Repository: scipy/scipy
File: scipy/sparse/linalg/_dsolve/linsolve.py (Python)

Code before:
        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
    -----
    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``.

    The effect of ``drop_tol`` is matrix-dependent. In particular, ``drop_tol``
    does not guarantee that existing off-diagonal entries will be removed;
    it controls dropping of candidate entries during factorization (often
    fill-in). For some sparsity patterns, the ILU factors can be identical
    to the full LU factors even for large ``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.])
    """


Review comment (suggestion):
```suggestion
    increase `fill_factor` AND decrease `drop_tol`.
```

Code after:
        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
    -----
    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`.

    The effect of `drop_tol` is matrix-dependent. In particular, `drop_tol`
    does not guarantee that existing off-diagonal entries will be removed;
    it controls dropping of candidate entries during factorization (often
    fill-in). For some sparsity patterns, the ILU factors can be identical
    to the full LU factors even for large `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.])
    """