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bundles / scipy 1.17.1 / scipy / sparse / linalg / _dsolve / linsolve / spilu

function

scipy.sparse.linalg._dsolve.linsolve:spilu

source: /scipy/sparse/linalg/_dsolve/linsolve.py :438

Signature

def   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 )

Summary

Compute an incomplete LU decomposition for a sparse, square matrix.

Extended Summary

The resulting object is an approximation to the inverse of A.

Parameters

A : (N, N) array_like

Sparse array to factorize. Most efficient when provided in CSC format. Other formats will be converted to CSC before factorization.

drop_tol : float, optional

Drop tolerance (0 <= tol <= 1) for an incomplete LU decomposition. (default: 1e-4)

fill_factor : float, optional

Specifies the fill ratio upper bound (>= 1.0) for ILU. (default: 10)

drop_rule : str, optional

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.

Remaining other options

Same as for splu

Returns

invA_approx : scipy.sparse.linalg.SuperLU

Object, which has a solve method.

Notes

When a real array is factorized and the returned SuperLU object's solve() method is used with complex arguments an error is generated. Instead, cast the initial array to complex and then factorize.

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.

Examples

import numpy as np
from scipy.sparse import csc_array
from scipy.sparse.linalg import spilu
A = csc_array([[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))
✗

See also

splu

complete LU decomposition

Aliases

  • scipy.sparse.linalg.spilu

Referenced by

This package