Eigen::SPQR
template<typename _MatrixType>
class Eigen::SPQR< _MatrixType >
Sparse QR factorization based on SuiteSparseQR library.
This class is used to perform a multithreaded and multifrontal rank-revealing QR decomposition of sparse matrices. The result is then used to solve linear leasts_square systems. Clearly, a QR factorization is returned such that A*P = Q*R where :
P is the column permutation. Use colsPermutation() to get it.
Q is the orthogonal matrix represented as Householder reflectors. Use matrixQ() to get an expression and matrixQ().transpose() to get the transpose. You can then apply it to a vector.
R is the sparse triangular factor. Use matrixQR() to get it as SparseMatrix. NOTE : The Index type of R is always SuiteSparse_long. You can get it with SPQR::Index
- Template Parameters
-
| _MatrixType |
The type of the sparse matrix A, must be a column-major SparseMatrix<> |
This class follows the sparse solver concept .
cholmodCommon()
template<typename _MatrixType >
| cholmod_common* Eigen::SPQR< _MatrixType >::cholmodCommon | ( | |
) | const | | inline |
- Returns
- a pointer to the SPQR workspace
cols()
template<typename _MatrixType >
Get the number of columns of the input matrix.
info()
template<typename _MatrixType >
Reports whether previous computation was successful.
- Returns
-
Success if computation was successful, NumericalIssue if the sparse QR can not be computed
matrixR()
template<typename _MatrixType >
- Returns
- the sparse triangular factor R. It is a sparse matrix
rank()
template<typename _MatrixType >
Gets the rank of the matrix. It should be equal to matrixQR().cols if the matrix is full-rank
rows()
template<typename _MatrixType >
Get the number of rows of the input matrix and the Q matrix
The documentation for this class was generated from the following file:
Public Member Functions inherited from Eigen::SparseSolverBase< SPQR< _MatrixType > >