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 fill_factor AND decrease drop_tol.
This function uses the SuperLU library.
Sparse matrix to factorize. Most efficient when provided in CSC format. Other formats will be converted to CSC before factorization.
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
import numpy as np
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)
A.dot(B.solve(x))
B.solve(A.dot(x))
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.sparse.linalg._dsolve.linsolve:splu
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