The IDR(s)STAB(l) is a combination of IDR(s) and BiCGSTAB(l). It is a short-recurrences Krylov method for sparse square problems. It can outperform both IDR(s) and BiCGSTAB(l). IDR(s)STAB(l) generally closely follows the optimal GMRES convergence in terms of the number of Matrix-Vector products. However, without the increasing cost per iteration of GMRES. IDR(s)STAB(l) is suitable for both indefinite systems and systems with complex eigenvalues. More...
Public Types | |
typedef MatrixType_ | MatrixType |
typedef Preconditioner_ | Preconditioner |
typedef MatrixType::RealScalar | RealScalar |
typedef MatrixType::Scalar | Scalar |
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typedef internal::traits< Derived >::MatrixType | MatrixType |
typedef internal::traits< Derived >::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 |
IDRSTABL () | |
template<typename MatrixDerived > | |
IDRSTABL (const EigenBase< MatrixDerived > &A) | |
void | setL (Index L) |
void | setS (Index S) |
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void | _solve_impl (const Rhs &b, Dest &x) const |
std::enable_if_t< Rhs::ColsAtCompileTime!=1 &&DestDerived::ColsAtCompileTime!=1 > | _solve_with_guess_impl (const Rhs &b, MatrixBase< DestDerived > &aDest) const |
std::enable_if_t< Rhs::ColsAtCompileTime==1||DestDerived::ColsAtCompileTime==1 > | _solve_with_guess_impl (const Rhs &b, MatrixBase< DestDerived > &dest) const |
void | _solve_with_guess_impl (const Rhs &b, SparseMatrixBase< DestDerived > &aDest) const |
Derived & | analyzePattern (const EigenBase< MatrixDerived > &A) |
EIGEN_CONSTEXPR Index | cols () const EIGEN_NOEXCEPT |
Derived & | compute (const EigenBase< MatrixDerived > &A) |
Derived & | derived () |
const Derived & | derived () const |
RealScalar | error () const |
Derived & | factorize (const EigenBase< MatrixDerived > &A) |
ComputationInfo | info () const |
Index | iterations () const |
IterativeSolverBase () | |
IterativeSolverBase (const EigenBase< MatrixDerived > &A) | |
IterativeSolverBase (IterativeSolverBase &&)=default | |
Index | maxIterations () const |
Preconditioner & | preconditioner () |
const Preconditioner & | preconditioner () const |
EIGEN_CONSTEXPR Index | rows () const EIGEN_NOEXCEPT |
Derived & | setMaxIterations (Index maxIters) |
Derived & | setTolerance (const RealScalar &tolerance) |
const SolveWithGuess< Derived, Rhs, Guess > | solveWithGuess (const MatrixBase< Rhs > &b, const Guess &x0) const |
RealScalar | tolerance () const |
~IterativeSolverBase () | |
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Derived & | derived () |
const Derived & | derived () const |
const Solve< Derived, Rhs > | solve (const MatrixBase< Rhs > &b) const |
const Solve< Derived, Rhs > | solve (const SparseMatrixBase< Rhs > &b) const |
SparseSolverBase () | |
SparseSolverBase (SparseSolverBase &&other) | |
~SparseSolverBase () | |
Private Types | |
typedef IterativeSolverBase< IDRSTABL > | Base |
Private Member Functions | |
const ActualMatrixType & | matrix () const |
Private Attributes | |
RealScalar | m_error |
ComputationInfo | m_info |
bool | m_isInitialized |
Index | m_iterations |
Index | m_L |
Index | m_S |
The IDR(s)STAB(l) is a combination of IDR(s) and BiCGSTAB(l). It is a short-recurrences Krylov method for sparse square problems. It can outperform both IDR(s) and BiCGSTAB(l). IDR(s)STAB(l) generally closely follows the optimal GMRES convergence in terms of the number of Matrix-Vector products. However, without the increasing cost per iteration of GMRES. IDR(s)STAB(l) is suitable for both indefinite systems and systems with complex eigenvalues.
This class allows solving for A.x = b sparse linear problems. The vectors x and b can be either dense or sparse.
MatrixType_ | the type of the sparse matrix A, can be a dense or a sparse matrix. |
Preconditioner_ | the type of the preconditioner. Default is DiagonalPreconditioner |
This class follows the sparse solver concept .
The maximum 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 maximum number of iterations and NumTraits<Scalar>::epsilon() for the tolerance.
The tolerance is the maximum relative residual error: |Ax-b|/|b| for which the linear system is considered solved.
Performance: When using sparse matrices, best performance is achieved for a row-major sparse matrix format. Moreover, in this case multi-threading can be exploited if the user code is compiled with OpenMP enabled. See Eigen and multi-threading for details.
By default the iterations start with x=0 as an initial guess of the solution. One can control the start using the solveWithGuess() method.
IDR(s)STAB(l) can also be used in a matrix-free context, see the following example .
Definition at line 412 of file IDRSTABL.h.
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Definition at line 413 of file IDRSTABL.h.
typedef MatrixType_ Eigen::IDRSTABL< MatrixType_, Preconditioner_ >::MatrixType |
Definition at line 423 of file IDRSTABL.h.
typedef Preconditioner_ Eigen::IDRSTABL< MatrixType_, Preconditioner_ >::Preconditioner |
Definition at line 426 of file IDRSTABL.h.
typedef MatrixType::RealScalar Eigen::IDRSTABL< MatrixType_, Preconditioner_ >::RealScalar |
Definition at line 425 of file IDRSTABL.h.
typedef MatrixType::Scalar Eigen::IDRSTABL< MatrixType_, Preconditioner_ >::Scalar |
Definition at line 424 of file IDRSTABL.h.
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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 443 of file IDRSTABL.h.
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Loops over the number of columns of b and does the following:
Definition at line 452 of file IDRSTABL.h.
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Sets the parameter L, indicating the amount of minimize residual steps are used.
Definition at line 462 of file IDRSTABL.h.
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Sets the parameter S, indicating the dimension of the shadow residual space..
Definition at line 468 of file IDRSTABL.h.
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Definition at line 419 of file IDRSTABL.h.
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Definition at line 420 of file IDRSTABL.h.