spilu#
- scipy.sparse.linalg.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)[source]#
- Compute an incomplete LU decomposition for a sparse, square matrix. - 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_tolfloat, optional
- Drop tolerance (0 <= tol <= 1) for an incomplete LU decomposition. (default: 1e-4) 
- fill_factorfloat, optional
- Specifies the fill ratio upper bound (>= 1.0) for ILU. (default: 10) 
- drop_rulestr, 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_approxscipy.sparse.linalg.SuperLU
- Object, which has a - solvemethod.
 
 - See also - splu
- complete LU decomposition 
 - Notes - 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) array([ 1. , -3. , -1.5]) >>> A.dot(B.solve(x)) array([ 1., 2., 3.]) >>> B.solve(A.dot(x)) array([ 1., 2., 3.])