lu(a, permute_l=False, overwrite_a=False, check_finite=True)
The decomposition is:
A = P L U
where P is a permutation matrix, L lower triangular with unit diagonal elements, and U upper triangular.
This is a LU factorization routine written for SciPy.
Array to decompose
Perform the multiplication P*L (Default: do not permute)
Whether to overwrite data in a (may improve performance)
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.
Permutation matrix
Lower triangular or trapezoidal matrix with unit diagonal. K = min(M, N)
Upper triangular or trapezoidal matrix
Permuted L matrix. K = min(M, N)
Upper triangular or trapezoidal matrix
Compute pivoted LU decomposition of a matrix.
>>> from scipy.linalg import luSee :
... A = np.array([[2, 5, 8, 7], [5, 2, 2, 8], [7, 5, 6, 6], [5, 4, 4, 8]])
... p, l, u = lu(A)
... np.allclose(A - p @ l @ u, np.zeros((4, 4))) True
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.linalg._decomp_ldl.ldl
scipy.linalg._decomp_lu.lu
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