Eigen-unsupported  3.4.90 (git rev 67eeba6e720c5745abc77ae6c92ce0a44aa7b7ae)
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.

MatrixTypethe 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

Constructor & Destructor Documentation

◆ SkylineInplaceLU()

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

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

Member Function Documentation

◆ 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
the current flags

◆ precision()

template<typename MatrixType >
RealScalar Eigen::SkylineInplaceLU< MatrixType >::precision ( ) const
the current precision.
See also

◆ setFlags()

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

Sets the flags. Possible values are:

  • CompleteFactorization
  • IncompleteFactorization
  • MemoryEfficient
  • one of the ordering methods
  • etc...
See also

◆ setPrecision()

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

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 incomplete 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.

See also

◆ 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
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
true if the factorization succeeded

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