375 lines
12 KiB
Python
375 lines
12 KiB
Python
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"""LU decomposition functions."""
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from warnings import warn
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from numpy import asarray, asarray_chkfinite
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import numpy as np
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from itertools import product
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# Local imports
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from ._misc import _datacopied, LinAlgWarning
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from .lapack import get_lapack_funcs
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from ._decomp_lu_cython import lu_dispatcher
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lapack_cast_dict = {x: ''.join([y for y in 'fdFD' if np.can_cast(x, y)])
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for x in np.typecodes['All']}
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__all__ = ['lu', 'lu_solve', 'lu_factor']
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def lu_factor(a, overwrite_a=False, check_finite=True):
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"""
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Compute pivoted LU decomposition of a matrix.
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The decomposition is::
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A = P L U
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where P is a permutation matrix, L lower triangular with unit
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diagonal elements, and U upper triangular.
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Parameters
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----------
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a : (M, N) array_like
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Matrix to decompose
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overwrite_a : bool, optional
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Whether to overwrite data in A (may increase performance)
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check_finite : bool, optional
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Whether to check that the input matrix contains only finite numbers.
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Disabling may give a performance gain, but may result in problems
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(crashes, non-termination) if the inputs do contain infinities or NaNs.
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Returns
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-------
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lu : (M, N) ndarray
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Matrix containing U in its upper triangle, and L in its lower triangle.
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The unit diagonal elements of L are not stored.
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piv : (K,) ndarray
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Pivot indices representing the permutation matrix P:
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row i of matrix was interchanged with row piv[i].
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Of shape ``(K,)``, with ``K = min(M, N)``.
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See Also
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--------
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lu : gives lu factorization in more user-friendly format
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lu_solve : solve an equation system using the LU factorization of a matrix
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Notes
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-----
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This is a wrapper to the ``*GETRF`` routines from LAPACK. Unlike
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:func:`lu`, it outputs the L and U factors into a single array
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and returns pivot indices instead of a permutation matrix.
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While the underlying ``*GETRF`` routines return 1-based pivot indices, the
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``piv`` array returned by ``lu_factor`` contains 0-based indices.
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Examples
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--------
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>>> import numpy as np
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>>> from scipy.linalg import lu_factor
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>>> A = np.array([[2, 5, 8, 7], [5, 2, 2, 8], [7, 5, 6, 6], [5, 4, 4, 8]])
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>>> lu, piv = lu_factor(A)
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>>> piv
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array([2, 2, 3, 3], dtype=int32)
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Convert LAPACK's ``piv`` array to NumPy index and test the permutation
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>>> def pivot_to_permutation(piv):
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... perm = np.arange(len(piv))
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... for i in range(len(piv)):
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... perm[i], perm[piv[i]] = perm[piv[i]], perm[i]
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... return perm
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...
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>>> p_inv = pivot_to_permutation(piv)
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>>> p_inv
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array([2, 0, 3, 1])
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>>> L, U = np.tril(lu, k=-1) + np.eye(4), np.triu(lu)
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>>> np.allclose(A[p_inv] - L @ U, np.zeros((4, 4)))
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True
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The P matrix in P L U is defined by the inverse permutation and
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can be recovered using argsort:
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>>> p = np.argsort(p_inv)
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>>> p
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array([1, 3, 0, 2])
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>>> np.allclose(A - L[p] @ U, np.zeros((4, 4)))
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True
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or alternatively:
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>>> P = np.eye(4)[p]
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>>> np.allclose(A - P @ L @ U, np.zeros((4, 4)))
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True
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"""
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if check_finite:
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a1 = asarray_chkfinite(a)
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else:
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a1 = asarray(a)
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overwrite_a = overwrite_a or (_datacopied(a1, a))
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getrf, = get_lapack_funcs(('getrf',), (a1,))
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lu, piv, info = getrf(a1, overwrite_a=overwrite_a)
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if info < 0:
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raise ValueError('illegal value in %dth argument of '
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'internal getrf (lu_factor)' % -info)
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if info > 0:
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warn("Diagonal number %d is exactly zero. Singular matrix." % info,
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LinAlgWarning, stacklevel=2)
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return lu, piv
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def lu_solve(lu_and_piv, b, trans=0, overwrite_b=False, check_finite=True):
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"""Solve an equation system, a x = b, given the LU factorization of a
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Parameters
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----------
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(lu, piv)
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Factorization of the coefficient matrix a, as given by lu_factor.
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In particular piv are 0-indexed pivot indices.
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b : array
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Right-hand side
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trans : {0, 1, 2}, optional
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Type of system to solve:
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===== =========
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trans system
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===== =========
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0 a x = b
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1 a^T x = b
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2 a^H x = b
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===== =========
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overwrite_b : bool, optional
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Whether to overwrite data in b (may increase performance)
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check_finite : bool, optional
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Whether to check that the input matrices contain only finite numbers.
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Disabling may give a performance gain, but may result in problems
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(crashes, non-termination) if the inputs do contain infinities or NaNs.
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Returns
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-------
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x : array
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Solution to the system
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See Also
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--------
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lu_factor : LU factorize a matrix
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Examples
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--------
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>>> import numpy as np
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>>> from scipy.linalg import lu_factor, lu_solve
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>>> A = np.array([[2, 5, 8, 7], [5, 2, 2, 8], [7, 5, 6, 6], [5, 4, 4, 8]])
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>>> b = np.array([1, 1, 1, 1])
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>>> lu, piv = lu_factor(A)
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>>> x = lu_solve((lu, piv), b)
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>>> np.allclose(A @ x - b, np.zeros((4,)))
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True
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"""
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(lu, piv) = lu_and_piv
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if check_finite:
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b1 = asarray_chkfinite(b)
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else:
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b1 = asarray(b)
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overwrite_b = overwrite_b or _datacopied(b1, b)
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if lu.shape[0] != b1.shape[0]:
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raise ValueError(f"Shapes of lu {lu.shape} and b {b1.shape} are incompatible")
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getrs, = get_lapack_funcs(('getrs',), (lu, b1))
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x, info = getrs(lu, piv, b1, trans=trans, overwrite_b=overwrite_b)
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if info == 0:
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return x
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raise ValueError('illegal value in %dth argument of internal gesv|posv'
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% -info)
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def lu(a, permute_l=False, overwrite_a=False, check_finite=True,
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p_indices=False):
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"""
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Compute LU decomposition of a matrix with partial pivoting.
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The decomposition satisfies::
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A = P @ L @ U
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where ``P`` is a permutation matrix, ``L`` lower triangular with unit
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diagonal elements, and ``U`` upper triangular. If `permute_l` is set to
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``True`` then ``L`` is returned already permuted and hence satisfying
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``A = L @ U``.
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Parameters
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----------
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a : (M, N) array_like
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Array to decompose
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permute_l : bool, optional
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Perform the multiplication P*L (Default: do not permute)
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overwrite_a : bool, optional
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Whether to overwrite data in a (may improve performance)
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check_finite : bool, optional
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Whether to check that the input matrix contains only finite numbers.
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Disabling may give a performance gain, but may result in problems
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(crashes, non-termination) if the inputs do contain infinities or NaNs.
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p_indices : bool, optional
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If ``True`` the permutation information is returned as row indices.
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The default is ``False`` for backwards-compatibility reasons.
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Returns
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-------
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**(If `permute_l` is ``False``)**
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p : (..., M, M) ndarray
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Permutation arrays or vectors depending on `p_indices`
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l : (..., M, K) ndarray
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Lower triangular or trapezoidal array with unit diagonal.
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``K = min(M, N)``
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u : (..., K, N) ndarray
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Upper triangular or trapezoidal array
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**(If `permute_l` is ``True``)**
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pl : (..., M, K) ndarray
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Permuted L matrix.
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``K = min(M, N)``
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u : (..., K, N) ndarray
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Upper triangular or trapezoidal array
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Notes
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-----
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Permutation matrices are costly since they are nothing but row reorder of
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``L`` and hence indices are strongly recommended to be used instead if the
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permutation is required. The relation in the 2D case then becomes simply
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``A = L[P, :] @ U``. In higher dimensions, it is better to use `permute_l`
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to avoid complicated indexing tricks.
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In 2D case, if one has the indices however, for some reason, the
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permutation matrix is still needed then it can be constructed by
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``np.eye(M)[P, :]``.
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Examples
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--------
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>>> import numpy as np
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>>> from scipy.linalg import lu
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>>> A = np.array([[2, 5, 8, 7], [5, 2, 2, 8], [7, 5, 6, 6], [5, 4, 4, 8]])
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>>> p, l, u = lu(A)
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>>> np.allclose(A, p @ l @ u)
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True
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>>> p # Permutation matrix
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array([[0., 1., 0., 0.], # Row index 1
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[0., 0., 0., 1.], # Row index 3
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[1., 0., 0., 0.], # Row index 0
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[0., 0., 1., 0.]]) # Row index 2
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>>> p, _, _ = lu(A, p_indices=True)
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>>> p
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array([1, 3, 0, 2]) # as given by row indices above
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>>> np.allclose(A, l[p, :] @ u)
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True
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We can also use nd-arrays, for example, a demonstration with 4D array:
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>>> rng = np.random.default_rng()
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>>> A = rng.uniform(low=-4, high=4, size=[3, 2, 4, 8])
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>>> p, l, u = lu(A)
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>>> p.shape, l.shape, u.shape
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((3, 2, 4, 4), (3, 2, 4, 4), (3, 2, 4, 8))
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>>> np.allclose(A, p @ l @ u)
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True
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>>> PL, U = lu(A, permute_l=True)
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>>> np.allclose(A, PL @ U)
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True
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"""
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a1 = np.asarray_chkfinite(a) if check_finite else np.asarray(a)
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if a1.ndim < 2:
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raise ValueError('The input array must be at least two-dimensional.')
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# Also check if dtype is LAPACK compatible
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if a1.dtype.char not in 'fdFD':
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dtype_char = lapack_cast_dict[a1.dtype.char]
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if not dtype_char: # No casting possible
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raise TypeError(f'The dtype {a1.dtype} cannot be cast '
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'to float(32, 64) or complex(64, 128).')
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a1 = a1.astype(dtype_char[0]) # makes a copy, free to scratch
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overwrite_a = True
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*nd, m, n = a1.shape
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k = min(m, n)
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real_dchar = 'f' if a1.dtype.char in 'fF' else 'd'
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# Empty input
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if min(*a1.shape) == 0:
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if permute_l:
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PL = np.empty(shape=[*nd, m, k], dtype=a1.dtype)
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U = np.empty(shape=[*nd, k, n], dtype=a1.dtype)
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return PL, U
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else:
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P = (np.empty([*nd, 0], dtype=np.int32) if p_indices else
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np.empty([*nd, 0, 0], dtype=real_dchar))
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L = np.empty(shape=[*nd, m, k], dtype=a1.dtype)
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U = np.empty(shape=[*nd, k, n], dtype=a1.dtype)
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return P, L, U
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# Scalar case
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if a1.shape[-2:] == (1, 1):
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if permute_l:
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return np.ones_like(a1), (a1 if overwrite_a else a1.copy())
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else:
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P = (np.zeros(shape=[*nd, m], dtype=int) if p_indices
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else np.ones_like(a1))
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return P, np.ones_like(a1), (a1 if overwrite_a else a1.copy())
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# Then check overwrite permission
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if not _datacopied(a1, a): # "a" still alive through "a1"
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if not overwrite_a:
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# Data belongs to "a" so make a copy
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a1 = a1.copy(order='C')
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# else: Do nothing we'll use "a" if possible
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# else: a1 has its own data thus free to scratch
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# Then layout checks, might happen that overwrite is allowed but original
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# array was read-only or non-contiguous.
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if not (a1.flags['C_CONTIGUOUS'] and a1.flags['WRITEABLE']):
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a1 = a1.copy(order='C')
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if not nd: # 2D array
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p = np.empty(m, dtype=np.int32)
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u = np.zeros([k, k], dtype=a1.dtype)
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lu_dispatcher(a1, u, p, permute_l)
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P, L, U = (p, a1, u) if m > n else (p, u, a1)
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else: # Stacked array
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# Prepare the contiguous data holders
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P = np.empty([*nd, m], dtype=np.int32) # perm vecs
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if m > n: # Tall arrays, U will be created
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U = np.zeros([*nd, k, k], dtype=a1.dtype)
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for ind in product(*[range(x) for x in a1.shape[:-2]]):
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lu_dispatcher(a1[ind], U[ind], P[ind], permute_l)
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L = a1
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else: # Fat arrays, L will be created
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L = np.zeros([*nd, k, k], dtype=a1.dtype)
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for ind in product(*[range(x) for x in a1.shape[:-2]]):
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lu_dispatcher(a1[ind], L[ind], P[ind], permute_l)
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U = a1
|
||
|
|
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||
|
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# Convert permutation vecs to permutation arrays
|
||
|
|
# permute_l=False needed to enter here to avoid wasted efforts
|
||
|
|
if (not p_indices) and (not permute_l):
|
||
|
|
if nd:
|
||
|
|
Pa = np.zeros([*nd, m, m], dtype=real_dchar)
|
||
|
|
# An unreadable index hack - One-hot encoding for perm matrices
|
||
|
|
nd_ix = np.ix_(*([np.arange(x) for x in nd]+[np.arange(m)]))
|
||
|
|
Pa[(*nd_ix, P)] = 1
|
||
|
|
P = Pa
|
||
|
|
else: # 2D case
|
||
|
|
Pa = np.zeros([m, m], dtype=real_dchar)
|
||
|
|
Pa[np.arange(m), P] = 1
|
||
|
|
P = Pa
|
||
|
|
|
||
|
|
return (L, U) if permute_l else (P, L, U)
|