Eigen-unsupported  3.3.7
Eigen::SkylineInplaceLU< MatrixType > Class Template Reference

## Detailed Description

### template<typename MatrixType> class Eigen::SkylineInplaceLU< MatrixType >

Inplace LU decomposition of a skyline matrix and associated features.

Parameters
 MatrixType the type of the matrix of which we are computing the LU factorization

## Public Member Functions

void compute ()

int flags () const

RealScalar precision () const

void setFlags (int f)

void setPrecision (RealScalar v)

SkylineInplaceLU (MatrixType &matrix, int flags=0)

template<typename BDerived , typename XDerived >
bool solve (const MatrixBase< BDerived > &b, MatrixBase< XDerived > *x, const int transposed=0) const

bool succeeded (void) const

## ◆ SkylineInplaceLU()

template<typename MatrixType >
 Eigen::SkylineInplaceLU< MatrixType >::SkylineInplaceLU ( MatrixType & matrix, int flags = `0` )
inline

Creates a LU object and compute the respective factorization of matrix using flags flags.

## ◆ compute()

template<typename MatrixType >
 void Eigen::SkylineInplaceLU< MatrixType >::compute ( )

Computes/re-computes the LU factorization

Computes / recomputes the in place LU decomposition of the SkylineInplaceLU. using the default algorithm.

## ◆ flags()

template<typename MatrixType >
 int Eigen::SkylineInplaceLU< MatrixType >::flags ( ) const
inline
Returns
the current flags

## ◆ precision()

template<typename MatrixType >
 RealScalar Eigen::SkylineInplaceLU< MatrixType >::precision ( ) const
inline
Returns
the current precision.
setPrecision()

## ◆ setFlags()

template<typename MatrixType >
 void Eigen::SkylineInplaceLU< MatrixType >::setFlags ( int f )
inline

Sets the flags. Possible values are:

• CompleteFactorization
• IncompleteFactorization
• MemoryEfficient
• one of the ordering methods
• etc...
flags()

## ◆ setPrecision()

template<typename MatrixType >
 void Eigen::SkylineInplaceLU< MatrixType >::setPrecision ( RealScalar v )
inline

Sets the relative threshold value used to prune zero coefficients during the decomposition.

Setting a value greater than zero speeds up computation, and yields to an imcomplete factorization with fewer non zero coefficients. Such approximate factors are especially useful to initialize an iterative solver.

Note that the exact meaning of this parameter might depends on the actual backend. Moreover, not all backends support this feature.

precision()

## ◆ solve()

template<typename MatrixType >
template<typename BDerived , typename XDerived >
 bool Eigen::SkylineInplaceLU< MatrixType >::solve ( const MatrixBase< BDerived > & b, MatrixBase< XDerived > * x, const int transposed = `0` ) const
Returns
the lower triangular matrix L
the upper triangular matrix U

Computes *x = U^-1 L^-1 b

If transpose is set to SvTranspose or SvAdjoint, the solution of the transposed/adjoint system is computed instead.

Not all backends implement the solution of the transposed or adjoint system.

## ◆ succeeded()

template<typename MatrixType >
 bool Eigen::SkylineInplaceLU< MatrixType >::succeeded ( void ) const
inline
Returns
true if the factorization succeeded

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