A conjugate gradient solver for sparse (or dense) least-square problems. More...
#include <LeastSquareConjugateGradient.h>
Public Types | |
typedef _MatrixType | MatrixType |
typedef _Preconditioner | Preconditioner |
typedef MatrixType::RealScalar | RealScalar |
typedef MatrixType::Scalar | Scalar |
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::RealScalar | RealScalar |
typedef MatrixType::Scalar | Scalar |
typedef MatrixType::StorageIndex | StorageIndex |
Public Member Functions | |
template<typename Rhs , typename Dest > | |
void | _solve_vector_with_guess_impl (const Rhs &b, Dest &x) const |
LeastSquaresConjugateGradient () | |
template<typename MatrixDerived > | |
LeastSquaresConjugateGradient (const EigenBase< MatrixDerived > &A) | |
~LeastSquaresConjugateGradient () | |
Public Member Functions inherited from Eigen::IterativeSolverBase< LeastSquaresConjugateGradient< _MatrixType, _Preconditioner > > | |
void | _solve_impl (const Rhs &b, Dest &x) 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 |
LeastSquaresConjugateGradient< _MatrixType, _Preconditioner > & | analyzePattern (const EigenBase< MatrixDerived > &A) |
EIGEN_CONSTEXPR Index | cols () const EIGEN_NOEXCEPT |
LeastSquaresConjugateGradient< _MatrixType, _Preconditioner > & | compute (const EigenBase< MatrixDerived > &A) |
RealScalar | error () const |
LeastSquaresConjugateGradient< _MatrixType, _Preconditioner > & | factorize (const EigenBase< MatrixDerived > &A) |
ComputationInfo | info () const |
Index | iterations () const |
IterativeSolverBase () | |
IterativeSolverBase (const EigenBase< MatrixDerived > &A) | |
Index | maxIterations () const |
Preconditioner & | preconditioner () |
const Preconditioner & | preconditioner () const |
EIGEN_CONSTEXPR Index | rows () const EIGEN_NOEXCEPT |
LeastSquaresConjugateGradient< _MatrixType, _Preconditioner > & | setMaxIterations (Index maxIters) |
LeastSquaresConjugateGradient< _MatrixType, _Preconditioner > & | setTolerance (const RealScalar &tolerance) |
const SolveWithGuess< LeastSquaresConjugateGradient< _MatrixType, _Preconditioner >, Rhs, Guess > | solveWithGuess (const MatrixBase< Rhs > &b, const Guess &x0) const |
RealScalar | tolerance () const |
~IterativeSolverBase () | |
Public Member Functions inherited from Eigen::SparseSolverBase< LeastSquaresConjugateGradient< _MatrixType, _Preconditioner > > | |
void | _solve_impl (const SparseMatrixBase< Rhs > &b, SparseMatrixBase< Dest > &dest) const |
LeastSquaresConjugateGradient< _MatrixType, _Preconditioner > & | derived () |
const LeastSquaresConjugateGradient< _MatrixType, _Preconditioner > & | derived () const |
const Solve< LeastSquaresConjugateGradient< _MatrixType, _Preconditioner >, Rhs > | solve (const MatrixBase< Rhs > &b) const |
const Solve< LeastSquaresConjugateGradient< _MatrixType, _Preconditioner >, Rhs > | solve (const SparseMatrixBase< Rhs > &b) const |
SparseSolverBase () | |
~SparseSolverBase () | |
Private Types | |
typedef IterativeSolverBase< LeastSquaresConjugateGradient > | Base |
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.
_MatrixType | the type of the matrix A, can be a dense or a sparse matrix. |
_Preconditioner | the type of the preconditioner. Default is LeastSquareDiagonalPreconditioner |
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:
By default the iterations start with x=0 as an initial guess of the solution. One can control the start using the solveWithGuess() method.
Definition at line 98 of file LeastSquareConjugateGradient.h.
|
private |
Definition at line 151 of file LeastSquareConjugateGradient.h.
typedef _MatrixType Eigen::LeastSquaresConjugateGradient< _MatrixType, _Preconditioner >::MatrixType |
Definition at line 158 of file LeastSquareConjugateGradient.h.
typedef _Preconditioner Eigen::LeastSquaresConjugateGradient< _MatrixType, _Preconditioner >::Preconditioner |
Definition at line 161 of file LeastSquareConjugateGradient.h.
typedef MatrixType::RealScalar Eigen::LeastSquaresConjugateGradient< _MatrixType, _Preconditioner >::RealScalar |
Definition at line 160 of file LeastSquareConjugateGradient.h.
typedef MatrixType::Scalar Eigen::LeastSquaresConjugateGradient< _MatrixType, _Preconditioner >::Scalar |
Definition at line 159 of file LeastSquareConjugateGradient.h.
|
inline |
Default constructor.
Definition at line 166 of file LeastSquareConjugateGradient.h.
|
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().
Definition at line 179 of file LeastSquareConjugateGradient.h.
|
inline |
Definition at line 181 of file LeastSquareConjugateGradient.h.
|
inline |
Definition at line 185 of file LeastSquareConjugateGradient.h.