bogus::krylov::solvers::CGS< Matrix, Preconditioner, Traits > Struct Template Reference

Solves ( m_A * x = b ) using the Conjugate Gradient Squared algorithm. More...

#include <KrylovMethods.hpp>

Inheritance diagram for bogus::krylov::solvers::CGS< Matrix, Preconditioner, Traits >:

Public Types
typedef KrylovSolverBase < solvers::CGS, Matrix, Preconditioner, Traits >	Base
typedef Traits::Scalar	Scalar
typedef CGS< Matrix, Preconditioner, Traits >	Derived
typedef Signal< unsigned, Scalar >	SignalType
enum
typedef BlockTraits< typename LinearSolverTraits< CGS < Matrix, Preconditioner, Traits > >::MatrixType >	UnderlyingBlockTraits

Public Member Functions
	CGS (const Matrix &A, unsigned maxIters, Scalar tol=NumTraits< Scalar >::epsilon(), const Preconditioner *P=(static_cast< const Preconditioner * >(0)), const typename Base::SignalType *callback=(static_cast< const typename Base::SignalType * >(0)))
template<typename RhsT , typename ResT >
Scalar	vectorSolve (const RhsT &b, ResT x) const
LinearSolverTraits< Derived > ::template Result< RhsT > ::Type	solve (const RhsT &rhs) const
	Returns the solution x of the linear system M * x = rhs.
Scalar	solve (const RhsT &rhs, ResT &x) const
	Returns the solution x of the linear system M * x = rhs.
Derived &	parallelizeRhs (bool parallelize=true)
	Whether to process multiple rhs in parallel.
Derived &	enableResCaching (bool doCache=true)
	Whether to enable caching of solve(rhs) result for warmstarting purposes. More...
const CGS< Matrix, Preconditioner, Traits > &	derived () const
CGS< Matrix, Preconditioner, Traits > &	derived ()

Public Attributes
const Matrix *	m_A
const Preconditioner *	m_P
const SignalType *	m_callback
Scalar	m_tol
unsigned	m_maxIters

Protected Attributes
bool	m_parallelizeRhs
bool	m_enableResCaching

Detailed Description

template<typename Matrix, typename Preconditioner = TrivialPreconditioner< Matrix >, typename Traits = ProblemTraits< typename MatrixTraits<Matrix>::Scalar >>
struct bogus::krylov::solvers::CGS< Matrix, Preconditioner, Traits >

Solves ( m_A * x = b ) using the Conjugate Gradient Squared algorithm.

Works for non-symmetric linear systems. Convergence not guaranteed. Supposedly faster convergence than BiCG when converging.

Matrix-vector mults/iter: 2 Preconditionner calls/iter: 2 Storage requirements: 7n

Member Function Documentation

Derived& bogus::krylov::KrylovSolverBase< CGS , Matrix, Preconditioner, Traits >::enableResCaching ( bool  doCache = true )

inherited

Whether to enable caching of solve(rhs) result for warmstarting purposes.

Warning

Not thread safe for parallel calls to solve(rhs)

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The documentation for this struct was generated from the following file:

• KrylovMethods.hpp