Classes | Functions
IterativeLinearSolvers_Module
Collaboration diagram for IterativeLinearSolvers_Module:

Classes

class  Eigen::BiCGSTAB< _MatrixType, _Preconditioner >
 A bi conjugate gradient stabilized solver for sparse square problems. More...
 
class  Eigen::ConjugateGradient< _MatrixType, _UpLo, _Preconditioner >
 A conjugate gradient solver for sparse (or dense) self-adjoint problems. More...
 
class  Eigen::DGMRES< _MatrixType, _Preconditioner >
 A Restarted GMRES with deflation. This class implements a modification of the GMRES solver for sparse linear systems. The basis is built with modified Gram-Schmidt. At each restart, a few approximated eigenvectors corresponding to the smallest eigenvalues are used to build a preconditioner for the next cycle. This preconditioner for deflation can be combined with any other preconditioner, the IncompleteLUT for instance. The preconditioner is applied at right of the matrix and the combination is multiplicative. More...
 
class  Eigen::DiagonalPreconditioner< _Scalar >
 A preconditioner based on the digonal entries. More...
 
class  Eigen::GMRES< _MatrixType, _Preconditioner >
 A GMRES solver for sparse square problems. More...
 
class  Eigen::IdentityPreconditioner
 A naive preconditioner which approximates any matrix as the identity matrix. More...
 
class  Eigen::IDRS< _MatrixType, _Preconditioner >
 The Induced Dimension Reduction method (IDR(s)) is a short-recurrences Krylov method for sparse square problems. More...
 
class  Eigen::IncompleteLUT< _Scalar, _StorageIndex >
 Incomplete LU factorization with dual-threshold strategy. More...
 
class  Eigen::IterationController
 Controls the iterations of the iterative solvers. More...
 
class  Eigen::IterativeSolverBase< Derived >
 Base class for linear iterative solvers. More...
 
class  Eigen::LeastSquareDiagonalPreconditioner< _Scalar >
 Jacobi preconditioner for LeastSquaresConjugateGradient. More...
 
class  Eigen::LeastSquaresConjugateGradient< _MatrixType, _Preconditioner >
 A conjugate gradient solver for sparse (or dense) least-square problems. More...
 
class  Eigen::MINRES< _MatrixType, _UpLo, _Preconditioner >
 A minimal residual solver for sparse symmetric problems. More...
 
class  Eigen::SolveWithGuess
 Pseudo expression representing a solving operation. More...
 

Functions

template<typename TMatrix , typename CMatrix , typename VectorX , typename VectorB , typename VectorF >
void Eigen::internal::constrained_cg (const TMatrix &A, const CMatrix &C, VectorX &x, const VectorB &b, const VectorF &f, IterationController &iter)
 
template<typename CMatrix , typename CINVMatrix >
void Eigen::internal::pseudo_inverse (const CMatrix &C, CINVMatrix &CINV)
 

Detailed Description

Function Documentation

◆ constrained_cg()

template<typename TMatrix , typename CMatrix , typename VectorX , typename VectorB , typename VectorF >
void Eigen::internal::constrained_cg ( const TMatrix &  A,
const CMatrix &  C,
VectorX x,
const VectorB &  b,
const VectorF &  f,
IterationController iter 
)

Constrained conjugate gradient

Computes the minimum of $ 1/2((Ax).x) - bx $ under the constraint $ Cx \le f $

Definition at line 106 of file ConstrainedConjGrad.h.

◆ pseudo_inverse()

template<typename CMatrix , typename CINVMatrix >
void Eigen::internal::pseudo_inverse ( const CMatrix &  C,
CINVMatrix &  CINV 
)

Compute the pseudo inverse of the non-square matrix C such that $ CINV = (C * C^T)^{-1} * C $ based on a conjugate gradient method.

This function is internally used by constrained_cg.

Definition at line 47 of file ConstrainedConjGrad.h.



gtsam
Author(s):
autogenerated on Fri Nov 1 2024 03:43:30