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 matrix to factorize

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.

milu : str, optional

Which version of modified ILU to use. (Choices: silu, smilu_1, smilu_2 (default), smilu_3.)

Remaining other options :

Same as for splu

Returns :

invA_approx : scipy.sparse.linalg.dsolve._superlu.SciPyLUType

Object, which has a solve method.

See also

complete LU decomposition


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.

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