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< Decomposition, RhsType, GuessType > |
| 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 
under the constraint 
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 
based on a conjugate gradient method.
This function is internally used by constrained_cg.
Definition at line 47 of file ConstrainedConjGrad.h.