def use_solver(**kwargs):
"""
Select default sparse direct solver to be used.
Parameters
----------
useUmfpack : bool, optional
Use UMFPACK 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.
"""
if 'useUmfpack' in kwargs:
globals()['useUmfpack'] = kwargs['useUmfpack']
if useUmfpack and 'assumeSortedIndices' in kwargs:
umfpack.configure(assumeSortedIndices=kwargs['assumeSortedIndices'])
def __init__(self):
# List here the functions that can be used to solve equations
self.convergence_helpers = [self.solve_simple,
self.solve_homotopy_gmin2,
self.solve_homotopy_source,
None]
# Allocate UmfPack context
umfp.configure(assumeSortedIndices = True)
self._umf = umfp.UmfpackContext(family='di', maxCond=1e20)
# Set control options. Try to find optimum values?
self._umf.control[5] = umfp.UMFPACK_STRATEGY_AUTO
self._umf.control[7] = 1 # Iterative refinement steps
self._umf.control[16] = umfp.UMFPACK_SCALE_NONE
# print(self._umf.report_control())
# Force Symbolic and Numeric factorization
self.__doSymbolic = True
def use_solver( **kwargs ):
"""
Valid keyword arguments with defaults (other ignored):
useUmfpack = True
assumeSortedIndices = False
The default sparse solver is umfpack when available. 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.
"""
if 'useUmfpack' in kwargs:
globals()['useUmfpack'] = kwargs['useUmfpack']
if isUmfpack:
umfpack.configure( **kwargs )
def use_solver(**kwargs):
"""
Valid keyword arguments with defaults (other ignored)::
useUmfpack = True
assumeSortedIndices = False
The default sparse solver is umfpack when available. 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.
"""
if 'useUmfpack' in kwargs:
globals()['useUmfpack'] = kwargs['useUmfpack']
if isUmfpack:
umfpack.configure(**kwargs)
def use_solver(**kwargs):
"""
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
--------
>>> from scipy.sparse.linalg import use_solver, spsolve
>>> from scipy.sparse import csc_matrix
>>> R = np.random.randn(5, 5)
>>> A = csc_matrix(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
"""
if 'useUmfpack' in kwargs:
globals()['useUmfpack'] = kwargs['useUmfpack']
if useUmfpack and 'assumeSortedIndices' in kwargs:
umfpack.configure(assumeSortedIndices=kwargs['assumeSortedIndices'])
