scipy.sparse.linalg.splu¶
- scipy.sparse.linalg.splu(A, permc_spec=None, diag_pivot_thresh=None, drop_tol=None, relax=None, panel_size=None, options={})[source]¶
Compute the LU decomposition of a sparse, square matrix.
Parameters : A : sparse matrix
Sparse matrix to factorize. Should be in CSR or CSC format.
permc_spec : str, optional
How to permute the columns of the matrix for sparsity preservation. (default: ‘COLAMD’)
- NATURAL: natural ordering.
- MMD_ATA: minimum degree ordering on the structure of A^T A.
- MMD_AT_PLUS_A: minimum degree ordering on the structure of A^T+A.
- COLAMD: approximate minimum degree column ordering
diag_pivot_thresh : float, optional
Threshold used for a diagonal entry to be an acceptable pivot. See SuperLU user’s guide for details [SLU]
drop_tol : float, optional
(deprecated) No effect.
relax : int, optional
Expert option for customizing the degree of relaxing supernodes. See SuperLU user’s guide for details [SLU]
panel_size : int, optional
Expert option for customizing the panel size. See SuperLU user’s guide for details [SLU]
options : dict, optional
Dictionary containing additional expert options to SuperLU. See SuperLU user guide [SLU] (section 2.4 on the ‘Options’ argument) for more details. For example, you can specify options=dict(Equil=False, IterRefine='SINGLE')) to turn equilibration off and perform a single iterative refinement.
Returns : invA : scipy.sparse.linalg.dsolve._superlu.SciPyLUType
Object, which has a solve method.
See also
- spilu
- incomplete LU decomposition
Notes
This function uses the SuperLU library.
References
[SLU] (1, 2, 3, 4, 5) SuperLU http://crd.lbl.gov/~xiaoye/SuperLU/
