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File Content: `decomp_lu.cpython-35.opt-1.pyc`



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    Compute pivoted LU decomposition of a matrix.

    The decomposition is::

        A = P L U

    where P is a permutation matrix, L lower triangular with unit
    diagonal elements, and U upper triangular.

    Parameters
    ----------
    a : (M, M) array_like
        Matrix to decompose
    overwrite_a : bool, optional
        Whether to overwrite data in A (may increase performance)
    check_finite : bool, optional
        Whether to check that the input matrix contains only finite numbers.
        Disabling may give a performance gain, but may result in problems
        (crashes, non-termination) if the inputs do contain infinities or NaNs.

    Returns
    -------
    lu : (N, N) ndarray
        Matrix containing U in its upper triangle, and L in its lower triangle.
        The unit diagonal elements of L are not stored.
    piv : (N,) ndarray
        Pivot indices representing the permutation matrix P:
        row i of matrix was interchanged with row piv[i].

    See also
    --------
    lu_solve : solve an equation system using the LU factorization of a matrix

    Notes
    -----
    This is a wrapper to the ``*GETRF`` routines from LAPACK.

    ����r���r���zexpected square matrix�getrf�overwrite_az=illegal value in %d-th argument of internal getrf (lu_factor)z4Diagonal number %d is exactly zero. Singular matrix.)r���)	r���r����len�shape�
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�����d�S)	a���Solve an equation system, a x = b, given the LU factorization of a

    Parameters
    ----------
    (lu, piv)
        Factorization of the coefficient matrix a, as given by lu_factor
    b : array
        Right-hand side
    trans : {0, 1, 2}, optional
        Type of system to solve:

        =====  =========
        trans  system
        =====  =========
        0      a x   = b
        1      a^T x = b
        2      a^H x = b
        =====  =========
    overwrite_b : bool, optional
        Whether to overwrite data in b (may increase performance)
    check_finite : bool, optional
        Whether to check that the input matrices contain only finite numbers.
        Disabling may give a performance gain, but may result in problems
        (crashes, non-termination) if the inputs do contain infinities or NaNs.

    Returns
    -------
    x : array
        Solution to the system

    See also
    --------
    lu_factor : LU factorize a matrix

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    Compute pivoted LU decomposition of a matrix.

    The decomposition is::

        A = P L U

    where P is a permutation matrix, L lower triangular with unit
    diagonal elements, and U upper triangular.

    Parameters
    ----------
    a : (M, N) array_like
        Array to decompose
    permute_l : bool, optional
        Perform the multiplication P*L  (Default: do not permute)
    overwrite_a : bool, optional
        Whether to overwrite data in a (may improve performance)
    check_finite : bool, optional
        Whether to check that the input matrix contains only finite numbers.
        Disabling may give a performance gain, but may result in problems
        (crashes, non-termination) if the inputs do contain infinities or NaNs.

    Returns
    -------
    **(If permute_l == False)**

    p : (M, M) ndarray
        Permutation matrix
    l : (M, K) ndarray
        Lower triangular or trapezoidal matrix with unit diagonal.
        K = min(M, N)
    u : (K, N) ndarray
        Upper triangular or trapezoidal matrix

    **(If permute_l == True)**

    pl : (M, K) ndarray
        Permuted L matrix.
        K = min(M, N)
    u : (K, N) ndarray
        Upper triangular or trapezoidal matrix

    Notes
    -----
    This is a LU factorization routine written for Scipy.

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special_matrices.cpython-35.pyc	28873 bytes	0644

File Content: decomp_lu.cpython-35.opt-1.pyc

File Content: `decomp_lu.cpython-35.opt-1.pyc`