Module « scipy.sparse.linalg »

Fonction spilu - module scipy.sparse.linalg

Signature de la fonction spilu

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) 

Description

spilu.__doc__

    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.

    Remaining other options
        Same as for `splu`

    Returns
    -------
    invA_approx : scipy.sparse.linalg.SuperLU
        Object, which has a ``solve`` method.

    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
    --------
    >>> 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.])