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Eigen
3.2.6
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A sparse direct Cholesky (LDLT) factorization and solver based on the PARDISO library.
This class allows to solve for A.X = B sparse linear problems via a LDL^T Cholesky factorization using the Intel MKL PARDISO library. The sparse matrix A is assumed to be selfajoint and positive definite. For complex matrices, A can also be symmetric only, see the Options template parameter. The vectors or matrices X and B can be either dense or sparse.
MatrixType | the type of the sparse matrix A, it must be a SparseMatrix<> |
Options | can be any bitwise combination of Upper, Lower, and Symmetric. The default is Upper, meaning only the upper triangular part has to be used. Symmetric can be used for symmetric, non-selfadjoint complex matrices, the default being to assume a selfadjoint matrix. Upper|Lower can be used to tell both triangular parts can be used as input. |
Inherits PardisoImpl< Derived >.
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Derived & | analyzePattern (const MatrixType &matrix) |
Derived & | factorize (const MatrixType &matrix) |
ComputationInfo | info () const |
Reports whether previous computation was successful. More... | |
ParameterType & | pardisoParameterArray () |
template<typename Rhs > | |
const internal::solve_retval< PardisoImpl, Rhs > | solve (const MatrixBase< Rhs > &b) const |
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const internal::sparse_solve_retval< PardisoImpl, Rhs > | solve (const SparseMatrixBase< Rhs > &b) const |
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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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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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Reports whether previous computation was successful.
Success
if computation was succesful, NumericalIssue
if the matrix appears to be negative.
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