 Eigen  3.3.7 Eigen::CholmodBase< _MatrixType, _UpLo, Derived > Class Template Reference

## Detailed Description

### template<typename _MatrixType, int _UpLo, typename Derived> class Eigen::CholmodBase< _MatrixType, _UpLo, Derived >

The base class for the direct Cholesky factorization of Cholmod.

class CholmodSupernodalLLT, class CholmodSimplicialLDLT, class CholmodSimplicialLLT Inheritance diagram for Eigen::CholmodBase< _MatrixType, _UpLo, Derived >:

## Public Member Functions

void analyzePattern (const MatrixType &matrix)

cholmod_common & cholmod ()

Derived & compute (const MatrixType &matrix)

Scalar determinant () const

void factorize (const MatrixType &matrix)

ComputationInfo info () const
Reports whether previous computation was successful. More...

Scalar logDeterminant () const

Derived & setShift (const RealScalar &offset) Public Member Functions inherited from Eigen::SparseSolverBase< Derived >
template<typename Rhs >
const Solve< Derived, Rhs > solve (const MatrixBase< Rhs > &b) const

template<typename Rhs >
const Solve< Derived, Rhs > solve (const SparseMatrixBase< Rhs > &b) const

SparseSolverBase ()

## ◆ analyzePattern()

template<typename _MatrixType, int _UpLo, typename Derived>
 void Eigen::CholmodBase< _MatrixType, _UpLo, Derived >::analyzePattern ( const MatrixType & matrix )
inline

Performs a symbolic decomposition on the sparsity pattern of matrix.

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

factorize()

## ◆ cholmod()

template<typename _MatrixType, int _UpLo, typename Derived>
 cholmod_common& Eigen::CholmodBase< _MatrixType, _UpLo, Derived >::cholmod ( )
inline

Returns a reference to the Cholmod's configuration structure to get a full control over the performed operations. See the Cholmod user guide for details.

## ◆ compute()

template<typename _MatrixType, int _UpLo, typename Derived>
 Derived& Eigen::CholmodBase< _MatrixType, _UpLo, Derived >::compute ( const MatrixType & matrix )
inline

Computes the sparse Cholesky decomposition of matrix

## ◆ determinant()

template<typename _MatrixType, int _UpLo, typename Derived>
 Scalar Eigen::CholmodBase< _MatrixType, _UpLo, Derived >::determinant ( ) const
inline
Returns
the determinant of the underlying matrix from the current factorization

## ◆ factorize()

template<typename _MatrixType, int _UpLo, typename Derived>
 void Eigen::CholmodBase< _MatrixType, _UpLo, Derived >::factorize ( const MatrixType & matrix )
inline

Performs a numeric decomposition of matrix

The given matrix must have the same sparsity pattern as the matrix on which the symbolic decomposition has been performed.

analyzePattern()

## ◆ info()

template<typename _MatrixType, int _UpLo, typename Derived>
 ComputationInfo Eigen::CholmodBase< _MatrixType, _UpLo, Derived >::info ( ) const
inline

Reports whether previous computation was successful.

Returns
`Success` if computation was succesful, `NumericalIssue` if the matrix.appears to be negative.

## ◆ logDeterminant()

template<typename _MatrixType, int _UpLo, typename Derived>
 Scalar Eigen::CholmodBase< _MatrixType, _UpLo, Derived >::logDeterminant ( ) const
inline
Returns
the log determinant of the underlying matrix from the current factorization

## ◆ setShift()

template<typename _MatrixType, int _UpLo, typename Derived>
 Derived& Eigen::CholmodBase< _MatrixType, _UpLo, Derived >::setShift ( const RealScalar & offset )
inline

Sets the shift parameter that will be used to adjust the diagonal coefficients during the numerical factorization.

During the numerical factorization, an offset term is added to the diagonal coefficients:
`d_ii` = offset + `d_ii`

The default is offset=0.

Returns
a reference to `*this`.

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