HLIBpro
3.0
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implements Gauss-Seidel preconditioner More...
#include <TSOR.hh>
Public Member Functions | |
TGaussSeidel (const TMatrix< value_t > *A, const gs_type_t gs_type, const real_t damping=real_t(1)) | |
constructs Gauss-Seidel preconditioner of type gs_type | |
const TMatrix< value_t > * | matrix () const |
return internal sparse matrix | |
gs_type_t | gs_type () const |
return GaussSeidel type | |
real_t | damping_factor () const |
return damping factor | |
bool | is_complex () const |
return true, if field type is complex | |
bool | is_self_adjoint () const |
return true, of operator is self adjoint | |
virtual void | apply (const TVector< value_t > *x, TVector< value_t > *y, const matop_t op=apply_normal) const |
virtual void | apply_add (const value_t alpha, const TVector< value_t > *x, TVector< value_t > *y, const matop_t op=apply_normal) const |
virtual void | apply_add (const value_t alpha, const BLAS::Vector< value_t > &x, BLAS::Vector< value_t > &y, const matop_t op=apply_normal) const |
virtual size_t | domain_dim () const |
return dimension of domain | |
virtual size_t | range_dim () const |
return dimension of range | |
virtual auto | domain_vector () const -> std::unique_ptr< TVector< value_t > > |
return vector in domain space | |
virtual auto | range_vector () const -> std::unique_ptr< TVector< value_t > > |
return vector in range space | |
Public Member Functions inherited from TLinearOperator< T_value > | |
virtual bool | is_real () const |
return true, if field type is real valued | |
Public Member Functions inherited from TTypeInfo | |
virtual typeid_t | type () const =0 |
return type ID of object | |
virtual bool | is_type (const typeid_t t) const |
return true if local object is of given type ID t | |
virtual std::string | typestr () const |
return string representation of type | |
TGaussSeidel provides application of a Gauss-Seidel type preconditioner for a given matrix \f$A = D - E - F\f$, with diagonal D, strictly lower triangular matrix E and strictly upper triangular matrix F. For forward GS, the applied operator is \f$ ( D - E )^{-1}\f$, for backward GS \f$ ( D - F )^{-1}\f$ and the combination of both for the symmetric GS, i.e., \f$ ( D - F )^{-1} D ( D - E )^{-1}\f$. TGaussSeidel implements point-wise and block-wise GS steps. However, point-wise GS is only supported for sparse matrices while block-wise GS is only supported for H-matrices
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mapping function of linear operator \(A\), e.g. \( y := A(x)\). Depending on op, either \(A\), \(A^T\) or \(A^H\) is applied.
Implements TLinearOperator< T_value >.
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same as above but only the dimension of the vector spaces is tested, not the corresponding index sets
Implements TLinearOperator< T_value >.
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mapping function with update: \( y := y + \alpha A(x)\). Depending on op, either \(A\), \(A^T\) or \(A^H\) is applied.
Implements TLinearOperator< T_value >.