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Eigen  3.3.9
Eigen::SuperLU< _MatrixType > Class Template Reference

Detailed Description

template<typename _MatrixType>
class Eigen::SuperLU< _MatrixType >

A sparse direct LU factorization and solver based on the SuperLU library.

This class allows to solve for A.X = B sparse linear problems via a direct LU factorization using the SuperLU library. The sparse matrix A must be squared and invertible. The vectors or matrices X and B can be either dense or sparse.

Template Parameters
_MatrixTypethe type of the sparse matrix A, it must be a SparseMatrix<>
This class is only for the 4.x versions of SuperLU. The 3.x and 5.x versions are not supported.

This class follows the sparse solver concept .

See also
Sparse solver concept, class SparseLU
+ Inheritance diagram for Eigen::SuperLU< _MatrixType >:

Public Member Functions

void analyzePattern (const MatrixType &matrix)
void factorize (const MatrixType &matrix)
- Public Member Functions inherited from Eigen::SuperLUBase< _MatrixType, SuperLU< _MatrixType > >
void analyzePattern (const MatrixType &)
void compute (const MatrixType &matrix)
ComputationInfo info () const
 Reports whether previous computation was successful. More...
superlu_options_t & options ()
- Public Member Functions inherited from Eigen::SparseSolverBase< SuperLU< _MatrixType > >
const Solve< SuperLU< _MatrixType >, Rhs > solve (const MatrixBase< Rhs > &b) const
const Solve< SuperLU< _MatrixType >, Rhs > solve (const SparseMatrixBase< Rhs > &b) const
 SparseSolverBase ()

Member Function Documentation

◆ analyzePattern()

template<typename _MatrixType >
void Eigen::SuperLU< _MatrixType >::analyzePattern ( const MatrixType &  matrix)

Performs a symbolic decomposition on the sparcity of matrix.

This function is particularly useful when solving for several problems having the same structure.

See also

◆ factorize()

template<typename MatrixType >
void Eigen::SuperLU< MatrixType >::factorize ( const MatrixType &  matrix)

Performs a numeric decomposition of matrix

The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.

See also

The documentation for this class was generated from the following file: