spilu(A, drop_tol=None, fill_factor=None, drop_rule=None, permc_spec=None, diag_pivot_thresh=None, relax=None, panel_size=None, options=None)
The resulting object is an approximation to the inverse of A.
To improve the better approximation to the inverse, you may need to increase :None:None:`fill_factor` AND decrease :None:None:`drop_tol`.
This function uses the SuperLU library.
Sparse matrix to factorize
Drop tolerance (0 <= tol <= 1) for an incomplete LU decomposition. (default: 1e-4)
Specifies the fill ratio upper bound (>= 1.0) for ILU. (default: 10)
Comma-separated string of drop rules to use. Available rules: basic
, prows
, column
, area
, secondary
, dynamic
, interp
. (Default: basic,area
)
See SuperLU documentation for details.
Same as for splu
Object, which has a solve
method.
Compute an incomplete LU decomposition for a sparse, square matrix.
splu
complete LU decomposition
>>> from scipy.sparse import csc_matrix
... from scipy.sparse.linalg import spilu
... A = csc_matrix([[1., 0., 0.], [5., 0., 2.], [0., -1., 0.]], dtype=float)
... B = spilu(A)
... x = np.array([1., 2., 3.], dtype=float)
... B.solve(x) array([ 1. , -3. , -1.5])
>>> A.dot(B.solve(x)) array([ 1., 2., 3.])
>>> B.solve(A.dot(x)) array([ 1., 2., 3.])See :
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.sparse.linalg._dsolve.linsolve.splu
scipy.sparse.linalg._dsolve.linsolve.spilu
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