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Eigen
3.4.0
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A direct sparse LDLT Cholesky factorizations without square root.
This class provides a LDL^T Cholesky factorizations without square root 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. |
_Ordering | The ordering method to use, either AMDOrdering<> or NaturalOrdering<>. Default is AMDOrdering<> |
This class follows the sparse solver concept .
Public Member Functions | |
void | analyzePattern (const MatrixType &a) |
SimplicialLDLT & | compute (const MatrixType &matrix) |
Scalar | determinant () const |
void | factorize (const MatrixType &a) |
const MatrixL | matrixL () const |
const MatrixU | matrixU () const |
SimplicialLDLT () | |
SimplicialLDLT (const MatrixType &matrix) | |
const VectorType | vectorD () const |
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ComputationInfo | info () const |
Reports whether previous computation was successful. | |
const PermutationMatrix< Dynamic, Dynamic, StorageIndex > & | permutationP () const |
const PermutationMatrix< Dynamic, Dynamic, StorageIndex > & | permutationPinv () const |
SimplicialLDLT< _MatrixType, _UpLo, _Ordering > & | setShift (const RealScalar &offset, const RealScalar &scale=1) |
SimplicialCholeskyBase () | |
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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 () | |
Additional Inherited Members | |
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void | compute (const MatrixType &matrix) |
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Default constructor
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Constructs and performs the LLT factorization of matrix
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Performs a symbolic decomposition on the sparcity of matrix.
This function is particularly useful when solving for several problems having the same structure.
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Computes the sparse Cholesky decomposition of matrix
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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.
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