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Module « scipy.sparse.linalg »

Fonction use_solver - module scipy.sparse.linalg

Signature de la fonction use_solver

def use_solver(**kwargs) 

Description

help(scipy.sparse.linalg.use_solver)

Select default sparse direct solver to be used.

Parameters
----------
useUmfpack : bool, optional
    Use UMFPACK [1]_, [2]_, [3]_, [4]_. over SuperLU. Has effect only
    if ``scikits.umfpack`` is installed. Default: True
assumeSortedIndices : bool, optional
    Allow UMFPACK to skip the step of sorting indices for a CSR/CSC matrix.
    Has effect only if useUmfpack is True and ``scikits.umfpack`` is
    installed. Default: False

Notes
-----
The default sparse solver is UMFPACK when available
(``scikits.umfpack`` is installed). This can be changed by passing
useUmfpack = False, which then causes the always present SuperLU
based solver to be used.

UMFPACK requires a CSR/CSC matrix to have sorted column/row indices. If
sure that the matrix fulfills this, pass ``assumeSortedIndices=True``
to gain some speed.

References
----------
.. [1] T. A. Davis, Algorithm 832:  UMFPACK - an unsymmetric-pattern
       multifrontal method with a column pre-ordering strategy, ACM
       Trans. on Mathematical Software, 30(2), 2004, pp. 196--199.
       https://dl.acm.org/doi/abs/10.1145/992200.992206

.. [2] T. A. Davis, A column pre-ordering strategy for the
       unsymmetric-pattern multifrontal method, ACM Trans.
       on Mathematical Software, 30(2), 2004, pp. 165--195.
       https://dl.acm.org/doi/abs/10.1145/992200.992205

.. [3] T. A. Davis and I. S. Duff, A combined unifrontal/multifrontal
       method for unsymmetric sparse matrices, ACM Trans. on
       Mathematical Software, 25(1), 1999, pp. 1--19.
       https://doi.org/10.1145/305658.287640

.. [4] T. A. Davis and I. S. Duff, An unsymmetric-pattern multifrontal
       method for sparse LU factorization, SIAM J. Matrix Analysis and
       Computations, 18(1), 1997, pp. 140--158.
       https://doi.org/10.1137/S0895479894246905T.

Examples
--------
>>> import numpy as np
>>> from scipy.sparse.linalg import use_solver, spsolve
>>> from scipy.sparse import csc_array
>>> R = np.random.randn(5, 5)
>>> A = csc_array(R)
>>> b = np.random.randn(5)
>>> use_solver(useUmfpack=False) # enforce superLU over UMFPACK
>>> x = spsolve(A, b)
>>> np.allclose(A.dot(x), b)
True
>>> use_solver(useUmfpack=True) # reset umfPack usage to default

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