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#ifndef ROCALUTION_KRYLOV_CG_HPP_
#define ROCALUTION_KRYLOV_CG_HPP_

#include "../solver.hpp"

#include <vector>

namespace rocalution
{

    /** \ingroup solver_module
  * \class CG
  * \brief Conjugate Gradient Method
  * \details
  * The Conjugate Gradient method is the best known iterative method for solving sparse
  * symmetric positive definite (SPD) linear systems \f$Ax=b\f$. It is based on
  * orthogonal projection onto the Krylov subspace \f$\mathcal{K}_{m}(r_{0}, A)\f$,
  * where \f$r_{0}\f$ is the initial residual. The method can be preconditioned, where
  * the approximation should also be SPD.
  * \cite SAAD
  *
  * \tparam OperatorType - can be LocalMatrix, GlobalMatrix or LocalStencil
  * \tparam VectorType - can be LocalVector or GlobalVector
  * \tparam ValueType - can be float, double, std::complex<float> or std::complex<double>
  */
    template <class OperatorType, class VectorType, typename ValueType>
    class CG : public IterativeLinearSolver<OperatorType, VectorType, ValueType>
    {
    public:
        CG();
        virtual ~CG();

        virtual void Print(void) const;

        virtual void Build(void);

        virtual void BuildMoveToAcceleratorAsync(void);
        virtual void Sync(void);

        virtual void ReBuildNumeric(void);
        virtual void Clear(void);

    protected:
        virtual void SolveNonPrecond_(const VectorType& rhs, VectorType* x);
        virtual void SolvePrecond_(const VectorType& rhs, VectorType* x);

        virtual void PrintStart_(void) const;
        virtual void PrintEnd_(void) const;

        virtual void MoveToHostLocalData_(void);
        virtual void MoveToAcceleratorLocalData_(void);

    private:
        VectorType r_, z_;
        VectorType p_, q_;
    };

} // namespace rocalution

#endif // ROCALUTION_KRYLOV_CG_HPP_