . . . . . 239 matimagecompl . . . . . . . . . . . . . . . 240 matindexrank . . . . . . . . . . . . . . . 240 matintersect . . . . . . . . . . . . . . . 240 matinverseimage . . . . . . . . . . . . . 240 matisdiagonal . . . . . . . . . . . . . . . 240 matker . . . . . . . . . . . . . . . . . . . 240 matker0 . . . . . . . . . . . . . . . . . . . 241 matkerint . . . . . . . . . . . . . . . . . 241 matkerint0 . . . . . . . . . . . . . . . . . 241 matmuldiagonal . . . . . . . . . . . . . . 241

matmultodiagonal . . . . . . . . . . . . . 241

matpascal . . . . . . . . . . . . . . . . . 241 matqpascal . . . . . . . . . . . . . . . . . 241 matqr . . . . . . . . . . . . . . . . . . . . 241 matrank . . . . . . . . . . . . . . . . . . . 241 matrix . . . . . . . . . . . . . . . . 7, 8, 24, 46 matrix . . . . . . . . . . . . . . . . . 24, 242 matrixqz . . . . . . . . . . . . . . . . . . 242 matrixqz0 . . . . . . . . . . . . . . . . . 242 matsize . . . . . . . . . . . . . . . . . . . 242 matsnf . . . . . . . . . . . . . . . . 237, 242 matsnf0 . . . . . . . . . . . . . . . . . . . 243 matsolve . . . . . . . . . . . . . . . . . . 243 matsolvemod . . . . . . . . . . . . . . . . 243 matsolvemod0 . . . . . . . . . . . . . . . 243 matsupplement . . . . . . . . . . . . . . . 243 mattranspose . . . . . . . . . . . . . . . 244 max . . . . . . . . . . . . . . . . . . . . . 69 member functions . . . . . . . . 44, 130, 155 min . . . . . . . . . . . . . . . . . . . . . 69 minim . . . . . . . . . . . . . . . . . . . . 250 minim2 . . . . . . . . . . . . . . . . . . . 250 minimal model . . . . . . . . . . . . 137, 143 minimal polynomial . . . . . . . . . . . . 244 minimal vector . . . . . . . . . . . . . . . 250 minim_raw . . . . . . . . . . . . . . . . . 250 minpoly . . . . . . . . . . . . . . . . . . . 244 Mod . . . . . . . . . . . . . . . . . . . . . 72 mod . . . . . . . . . . . . . . . . . . . . . 155 modpr . . . . . . . . . . . . . . . . . . . . 195 modprinit . . . . . . . . . . . . . . . . . 190 modreverse . . . . . . . . . . . 183, 184, 201 modulus . . . . . . . . . . . . . . . . . . . 154 Moebius . . . . . . . . . . . . . . . . . 99, 118 moebius . . . . . . . . . . . . . . . . . 99, 118 Mordell-Weil group . . 137, 139, 140, 146, 147 mpcatalan . . . . . . . . . . . . . . . . . 89 mpeuler . . . . . . . . . . . . . . . . . . . 89 mpfact . . . . . . . . . . . . . . . . . . . 110 mpfactr . . . . . . . . . . . . . . . . . . . 110 mppi . . . . . . . . . . . . . . . . . . . . . 89 MPQS . . . . . . . . . . . . . . . . . . 99, 110 multivariate polynomial . . . . . . . . . . 41 my . . . . . . . . . . . . . . . . . . . . 34, 37

N

nbessel . . . . . . . . . . . . . . . . . . . 92 nbthreads . . . . . . . . . . . . . . . . . 308 newtonpoly . . . . . . . . . . . . . . . . . 184

new_galois_format . . . . . . . . . 199, 305

next . . . . . . . . . . . . . . . . . . . 49, 286 nextprime . . . . . . . . . . . . . . 118, 119 nf . . . . . . . . . . . . . . . . . . . . 44, 152 nf . . . . . . . . . . . . . . . . . . . . . . 155 nfadd . . . . . . . . . . . . . . . . . . . . 188 nfalgtobasis . . . . . . . . . . . . 183, 184 nfbasis . . . . . 185, 186, 187, 189, 194, 309 nfbasistoalg . . . . . . . . . . . . 183, 186 nfcertify . . . . . . . . . 186, 187, 194, 200 nfdetint . . . . . . . . . . . . . . . . . . 187 nfdisc . . . . . . . . . . . . . 187, 189, 309 nfdiv . . . . . . . . . . . . . . . . . . . . 188 nfdiveuc . . . . . . . . . . . . . . . . . . 188 nfdivmodpr . . . . . . . . . . . . . . . . . 188 nfdivrem . . . . . . . . . . . . . . . . . . 188 nfeltadd . . . . . . . . . . . . . . . . . . 188 nfeltdiv . . . . . . . . . . . . . . . . . . 188 nfeltdiveuc . . . . . . . . . . . . . . . . 188 nfeltdivmodpr . . . . . . . . . . . . . . . 188 nfeltdivrem . . . . . . . . . . . . . . . . 188 nfeltmod . . . . . . . . . . . . . . . . . . 188 nfeltmul . . . . . . . . . . . . . . . . . . 188 nfeltmulmodpr . . . . . . . . . . . . . . . 188 nfeltnorm . . . . . . . . . . . . . . . . . 188 nfeltpow . . . . . . . . . . . . . . . . . . 188 nfeltpowmodpr . . . . . . . . . . . . . . . 189 nfeltreduce . . . . . . . . . . . . . . . . 189

nfeltreducemodpr . . . . . . . . . . . . . 189

nfelttrace . . . . . . . . . . . . . . . . . 189 nfeltval . . . . . . . . . . . . . . . . . . 189 nffactor . . . . . . . 108, 168, 189, 190, 195 nffactorback . . . . . . . . . 152, 175, 190 nffactormod . . . . . . . . . . . . . . . . 190 nfgaloisapply . . . . . . . . . . . . . . . 190 nfgaloisconj . . . . . . . . . . . . 169, 191 nfhilbert . . . . . . . . . . . . . . . . . 192 nfhilbert0 . . . . . . . . . . . . . . . . . 192 nfhnf . . . . . . . . . . . . . . . . . 192, 240 nfhnfmod . . . . . . . . . . . . . . . . . . 192 nfinit 152, 170, 192, 194, 199, 200, 201, 309 nfinit0 . . . . . . . . . . . . . . . . . . . 194 nfinitall . . . . . . . . . . . . . . . . . 194 nfinitred . . . . . . . . . . . . . . . . . 194 nfinitred2 . . . . . . . . . . . . . . . . . 194 nfinv . . . . . . . . . . . . . . . . . . . . 189 nfisideal . . . . . . . . . . . . . . . . . 195 nfisincl . . . . . . . . . . . . . . . . . . 195 nfisisom . . . . . . . . . . . . . . . . . . 195 nfkermodpr . . . . . . . . . . . . . . . . . 195 nfmod . . . . . . . . . . . . . . . . . . . . 188 nfmodprinit . . . . . . . . . . 188, 189, 195 nfmul . . . . . . . . . . . . . . . . . . . . 188 nfmulmodpr . . . . . . . . . . . . . . . . . 188 nfnewprec . . . . . . . . . . . . . . 193, 195 nfnorm . . . . . . . . . . . . . . . . . . . 188 nfpow . . . . . . . . . . . . . . . . . . . . 189 nfpowmodpr . . . . . . . . . . . . . . . . . 189 nfreduce . . . . . . . . . . . . . . . . . . 189 nfreducemodpr . . . . . . . . . . . . . . . 189 nfroots . . . . . . . . . . . . . . . . 195, 196 nfrootsof1 . . . . . . . . . . . . . . . . . 196 nfrootsQ . . . . . . . . . . . . . . . . . . 196 nfsnf . . . . . . . . . . . . . . . . . . . . 196 nfsolvemodpr . . . . . . . . . . . . . . . 197 nfsqr . . . . . . . . . . . . . . . . . . . . 189 nfsubfield . . . . . . . . . . . . . . . . . 169 nfsubfields . . . . . . . . . . . . . . . . 197 nftrace . . . . . . . . . . . . . . . . . . . 189 nfval . . . . . . . . . . . . . . . . . . . . 189 nf_ADDZK . . . . . . . . . . . . . . . . . . 201 nf_ALL . . . . . . . . . . . . . . . . . . . 201 nf_FORCE . . . . . . . . . . . . . . . 159, 161 nf_GEN . . . . . . . . . . . . . . . . 161, 183 nf_INIT . . . . . . . . . . . . . . . . . . . 183 nf_ORIG . . . . . . . . . . . . . . . . 195, 201 nf_PARTIALFACT . . . . . . . . 195, 201, 309 nf_RAW . . . . . . . . . . . . . . . . . . . 201 nf_RED . . . . . . . . . . . . . . . . 194, 195 nf_ROUND2 . . . . . . . . . . . . . . . . . 195 no . . . . . . . . . . . . . . . . . . . 131, 155 norm . . . . . . . . . . . . . . . . . . . . . 82 norml2 . . . . . . . . . . . . . . . . . . . 244 normlp . . . . . . . . . . . . . . . . . . . 244 not . . . . . . . . . . . . . . . . . . . . . . 70 nucomp . . . . . . . . . . . . . . . . . . . 122 nudupl . . . . . . . . . . . . . . . . . . . 122 number field . . . . . . . . . . . . . . . . 20 numbpart . . . . . . . . . . . . . . . . . . 119 numdiv . . . . . . . . . . . . . . . . . . . 119 numer . . . . . . . . . . . . . . . . . . . . 82 numerator . . . . . . . . . . . . . . . 32, 82 numerical derivation . . . . . . . . . . . . 28 numerical integration . . . . . . . . . . . 257 numtoperm . . . . . . . . . . . . . . . . . 83 nupow . . . . . . . . . . . . . . . . . . . . 123

O

O . . . . . . . . . . . . . . . . . . . . . . . 215 omega . . . . . . . . . . . . . . . . . . . . 124 omega . . . . . . . . . . . . . . . . . 119, 131 oncurve . . . . . . . . . . . . . . . . . . . 141 operator . . . . . . . . . . . . . . . . . . . 27 or . . . . . . . . . . . . . . . . . . . . . . 70 or . . . . . . . . . . . . . . . . . . . . 77, 78 order . . . . . . . . . . . . . . . . . . . . 129 orderell . . . . . . . . . . . . . . . . . . 145 output . . . . . . . . . . . . . . . . . 56, 308

P

p . . . . . . . . . . . . . . . . . . . . . . . 131 p-adic number . . . . . . . . . . . . . 7, 8, 19 padicappr . . . . . . . . . . . . . . 218, 219 padicfields . . . . . . . . . . . . . . . . 219 padicfields0 . . . . . . . . . . . . . . . 219 padicprec . . . . . . . . . . . . . . . . . 83 padic_lindep . . . . . . . . . . . . . . . 232 parametric plot . . . . . . . . . . . . . . . 274 parapply . . . . . . . . . . . . . . . 302, 303 pareval . . . . . . . . . . . . . . . . . . . 303 parfor . . . . . . . . . . . . . . . . . . . 303 parforprime . . . . . . . . . . . . . . . . 303 PariEmacs . . . . . . . . . . . . . . . . . 315 parisize . . . . . . . . . . . . . . . . . . 308 pari_malloc . . . . . . . . . . . . . . . . 285 pari_realloc . . . . . . . . . . . . . . . 285 pari_version . . . . . . . . . . . . . . . 301 parselect . . . . . . . . . . . . . . . . . 303 parsum . . . . . . . . . . . . . . . . . . . 303 partitions . . . . . . . . . . . . . . 119, 120 parvector . . . . . . . . . . . . . . . . . 303 Pascal triangle . . . . . . . . . . . . . . . 241 path . . . . . . . . . . . . . . . . . . . . . 309 Pauli . . . . . . . . . . . . . . . . . . . . . 185 perf . . . . . . . . . . . . . . . . . . . . . 251 Perl . . . . . . . . . . . . . . . . . . . . . 53 Perl . . . . . . . . . . . . . . . . . . . . . 34 permtonum . . . . . . . . . . . . . . . . . 83 Pi . . . . . . . . . . . . . . . . . . . . . . 89 plot . . . . . . . . . . . . . . . . . . . . . 273 plotbox . . . . . . . . . . . . . . . . . . . 273 plotclip . . . . . . . . . . . . . . . . . . 273 plotcolor . . . . . . . . . . . . . . 273, 306 plotcopy . . . . . . . . . . . . . . . . . . 273 plotcursor . . . . . . . . . . . . . . . . . 273 plotdraw . . . . . . . . . . . . . . . . . . 273 ploth . . . . . . . . . . . . . . . . . . 64, 274 plothraw . . . . . . . . . . . . . . . . . . 275 plothsizes . . . . . . . . . . . . . . 275, 276 plotinit . . . . . . . . . . . . . . . . . . 275 plotkill . . . . . . . . . . . . . . . . . . 276 plotlines . . . . . . . . . . . . . . . . . 276 plotlinetype . . . . . . . . . . . . . . . 276 plotmove . . . . . . . . . . . . . . . . . . 276 plotpoints . . . . . . . . . . . . . . . . . 276 plotpointsize . . . . . . . . . . . . . . . 276 plotpointtype . . . . . . . . . . . . . . . 276 plotrbox . . . . . . . . . . . . . . . . . . 276 plotrecth . . . . . . . . . . . . . . 274, 277 plotrecthraw . . . . . . . . . . . . . . . 277 plotrline . . . . . . . . . . . . . . . . . 277 plotrmove . . . . . . . . . . . . . . . . . 277 plotrpoint . . . . . . . . . . . . . . . . . 277 plotscale . . . . . . . . . . . . . . 274, 277 plotstring . . . . . . . . . . . . . . . 46, 277 plotterm . . . . . . . . . . . . . . . . . . 46 pnqn . . . . . . . . . . . . . . . . . . . . . 104 pointell . . . . . . . . . . . . . . . . . . 150 pointer . . . . . . . . . . . . . . . . . . . . 64 Pol . . . . . . . . . . . . . . . . . 21, 73, 74 pol . . . . . . . . . . . . . . . . . . . . . 155 polchebyshev . . . . . . . . . . . . . . . 219 polchebyshev1 . . . . . . . . . . . . 219, 224 polchebyshev2 . . . . . . . . . . . . . . . 219

polchebyshev_eval . . . . . . . . . . . . 219

polcoeff . . . . . . . . . . . . . . . . 78, 219 polcoeff0 . . . . . . . . . . . . . . . . . 219 polcompositum . . . . . . . . . . . . . . . 197 polcompositum0 . . . . . . . . . . . . . . 198 polcyclo . . . . . . . . . . . . . . . 219, 220 polcyclofactors . . . . . . . . . . . . . 220 polcyclo_eval . . . . . . . . . . . . . . . 220 poldegree . . . . . . . . . . . . . . . . . 220 poldisc . . . . . . . . . . . . . . . . . . . 220 poldisc0 . . . . . . . . . . . . . . . . . . 220 poldiscreduced . . . . . . . . . . . . . . 220 polfnf . . . . . . . . . . . . . . . . . . . 168 polgalois . . . . . . . . . . . 198, 199, 308 polgraeffe . . . . . . . . . . . . . . . . . 220 polhensellift . . . . . . . . . . . . . . . 221 polhermite . . . . . . . . . . . . . . . . . 221 polhermite_eval . . . . . . . . . . . . . 221 polint . . . . . . . . . . . . . . . . . . . 221 polinterpolate . . . . . . . . . . . . . . 221 poliscyclo . . . . . . . . . . . . . . . . . 221 poliscycloprod . . . . . . . . . . . 221, 222

polisirreducible . . . . . . . . . . . . . 222

Pollard Rho . . . . . . . . . . . . . . . 99, 110 pollead . . . . . . . . . . . . . . . . . . . 222 pollegendre . . . . . . . . . . . . . . . . 222

pollegendre_eval . . . . . . . . . . . . . 222

polmod . . . . . . . . . . . . . . . . . . . . . 7 polmod . . . . . . . . . . . . . . . . . . 8, 20 polrecip . . . . . . . . . . . . . . . . . . 222 polred . . . . . . . . . . . . . . . . 199, 200 polred2 . . . . . . . . . . . . . . . . . . . 200 polredabs . . . . . . . . . . . . . . 200, 201 polredabs0 . . . . . . . . . . . . . . . . . 200 polredbest . . . . . . 194, 199, 200, 201, 202 polredord . . . . . . . . . . . . . . . . . 202 polresultant . . . . . . . . . . . . 113, 222 polresultant0 . . . . . . . . . . . . . . . 222 polresultantext . . . . . . . . . . 222, 223 polresultantext0 . . . . . . . . . . 215, 223 Polrev . . . . . . . . . . . . . . . 21, 73, 74 polroots . . . . . . . . . . . . . . . 223, 228 polrootsff . . . . . . . . . . . . . . . . . 120 polrootsmod . . . . . . . . . . . . . 127, 223 polrootspadic . . . . . . . . . . . . 127, 223 polsturm . . . . . . . . . . . . . . . . . . 223 polsubcyclo . . . . . . . . . . . . . . . . 224

                                           polsylvestermatrix . . . . . . . . . . . 224

polsym . . . . . . . . . . . . . . . . . . . 224 poltchebi . . . . . . . . . . . . . . . . . 224 poltschirnhaus . . . . . . . . . . . . . . 202 polylog . . . . . . . . . . . . . . . . . . . 95 polylog0 . . . . . . . . . . . . . . . . . . 96 polynomial . . . . . . . . . . . . . . . 7, 8, 21 polzag . . . . . . . . . . . . . . . . . . . 224 polzagier . . . . . . . . . . . . . . . . . 224 PostScript . . . . . . . . . . . . . . . . . 272 power series . . . . . . . . . . . . . . . 7, 8, 21 powering . . . . . . . . . . . . . . . . 66, 89 precision . . . . . . . . . . . . . . . . . . . 88 precision . . . . . . . . . . . . . . . 83, 84 precision0 . . . . . . . . . . . . . . . . . 84 precprime . . . . . . . . . . . . . . . . . 120 preferences file . . . . . . . . . . . 13, 53, 57 prettymatrix format . . . . . . . . . . . . 308 prettyprinter . . . . . . . . . . . . 308, 309 prid . . . . . . . . . . . . . . . . . . . 44, 180 prime . . . . . . . . . . . . . . . . . . . . 120 primeform . . . . . . . . . . . . . . . . . 123 primelimit . . . . . . . . . . . . . . 199, 309 primepi . . . . . . . . . . . . . . . . 120, 121 primes . . . . . . . . . . . . . . . . . . . 121 primes0 . . . . . . . . . . . . . . . . . . . 121 principal ideal . . . . . . . . . . . . 160, 182 print . . . . . . . . . . . . . . . . 45, 46, 294 print1 . . . . . . . . . . . . . . . . . . . 294 printf . . . . . . . . . . . . . 294, 297, 306 printsep . . . . . . . . . . . . . . . . . . 297 printsep1 . . . . . . . . . . . . . . . . . 297 printtex . . . . . . . . . . . . . . . . . . 297 priority . . . . . . . . . . . . . . . . . . . 27 prod . . . . . . . . . . . . . . . . . . . . . 266 prodeuler . . . . . . . . . . . . . . 266, 267 prodinf . . . . . . . . . . . . . . . . . . . 267 prodinf1 . . . . . . . . . . . . . . . . . . 267 product . . . . . . . . . . . . . . . . . . . 65 produit . . . . . . . . . . . . . . . . . . . 266 programming . . . . . . . . . . . . . . . . 278 projective module . . . . . . . . . . . . . . 153 prompt . . . . . . . . . . . . . . . . . . . 309 psdraw . . . . . . . . . . . . . . . . . . . 277 pseudo-basis . . . . . . . . . . . . . . . . . 153 pseudo-matrix . . . . . . . . . . . . . . . . 153 psfile . . . . . . . . . . . . . . . . 272, 310 psi . . . . . . . . . . . . . . . . . . . . . 96 psploth . . . . . . . . . . . . . . . . . . . 277 psplothraw . . . . . . . . . . . . . . . . . 278 Python . . . . . . . . . . . . . . . . . . . 53 p_to_GEN . . . . . . . . . . . . . . . . . . 111

Q

qfauto . . . . . . . . . . . . . . . . . . . 245 qfauto0 . . . . . . . . . . . . . . . . . . . 245 qfautoexport . . . . . . . . . . . . . . . 245 Qfb . . . . . . . . . . . . . . . . . . . . . 74 Qfb0 . . . . . . . . . . . . . . . . . . . . . 74 qfbclassno . . . . . . . . . . . . . . 121, 124 qfbclassno0 . . . . . . . . . . . . . . . . 122 qfbcompraw . . . . . . . . . . . . . . . . . 122 qfbhclassno . . . . . . . . . . . . . . . . 122 qfbil . . . . . . . . . . . . . . . . . 246, 250 qfbnucomp . . . . . . . . . . . . . . . . . 122 qfbnupow . . . . . . . . . . . . . . . . . . 123 qfbpowraw . . . . . . . . . . . . . . . . . 123 qfbprimeform . . . . . . . . . . . . . . . 123 qfbred . . . . . . . . . . . . . . . . . . . 123 qfbred0 . . . . . . . . . . . . . . . . . . . 123 qfbsolve . . . . . . . . . . . . . . . 123, 124 qfgaussred . . . . . . . . . . . . . . . . . 246

qfgaussred_positive . . . . . . . . . . . 246

qfi . . . . . . . . . . . . . . . . . . . . . 74 qfisom . . . . . . . . . . . . . . . . . . . 247 qfisom0 . . . . . . . . . . . . . . . . . . . 247 qfisominit . . . . . . . . . . . . . . . . . 247 qfisominit0 . . . . . . . . . . . . . . . . 247 qfjacobi . . . . . . . . . . . . . . . 236, 247 qflll . . . . . . . . . . . . . . . . . 228, 248 qflll0 . . . . . . . . . . . . . . . . . . . 248 qflllgram . . . . . . . . . . . . . . . . . 248 qflllgram0 . . . . . . . . . . . . . . . . . 249 qfminim . . . . . . . . . . . . . . . . 249, 251 qfminim0 . . . . . . . . . . . . . . . . . . 250 qfnorm . . . . . . . . . . . . . 246, 250, 251 qfperfection . . . . . . . . . . . . . . . 251 qfr . . . . . . . . . . . . . . . . . . . . . 74 qfrep . . . . . . . . . . . . . . . . . . . . 251 qfrep0 . . . . . . . . . . . . . . . . . . . 251 qfsign . . . . . . . . . . . . . . . . . . . 251 Qp_exp . . . . . . . . . . . . . . . . . . . 93 Qp_gamma . . . . . . . . . . . . . . . . . . 94 Qp_log . . . . . . . . . . . . . . . . . . . 95 Qp_sqrt . . . . . . . . . . . . . . . . . 96, 97 QR-decomposition . . . . . . . . . . . . . 241 Qt . . . . . . . . . . . . . . . . . . . . . . 271 quadclassunit . . . . . . . . . . . . . . . 124 quadclassunit0 . . . . . . . . . . . . . . 124 quaddisc . . . . . . . . . . . . . . . 105, 124 quadgen . . . . . . . . . . . . . . . . 124, 125 quadhilbert . . . . . . . . . . . . . . . . 125 quadpoly . . . . . . . . . . . . . . . . . . 125 quadpoly0 . . . . . . . . . . . . . . . . . 125 quadratic number . . . . . . . . . . . 7, 8, 20 quadray . . . . . . . . . . . . . . . . . . . 125 quadregula . . . . . . . . . . . . . . . . . 124 quadregulator . . . . . . . . . . . . . . . 125 quadunit . . . . . . . . . . . . . . . . . . 125 quit . . . . . . . . . . . . . . . . . . . 56, 297 quote . . . . . . . . . . . . . . . . . . . . 294 quotient . . . . . . . . . . . . . . . . . . . 65

R

r1 . . . . . . . . . . . . . . . . . . . . . . 155 r2 . . . . . . . . . . . . . . . . . . . . . . 155 ramification group . . . . . . . . . . . . . 181 random . . . . . . . . . . . . . . . . . 84, 292 randomprime . . . . . . . . . . . . . . . . 125 rank . . . . . . . . . . . . . . . . . . . . . 242 rational function . . . . . . . . . . . . . 7, 22 rational number . . . . . . . . . . . . 7, 8, 18 raw format . . . . . . . . . . . . . . . . . 308 read . . . . . . . . . . . . . . 46, 54, 297, 302 readline . . . . . . . . . . . . . . . . . . 310 readstr . . . . . . . . . . . . . . . . . . . 298 readvec . . . . . . . . . . . . . . . . . 46, 298 real number . . . . . . . . . . . . . . . 7, 8, 17 real . . . . . . . . . . . . . . . . . . . . . 85 realprecision . . . . . . . . . 17, 56, 88, 310 recover . . . . . . . . . . . . . . . . . . . 311 recursion depth . . . . . . . . . . . . . . . 42 recursion . . . . . . . . . . . . . . . . . . 41 recursive plot . . . . . . . . . . . . . . . . 274 redimag . . . . . . . . . . . . . . . . . . . 123 redreal . . . . . . . . . . . . . . . . . . . 123 redrealnod . . . . . . . . . . . . . . . . . 123

reduceddiscsmith . . . . . . . . . . . . . 220

reduction . . . . . . . . . . . . . . . 122, 123 reference card . . . . . . . . . . . . . . . . 55 reg . . . . . . . . . . . . . . . . . . . . . 155 removeprimes . . . . . . . . . . . . 125, 306 return . . . . . . . . . . . . . . . . . 49, 287 rhoreal . . . . . . . . . . . . . . . . . . . 123 rhorealnod . . . . . . . . . . . . . . . . . 123 Riemann zeta-function . . . . . . . . . 40, 98 rnf . . . . . . . . . . . . . . . . . . . . . . 153 rnfalgtobasis . . . . . . . . . . . . . . . 202 rnfbasis . . . . . . . . . . . . . . . . . . 202 rnfbasistoalg . . . . . . . . . . . . . . . 202 rnfcharpoly . . . . . . . . . . . . . . . . 202 rnfconductor . . . . . . . . . . . . . . . 203 rnfdedekind . . . . . . . . . . . . . 203, 204 rnfdet . . . . . . . . . . . . . . . . . . . 204 rnfdisc . . . . . . . . . . . . . . . . . . . 204 rnfdiscf . . . . . . . . . . . . . . . . . . 204 rnfeltabstorel . . . . . . . . . . . . . . 204 rnfeltdown . . . . . . . . . . . . . . 204, 205 rnfeltnorm . . . . . . . . . . . . . . . . . 205 rnfeltreltoabs . . . . . . . . . . . . . . 205 rnfelttrace . . . . . . . . . . . . . 205, 206 rnfeltup . . . . . . . . . . . . . . . . . . 206 rnfequation . . . . . . . . . . . . . 206, 207 rnfequation0 . . . . . . . . . . . . . . . 207 rnfequation2 . . . . . . . . . . . . . . . 207 rnfhnfbasis . . . . . . . . . . . . . . . . 207

rnfidealabstorel . . . . . . . . . . . . . 207

rnfidealdown . . . . . . . . . . . . . . . 207 rnfidealhnf . . . . . . . . . . . . . . . . 208 rnfidealmul . . . . . . . . . . . . . . . . 208 rnfidealnormabs . . . . . . . . . . . . . 208 rnfidealnormrel . . . . . . . . . . . . . 208

rnfidealreltoabs . . . . . . . . . . . . . 208
rnfidealtwoelement . . . . . . . . . . . 208

rnfidealtwoelt . . . . . . . . . . . . . . 208 rnfidealup . . . . . . . . . . . . . . 208, 209 rnfinit . . . . . . . . . . . . . . . . 209, 210 rnfisabelian . . . . . . . . . . . . . . . 210 rnfisfree . . . . . . . . . . . . . . . . . 210 rnfisnorm . . . . . . . . . . . . . . 210, 211 rnfisnorminit . . . . . . . . . . . . 210, 211 rnfkummer . . . . . . . . . . . 167, 211, 214 rnflllgram . . . . . . . . . . . . . . . . . 211 rnfnormgroup . . . . . . . . . . . . . . . 211 rnfpolred . . . . . . . . . . . . . . 211, 212 rnfpolredabs . . . . . . . . . . . . . . . 212 rnfpolredbest . . . . . . . . . 211, 212, 213 rnfpseudobasis . . . . . . . . . . . . . . 213

                                           rnfsimplifybasis . . . . . . . . . . . . . 192

rnfsteinitz . . . . . . . . . . . . . . . . 213 Roblot . . . . . . . . . . . . . . . . . . . . 185 rootmod0 . . . . . . . . . . . . . . . . . . 223 rootpadic . . . . . . . . . . . . . . . . . 223 roots . . . . . . . . . . . . 130, 131, 155, 223 rootsof1 . . . . . . . . . . . . . . . . . . 196 rootsof1_kannan . . . . . . . . . . . . . 196 round 4 . . . . . . . . . . . . . . . . 185, 218 round . . . . . . . . . . . . . . . . . . . . 85 round0 . . . . . . . . . . . . . . . . . . . 85 row vector . . . . . . . . . . . . . . . . 7, 22

S

scalar product . . . . . . . . . . . . . . . 65 scalar type . . . . . . . . . . . . . . . . . . 7 Schertz . . . . . . . . . . . . . . . . . . . 125 Schönage . . . . . . . . . . . . . . . . . . 223 scientific format . . . . . . . . . . . . . . 306 SEA . . . . . . . . . . . . . . . . . . . . . 134 seadata . . . . . . . . . . . . . . . . . . . 134 secure . . . . . . . . . . . . . . . . . . . 311 select . . . . . . . . . . . . . . . . . . . 298 Ser . . . . . . . . . . . . . . . . . 21, 74, 226 seralgdep . . . . . . . . . . . . . . . . . 251 serconvol . . . . . . . . . . . . . . . . . 224 seriesprecision . . . . . . 56, 89, 148, 311 serlaplace . . . . . . . . . . . . . . . . . 224 serreverse . . . . . . . . . . . . . . 224, 225 Set . . . . . . . . . . . . . . . . . . . . . 75 setbinop . . . . . . . . . . . . . . . 251, 252 setintersect . . . . . . . . . . . . . . . 252 setisset . . . . . . . . . . . . . . . . . . 252 setminus . . . . . . . . . . . . . . . . . . 252 setrand . . . . . . . . . . . . . . 84, 292, 299 setsearch . . . . . . . . . . . 233, 252, 253 setunion . . . . . . . . . . . . . . . . . . 253 Shanks SQUFOF . . . . . . . . . . . . 99, 110 Shanks . . . . . . . . . . . . 74, 121, 122, 123 shift . . . . . . . . . . . . . . . . . . . . 69 shiftmul . . . . . . . . . . . . . . . . . . 69 sigma . . . . . . . . . . . . . . . . . 105, 125 sign . . . . . . . . . . . . . . . . . . . . . 69 sign . . . . . . . . . . . . . . . . 69, 155, 255 signunits . . . . . . . . . . . . . . . . . 162 simplify . . . . . . . . . . . . 55, 85, 86, 311 sin . . . . . . . . . . . . . . . . . . . . . 96 sinh . . . . . . . . . . . . . . . . . . . . . 96 sizebyte . . . . . . . . . . . . . . . . . . 86 sizedigit . . . . . . . . . . . . . . . . . 86 Smith normal form …. 155, 159, 161, 183,

196, 242, 282

snbf . . . . . . . . . . . . . . . . . . . . . 157 solve . . . . . . . . . . . . . . . . . . . . 267 somme . . . . . . . . . . . . . . . . . . . . 267 sopath . . . . . . . . . . . . . . . . . . . 311 sqr . . . . . . . . . . . . . . . . . . . . . 96 sqrt . . . . . . . . . . . . . . . . . . . . . 96 sqrtint . . . . . . . . . . . . . . . . . . . 126 sqrtn . . . . . . . . . . . . . . . . . . . . 96 sqrtnint . . . . . . . . . . . . . . . . . . 126 stack . . . . . . . . . . . . . . . . 57, 308, 312 stacksize . . . . . . . . . . . . . . . . . 42 Stark units . . . . . . . . . . . . . . 125, 167 startup . . . . . . . . . . . . . . . . . . . 57 Steinitz class . . . . . . . . . . . . . . . . 213 Stirling number . . . . . . . . . . . . . . . 126 stirling . . . . . . . . . . . . . . . . . . 126 stirling1 . . . . . . . . . . . . . . . . . 126 stirling2 . . . . . . . . . . . . . . . . . 126 Str . . . . . . . . . . . . . . . . . 45, 46, 75 Strchr . . . . . . . . . . . . . . . . . 75, 76 Strexpand . . . . . . . . . . . . . . . . . 75 strftime . . . . . . . . . . . . . . . . 54, 309 strictargs . . . . . . . . . . . . . . . 39, 312 strictmatch . . . . . . . . . . . . . . . . 312 string context . . . . . . . . . . . . . . . . 45 string . . . . . . . . . . . . . . . . . 7, 26, 45 Strprintf . . . . . . . . . . . . . . 287, 306 Strtex . . . . . . . . . . . . . . . . . . . 76 strtoGEN . . . . . . . . . . . . . . . . . . 75 sturm . . . . . . . . . . . . . . . . . . . . 223 sturmpart . . . . . . . . . . . . . . . . . 223 subfield . . . . . . . . . . . . . . . . . . . 197 subgroup . . . . . . . . . . . . . . . . . . . 153 subgroup . . . . . . . . . . . . . . . . . . 282 subgrouplist . . . . . . . . . . . . 213, 282 subgrouplist0 . . . . . . . . . . . . . . . 214 subresultant algorithm . . . . . 113, 220, 222 subst . . . . . . . . . . . . . . . . . 225, 228 substpol . . . . . . . . . . . . . . . . . . 225 substvec . . . . . . . . . . . . . . . . . . 225 sum . . . . . . . . . . . . . . . . . . . . . 64 sum . . . . . . . . . . . . . . . . . . . . . 267 sumalt . . . . . . . . . . . 264, 267, 268, 271 sumalt2 . . . . . . . . . . . . . . . . . . . 268 sumdedekind . . . . . . . . . . . . . . . . 127 sumdigits . . . . . . . . . . . . . . . . . 127 sumdiv . . . . . . . . . . . . . . . . 126, 268 sumdivk . . . . . . . . . . . . . . . . . . . 126 sumdivmult . . . . . . . . . . . . . . . . . 268 sumformal . . . . . . . . . . . . . . . . . 226 suminf . . . . . . . . . . . . . 267, 268, 270 sumnum . . . . . . . . . . . . . . . . 268, 270 sumnumalt . . . . . . . . . . . . . . 270, 271 sumnuminit . . . . . . . . . . . . . . . . . 271 sumpos . . . . . . . . . . . . . 268, 270, 271 sumpos2 . . . . . . . . . . . . . . . . . . . 271 suppl . . . . . . . . . . . . . . . . . . . . 244 sylvestermatrix . . . . . . . . . . . . . 224 symmetric powers . . . . . . . . . . . . . 224 system . . . . . . . . . . . 46, 292, 299, 311

T

t2 . . . . . . . . . . . . . . . . . . . . . . 155 Tamagawa number . . . . . . . . . . 137, 142 tan . . . . . . . . . . . . . . . . . . . . . 97 tanh . . . . . . . . . . . . . . . . . . . . . 97 Taniyama-Shimura-Weil conjecture …. 133 tate . . . . . . . . . . . . . . . . . . . . . 131 tayl . . . . . . . . . . . . . . . . . . . . . 226 Taylor series . . . . . . . . . . . . . . . . 65 taylor . . . . . . . . . . . . . . . . . . . 226 teich . . . . . . . . . . . . . . . . . . . . 98 teichmuller . . . . . . . . . . . . . . . . 98 tex2mail . . . . . . . . . . . . . . . 308, 309 TeXstyle . . . . . . . . . . . . . . . 304, 308 theta . . . . . . . . . . . . . . . . . . . . 98 thetanullk . . . . . . . . . . . . . . . . . 98 threadsize . . . . . . . . . . . . . . . . . 312 thue . . . . . . . . . . . . . . . . . . 226, 227 thueinit . . . . . . . . . . . . 226, 227, 228 time expansion . . . . . . . . . . . . . . 54 timer . . . . . . . . . . . . . . . . . . . . 312 trace . . . . . . . . . . . . . . . . . . . . 253 Trager . . . . . . . . . . . . . . . . . . . . 168 trap . . . . . . . . . . . . . . . . . . . 46, 299 trap0 . . . . . . . . . . . . . . . . . . . . 300 trueeta . . . . . . . . . . . . . . . . . . . 93 trunc0 . . . . . . . . . . . . . . . . . . . 86 truncate . . . . . . . 81, 82, 83, 86, 218, 223 tschirnhaus . . . . . . . . . . . . . . . . 202 tu . . . . . . . . . . . . . . . . . . . . . . 155 tufu . . . . . . . . . . . . . . . . . . . . . 156 tutorial . . . . . . . . . . . . . . . . . . . 55 type . . . . . . . . . . . . . . . . . . . . . 300 type0 . . . . . . . . . . . . . . . . . . . . 300 t_CLOSURE . . . . . . . . . . . . . . . . 7, 26 t_COL . . . . . . . . . . . . . . . . . . . 7, 22 t_COMPLEX . . . . . . . . . . . . . . . . 7, 19 t_ERROR . . . . . . . . . . . . . . . . . . 7, 26 t_FFELT . . . . . . . . . . . . . . . . . . 7, 18 t_FRAC . . . . . . . . . . . . . . . . . . 7, 18 t_INT . . . . . . . . . . . . . . . . . . . 7, 17 t_INTMOD . . . . . . . . . . . . . . . . . 7, 18 t_LIST . . . . . . . . . . . . . . . . . . 7, 26 t_MAT . . . . . . . . . . . . . . . . . . . 7, 24 t_PADIC . . . . . . . . . . . . . . . . . . 7, 19 t_POL . . . . . . . . . . . . . . . . . . . 7, 21 t_POLMOD . . . . . . . . . . . . . . . . . 7, 20 t_QFI . . . . . . . . . . . . . . . . . . . 7, 22 t_QFR . . . . . . . . . . . . . . . . . . . 7, 22 t_QUAD . . . . . . . . . . . . . . . . . . 7, 20 t_REAL . . . . . . . . . . . . . . . . . . 7, 17 t_RFRAC . . . . . . . . . . . . . . . . . . 7, 22 t_SER . . . . . . . . . . . . . . . . . . . 7, 21 t_STR . . . . . . . . . . . . . . . . . . . 7, 26 t_VEC . . . . . . . . . . . . . . . . . . . 7, 22 t_VECSMALL . . . . . . . . . . . . . . . . 7, 26

U

ulimit . . . . . . . . . . . . . . . . . . . 42 uninline . . . . . . . . . . . . . . . . . . 300 until . . . . . . . . . . . . . . . . . . . . 287 user defined functions . . . . . . . . . . . 36

V

valuation . . . . . . . . . . . . . . . . . 86 van Hoeij . . . . . . . . . . . . . . . 107, 168 variable (priority) . . . . . . . . . . . 20, 32 variable scope . . . . . . . . . . . . . . . . 34 variable . . . . . . . . . . . . . . . . . 20, 30 variable . . . . . . . . . . . . . . . . 32, 87 Vec . . . . . . . . . . . . . . . 22, 23, 25, 76 vecbinome . . . . . . . . . . . . . . . . . 102 veceint1 . . . . . . . . . . . . . . . . . . 92 vecextract . . . . . . . . . . . . . . 240, 253 vecmax . . . . . . . . . . . . . . . . . 69, 70 vecmax0 . . . . . . . . . . . . . . . . . . . 70 vecmin . . . . . . . . . . . . . . . . . . . 70 vecmin0 . . . . . . . . . . . . . . . . . . . 70 Vecrev . . . . . . . . . . . . . . . . . 23, 76 vecsearch . . . . . . . . . . . . . . 254, 255 vecsmall . . . . . . . . . . . . . . . . . . . . 7 Vecsmall . . . . . . . . . . . . . . . . . . 76 vecsort . . . . . . . . . . . . . . . . 254, 255 vecsort0 . . . . . . . . . . . . . . . . . . 256 vecsum . . . . . . . . . . . . . . . . . . . 256 vecthetanullk . . . . . . . . . . . . . . . 98

                                           vecthetanullk_tau . . . . . . . . . . . . 98

vector . . . . . . . . . . . . . . . . . . . . . 8 vector . . . . . . . . . . . . . . . . . 23, 256 vectorsmall . . . . . . . . . . . . . . . . 256 vectorv . . . . . . . . . . . . . . . . . 23, 256 version number . . . . . . . . . . . . . . . 57 version . . . . . . . . . . . . . . . . . . . 300 Vi . . . . . . . . . . . . . . . . . . . . . . 60

W

warning . . . . . . . . . . . . . . . . . . . 301 weber . . . . . . . . . . . . . . . . . . . . 98 weber0 . . . . . . . . . . . . . . . . . . . 98 weberf . . . . . . . . . . . . . . . . . . . 98 weberf1 . . . . . . . . . . . . . . . . . . . 98 weberf2 . . . . . . . . . . . . . . . . . . . 98 Weierstrass ℘-function . . . . . . . . . . . 149 Weierstrass equation . . . . . . . . . . . . 130 Weil curve . . . . . . . . . . . . . . . . . 148 whatnow . . . . . . . . . . . . . . . . . 46, 301 while . . . . . . . . . . . . . . . . . . . . 287 write . . . . . . . . . . . 46, 54, 57, 301, 302 write1 . . . . . . . . . . . . . . . . . . . 302 writebin . . . . . . . . . . . . . . . . . . 302 writetex . . . . . . . . . . . . . . . . . . 302

X

Xadic_lindep . . . . . . . . . . . . . . . 232 x[,n] . . . . . . . . . . . . . . . . . . . . 78 x[m,n] . . . . . . . . . . . . . . . . . . . 78 x[m,] . . . . . . . . . . . . . . . . . . . . 78 x[n] . . . . . . . . . . . . . . . . . . . . . 78

Z

Zassenhaus . . . . . . . . . . . . . . 109, 218 zbrent . . . . . . . . . . . . . . . . . . . 267 zell . . . . . . . . . . . . . . . . . . . . . 146 zero . . . . . . . . . . . . . . . . . . . . . . 9 zeropadic . . . . . . . . . . . . . . . . . 215 zeroser . . . . . . . . . . . . . . . . . . . 215 zeta function . . . . . . . . . . . . . . . . 40 zeta . . . . . . . . . . . . . . . . . . . . . 98 zetak . . . . . . . . . . . . . . . . . . . . 214 zetakinit . . . . . . . . . . . . . . . . . 215 zk . . . . . . . . . . . . . . . . . . . . . . 155 zkst . . . . . . . . . . . . . . . . . . . . . 155 ZM_det . . . . . . . . . . . . . . . . . . . 235 ZM_gauss . . . . . . . . . . . . . . . . . . 243 zncoppersmith . . . . . . . . . . . . 127, 128 znlog . . . . . . . . . 111, 128, 129, 142, 179 znorder . . . . . . . . . . . . . . . . . . . 129 znprimroot . . . . . . . . . . . . . . . . . 129 znstar . . . . . . . . . . . . . . . . . . . 129 Zp_appr . . . . . . . . . . . . . . . . . . . 219