template<typename _Scalar, typename _StorageIndex = int>
IncompleteLUT class
Incomplete LU factorization with dual-threshold strategy.
Contents
- Reference
This class follows the sparse solver concept.
During the numerical factorization, two dropping rules are used : 1) any element whose magnitude is less than some tolerance is dropped. This tolerance is obtained by multiplying the input tolerance droptol
by the average magnitude of all the original elements in the current row. 2) After the elimination of the row, only the fill
largest elements in the L part and the fill
largest elements in the U part are kept (in addition to the diagonal element ). Note that fill
is computed from the input parameter fillfactor
which is used the ratio to control the fill_in relatively to the initial number of nonzero elements.
The two extreme cases are when droptol=0
(to keep all the fill*2
largest elements) and when fill=n/2
with droptol
being different to zero.
References : Yousef Saad, ILUT: A dual threshold incomplete LU factorization, Numerical Linear Algebra with Applications, 1(4), pp 387-402, 1994.
NOTE : The following implementation is derived from the ILUT implementation in the SPARSKIT package, Copyright (C) 2005, the Regents of the University of Minnesota released under the terms of the GNU LGPL: http:/
Base classes
-
template<typename Derived>class SparseSolverBase
- A base class for sparse solvers.
Public functions
-
template<typename MatrixType>auto compute(const MatrixType& amat) -> IncompleteLUT&
- auto info() const -> ComputationInfo
- Reports whether previous computation was successful.
- void setDroptol(const RealScalar& droptol)
- void setFillfactor(int fillfactor)
Protected types
- struct keep_diag
Function documentation
template<typename _Scalar, typename _StorageIndex>
template<typename MatrixType>
IncompleteLUT& Eigen:: IncompleteLUT<_Scalar, _StorageIndex>:: compute(const MatrixType& amat)
Compute an incomplete LU factorization with dual threshold on the matrix mat No pivoting is done in this version
template<typename _Scalar, typename _StorageIndex>
ComputationInfo Eigen:: IncompleteLUT<_Scalar, _StorageIndex>:: info() const
Reports whether previous computation was successful.
Returns | Success if computation was successful, NumericalIssue if the matrix.appears to be negative. |
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template<typename _Scalar, typename _StorageIndex>
void Eigen:: IncompleteLUT<_Scalar, _StorageIndex>:: setDroptol(const RealScalar& droptol)
Parameters | |
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droptol | Drop any element whose magnitude is less than this tolerance |
Set control parameter droptol
template<typename _Scalar, typename _StorageIndex>
void Eigen:: IncompleteLUT<_Scalar, _StorageIndex>:: setFillfactor(int fillfactor)
Parameters | |
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fillfactor | This is used to compute the number fill_in of largest elements to keep on each row. |
Set control parameter fillfactor