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Eigen
3.2.9
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A direct sparse Cholesky factorizations.
These classes provide LL^T and LDL^T Cholesky factorizations of sparse matrices that are selfadjoint and positive definite. The factorization allows for solving A.X = B where X and B can be either dense or sparse.
In order to reduce the fill-in, a symmetric permutation P is applied prior to the factorization such that the factorized matrix is P A P^-1.
_MatrixType | the type of the sparse matrix A, it must be a SparseMatrix<> |
_UpLo | the triangular part that will be used for the computations. It can be Lower or Upper. Default is Lower. |
Inherits noncopyable.
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struct | keep_diag |
Public Member Functions | |
ComputationInfo | info () const |
Reports whether previous computation was successful. More... | |
const PermutationMatrix< Dynamic, Dynamic, Index > & | permutationP () const |
const PermutationMatrix< Dynamic, Dynamic, Index > & | permutationPinv () const |
Derived & | setShift (const RealScalar &offset, const RealScalar &scale=1) |
SimplicialCholeskyBase () | |
template<typename Rhs > | |
const internal::solve_retval< SimplicialCholeskyBase, Rhs > | solve (const MatrixBase< Rhs > &b) const |
template<typename Rhs > | |
const internal::sparse_solve_retval< SimplicialCholeskyBase, Rhs > | solve (const SparseMatrixBase< Rhs > &b) const |
Protected Member Functions | |
template<bool DoLDLT> | |
void | compute (const MatrixType &matrix) |
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Default constructor
Referenced by SimplicialCholeskyBase< SimplicialLDLT< _MatrixType, _UpLo, _Ordering > >::SimplicialCholeskyBase().
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Computes the sparse Cholesky decomposition of matrix
Referenced by SimplicialLDLT< _MatrixType, _UpLo, _Ordering >::determinant().
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Reports whether previous computation was successful.
Success
if computation was succesful, NumericalIssue
if the matrix.appears to be negative.
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Sets the shift parameters that will be used to adjust the diagonal coefficients during the numerical factorization.
During the numerical factorization, the diagonal coefficients are transformed by the following linear model:
d_ii
= offset + scale * d_ii
The default is the identity transformation with offset=0, and scale=1.
*this
.
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