A conjugate gradient solver for sparse self-adjoint problems. More...

#include <ConjugateGradient.h>

Inheritance diagram for Eigen::ConjugateGradient< _MatrixType, _UpLo, _Preconditioner >:

Collaboration diagram for Eigen::ConjugateGradient< _MatrixType, _UpLo, _Preconditioner >:

Public Types
enum	{ UpLo = _UpLo }

typedef _MatrixType	MatrixType

typedef MatrixType::Scalar	Scalar

typedef MatrixType::Index	Index

typedef MatrixType::RealScalar	RealScalar

typedef _Preconditioner	Preconditioner

Public Types inherited from Eigen::IterativeSolverBase< ConjugateGradient< _MatrixType, _UpLo, _Preconditioner > >
typedef internal::traits< ConjugateGradient< _MatrixType, _UpLo, _Preconditioner > >::MatrixType	MatrixType

typedef internal::traits< ConjugateGradient< _MatrixType, _UpLo, _Preconditioner > >::Preconditioner	Preconditioner

typedef MatrixType::Scalar	Scalar

typedef MatrixType::Index	Index

typedef MatrixType::RealScalar	RealScalar

Public Member Functions
	ConjugateGradient ()

	ConjugateGradient (const MatrixType &A)

template<typename Rhs , typename Guess >
const internal::solve_retval_with_guess< ConjugateGradient, Rhs, Guess >	solveWithGuess (const MatrixBase< Rhs > &b, const Guess &x0) const

template<typename Rhs , typename Dest >
void	_solveWithGuess (const Rhs &b, Dest &x) const

template<typename Rhs , typename Dest >
void	_solve (const Rhs &b, Dest &x) const

Public Member Functions inherited from Eigen::IterativeSolverBase< ConjugateGradient< _MatrixType, _UpLo, _Preconditioner > >
ConjugateGradient< _MatrixType, _UpLo, _Preconditioner > &	derived ()

const ConjugateGradient< _MatrixType, _UpLo, _Preconditioner > &	derived () const

	IterativeSolverBase ()

	IterativeSolverBase (const MatrixType &A)

ConjugateGradient< _MatrixType, _UpLo, _Preconditioner > &	analyzePattern (const MatrixType &A)

ConjugateGradient< _MatrixType, _UpLo, _Preconditioner > &	factorize (const MatrixType &A)

ConjugateGradient< _MatrixType, _UpLo, _Preconditioner > &	compute (const MatrixType &A)

Index	rows () const

Index	cols () const

RealScalar	tolerance () const

ConjugateGradient< _MatrixType, _UpLo, _Preconditioner > &	setTolerance (const RealScalar &tolerance)

Preconditioner &	preconditioner ()

const Preconditioner &	preconditioner () const

int	maxIterations () const

ConjugateGradient< _MatrixType, _UpLo, _Preconditioner > &	setMaxIterations (int maxIters)

int	iterations () const

RealScalar	error () const

const internal::solve_retval< ConjugateGradient< _MatrixType, _UpLo, _Preconditioner >, Rhs >	solve (const MatrixBase< Rhs > &b) const

const internal::sparse_solve_retval< IterativeSolverBase, Rhs >	solve (const SparseMatrixBase< Rhs > &b) const

ComputationInfo	info () const

void	_solve_sparse (const Rhs &b, SparseMatrix< DestScalar, DestOptions, DestIndex > &dest) const

Additional Inherited Members
Protected Member Functions inherited from Eigen::IterativeSolverBase< ConjugateGradient< _MatrixType, _UpLo, _Preconditioner > >
void	init ()

Protected Attributes inherited from Eigen::IterativeSolverBase< ConjugateGradient< _MatrixType, _UpLo, _Preconditioner > >
const MatrixType *	mp_matrix

Preconditioner	m_preconditioner

int	m_maxIterations

RealScalar	m_tolerance

RealScalar	m_error

int	m_iterations

ComputationInfo	m_info

bool	m_isInitialized

bool	m_analysisIsOk

bool	m_factorizationIsOk

Detailed Description

template<typename _MatrixType, int _UpLo, typename _Preconditioner>
class Eigen::ConjugateGradient< _MatrixType, _UpLo, _Preconditioner >

A conjugate gradient solver for sparse self-adjoint problems.

This class allows to solve for A.x = b sparse linear problems using a conjugate gradient algorithm. The sparse matrix A must be selfadjoint. The vectors x and b can be either dense or sparse.

Template Parameters

_MatrixType	the type of the sparse matrix A, can be a dense or a sparse matrix.
_UpLo	the triangular part that will be used for the computations. It can be Lower or Upper. Default is Lower.
_Preconditioner	the type of the preconditioner. Default is DiagonalPreconditioner

The maximal number of iterations and tolerance value can be controlled via the setMaxIterations() and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations and NumTraits<Scalar>::epsilon() for the tolerance.

This class can be used as the direct solver classes. Here is a typical usage example:

int n = 10000;
VectorXd x(n), b(n);
SparseMatrix<double> A(n,n);
// fill A and b
ConjugateGradient<SparseMatrix<double> > cg;
cg.compute(A);
x = cg.solve(b);
std::cout << "#iterations:     " << cg.iterations() << std::endl;
std::cout << "estimated error: " << cg.error()      << std::endl;
// update b, and solve again
x = cg.solve(b);

By default the iterations start with x=0 as an initial guess of the solution. One can control the start using the solveWithGuess() method. Here is a step by step execution example starting with a random guess and printing the evolution of the estimated error:

x = VectorXd::Random(n);
cg.setMaxIterations(1);
int i = 0;
do {
x = cg.solveWithGuess(b,x);
std::cout << i << " : " << cg.error() << std::endl;
++i;
} while (cg.info()!=Success && i<100);
Note that such a step by step excution is slightly slower.

See also: class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner

Definition at line 96 of file ConjugateGradient.h.

Constructor & Destructor Documentation

template<typename _MatrixType , int _UpLo, typename _Preconditioner >

Eigen::ConjugateGradient< _MatrixType, _UpLo, _Preconditioner >::ConjugateGradient ( )

inline

Default constructor.

Definition at line 180 of file ConjugateGradient.h.

180 : Base() {}

template<typename _MatrixType , int _UpLo, typename _Preconditioner >

Eigen::ConjugateGradient< _MatrixType, _UpLo, _Preconditioner >::ConjugateGradient ( const MatrixType & A )

inline

Initialize the solver with matrix A for further Ax=b solving.

This constructor is a shortcut for the default constructor followed by a call to compute().

Warning: this class stores a reference to the matrix A as well as some precomputed values that depend on it. Therefore, if A is changed this class becomes invalid. Call compute() to update it with the new matrix A, or modify a copy of A.

Definition at line 192 of file ConjugateGradient.h.

192 : Base(A) {}

Member Function Documentation

template<typename _MatrixType , int _UpLo, typename _Preconditioner >

template<typename Rhs , typename Guess >

const internal::solve_retval_with_guess<ConjugateGradient, Rhs, Guess> Eigen::ConjugateGradient< _MatrixType, _UpLo, _Preconditioner >::solveWithGuess	(	const MatrixBase< Rhs > &	b,
		const Guess &	x0
	)		const

inline

Returns: the solution x of using the current decomposition of A x0 as an initial solution.

See also: compute()

Definition at line 203 of file ConjugateGradient.h.

   {
     eigen_assert(m_isInitialized && "ConjugateGradient is not initialized.");
     eigen_assert(Base::rows()==b.rows()
               && "ConjugateGradient::solve(): invalid number of rows of the right hand side matrix b");
     return internal::solve_retval_with_guess
             <ConjugateGradient, Rhs, Guess>(*this, b.derived(), x0);
   }

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

C:/Users/Brig/Documents/ShapeWorksStudio/src/Surfworks/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h

Public Types

Public Member Functions

Additional Inherited Members

Detailed Description

template<typename _MatrixType, int _UpLo, typename _Preconditioner> class Eigen::ConjugateGradient< _MatrixType, _UpLo, _Preconditioner >

Constructor & Destructor Documentation

Member Function Documentation

template<typename _MatrixType, int _UpLo, typename _Preconditioner>
class Eigen::ConjugateGradient< _MatrixType, _UpLo, _Preconditioner >