A conjugate gradient solver for sparse (or dense) least-square problems. More...

#include <LeastSquareConjugateGradient.h>

Inheritance diagram for Eigen::LeastSquaresConjugateGradient< _MatrixType, _Preconditioner >:

Public Types
typedef _MatrixType	MatrixType

typedef MatrixType::Scalar	Scalar

typedef MatrixType::RealScalar	RealScalar

typedef _Preconditioner	Preconditioner

Public Types inherited from Eigen::IterativeSolverBase< LeastSquaresConjugateGradient< _MatrixType, _Preconditioner > >
enum

typedef internal::traits< LeastSquaresConjugateGradient< _MatrixType, _Preconditioner > >::MatrixType	MatrixType

typedef internal::traits< LeastSquaresConjugateGradient< _MatrixType, _Preconditioner > >::Preconditioner	Preconditioner

typedef MatrixType::Scalar	Scalar

typedef MatrixType::StorageIndex	StorageIndex

typedef MatrixType::RealScalar	RealScalar

Public Member Functions
	LeastSquaresConjugateGradient ()
	Default constructor.

template<typename MatrixDerived >
	LeastSquaresConjugateGradient (const EigenBase< MatrixDerived > &A)
	Initialize the solver with matrix A for further `Ax=b` solving.

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

Public Member Functions inherited from Eigen::IterativeSolverBase< LeastSquaresConjugateGradient< _MatrixType, _Preconditioner > >
	IterativeSolverBase ()
	Default constructor.

	IterativeSolverBase (const EigenBase< MatrixDerived > &A)
	Initialize the solver with matrix A for further `Ax=b` solving.

LeastSquaresConjugateGradient< _MatrixType, _Preconditioner > &	analyzePattern (const EigenBase< MatrixDerived > &A)
	Initializes the iterative solver for the sparsity pattern of the matrix A for further solving `Ax=b` problems.

LeastSquaresConjugateGradient< _MatrixType, _Preconditioner > &	factorize (const EigenBase< MatrixDerived > &A)
	Initializes the iterative solver with the numerical values of the matrix A for further solving `Ax=b` problems.

LeastSquaresConjugateGradient< _MatrixType, _Preconditioner > &	compute (const EigenBase< MatrixDerived > &A)
	Initializes the iterative solver with the matrix A for further solving `Ax=b` problems.

EIGEN_CONSTEXPR Index	rows () const EIGEN_NOEXCEPT

EIGEN_CONSTEXPR Index	cols () const EIGEN_NOEXCEPT

RealScalar	tolerance () const

LeastSquaresConjugateGradient< _MatrixType, _Preconditioner > &	setTolerance (const RealScalar &tolerance)
	Sets the tolerance threshold used by the stopping criteria.

Preconditioner &	preconditioner ()

const Preconditioner &	preconditioner () const

Index	maxIterations () const

LeastSquaresConjugateGradient< _MatrixType, _Preconditioner > &	setMaxIterations (Index maxIters)
	Sets the max number of iterations.

Index	iterations () const

RealScalar	error () const

const SolveWithGuess< LeastSquaresConjugateGradient< _MatrixType, _Preconditioner >, Rhs, Guess >	solveWithGuess (const MatrixBase< Rhs > &b, const Guess &x0) const

ComputationInfo	info () const

void	_solve_with_guess_impl (const Rhs &b, SparseMatrixBase< DestDerived > &aDest) const

internal::enable_if< Rhs::ColsAtCompileTime!=1 &&DestDerived::ColsAtCompileTime!=1 >::type	_solve_with_guess_impl (const Rhs &b, MatrixBase< DestDerived > &aDest) const

internal::enable_if< Rhs::ColsAtCompileTime==1\|\|DestDerived::ColsAtCompileTime==1 >::type	_solve_with_guess_impl (const Rhs &b, MatrixBase< DestDerived > &dest) const

void	_solve_impl (const Rhs &b, Dest &x) const

LeastSquaresConjugateGradient< _MatrixType, _Preconditioner > &	derived ()

const LeastSquaresConjugateGradient< _MatrixType, _Preconditioner > &	derived () const

Public Member Functions inherited from Eigen::SparseSolverBase< Derived >
	SparseSolverBase ()
	Default constructor.

Derived &	derived ()

const Derived &	derived () const

template<typename Rhs >
const Solve< Derived, Rhs >	solve (const MatrixBase< Rhs > &b) const

template<typename Rhs >
const Solve< Derived, Rhs >	solve (const SparseMatrixBase< Rhs > &b) const

template<typename Rhs , typename Dest >
void	_solve_impl (const SparseMatrixBase< Rhs > &b, SparseMatrixBase< Dest > &dest) const

Additional Inherited Members
Protected Types inherited from Eigen::IterativeSolverBase< LeastSquaresConjugateGradient< _MatrixType, _Preconditioner > >
typedef SparseSolverBase< LeastSquaresConjugateGradient< _MatrixType, _Preconditioner > >	Base

typedef internal::generic_matrix_wrapper< MatrixType >	MatrixWrapper

typedef MatrixWrapper::ActualMatrixType	ActualMatrixType

Protected Member Functions inherited from Eigen::IterativeSolverBase< LeastSquaresConjugateGradient< _MatrixType, _Preconditioner > >
void	init ()

const ActualMatrixType &	matrix () const

void	grab (const InputType &A)

Protected Attributes inherited from Eigen::IterativeSolverBase< LeastSquaresConjugateGradient< _MatrixType, _Preconditioner > >
MatrixWrapper	m_matrixWrapper

Preconditioner	m_preconditioner

Index	m_maxIterations

RealScalar	m_tolerance

RealScalar	m_error

Index	m_iterations

ComputationInfo	m_info

bool	m_analysisIsOk

bool	m_factorizationIsOk

bool	m_isInitialized

Protected Attributes inherited from Eigen::SparseSolverBase< Derived >
bool	m_isInitialized

Detailed Description

template<typename _MatrixType, typename _Preconditioner>
class Eigen::LeastSquaresConjugateGradient< _MatrixType, _Preconditioner >

A conjugate gradient solver for sparse (or dense) least-square problems.

This class allows to solve for A x = b linear problems using an iterative conjugate gradient algorithm. The matrix A can be non symmetric and rectangular, but the matrix A' A should be positive-definite to guaranty stability. Otherwise, the SparseLU or SparseQR classes might be preferable. The matrix A and the vectors x and b can be either dense or sparse.

Template Parameters

_MatrixType	the type of the matrix A, can be a dense or a sparse matrix.
_Preconditioner	the type of the preconditioner. Default is LeastSquareDiagonalPreconditioner

\implsparsesolverconcept

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 m=1000000, n = 10000;
VectorXd x(n), b(m);
SparseMatrix<double> A(m,n);
// fill A and b
LeastSquaresConjugateGradient<SparseMatrix<double> > lscg;
lscg.compute(A);
x = lscg.solve(b);
std::cout << "#iterations:     " << lscg.iterations() << std::endl;
std::cout << "estimated error: " << lscg.error()      << std::endl;
// update b, and solve again
x = lscg.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.

See also: class ConjugateGradient, SparseLU, SparseQR

Constructor & Destructor Documentation

◆ LeastSquaresConjugateGradient()

template<typename _MatrixType , typename _Preconditioner >

template<typename MatrixDerived >

Eigen::LeastSquaresConjugateGradient< _MatrixType, _Preconditioner >::LeastSquaresConjugateGradient ( const EigenBase< MatrixDerived > & A )

inlineexplicit

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.

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

External/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h

Public Types

Public Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ LeastSquaresConjugateGradient()