scipy.linalg.lu¶
- scipy.linalg.lu(a, permute_l=False, overwrite_a=False, check_finite=True)[source]¶
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.