scipy.linalg.cho_factor#

scipy.linalg.cho_factor(a, lower=False, overwrite_a=False, check_finite=True)[source]#

Compute the Cholesky decomposition of a matrix, to use in cho_solve

Returns a matrix containing the Cholesky decomposition, A = L L* or A = U* U of a Hermitian positive-definite matrix a. The return value can be directly used as the first parameter to cho_solve.

Warning

The returned matrix also contains random data in the entries not used by the Cholesky decomposition. If you need to zero these entries, use the function cholesky instead.

Parameters

a(M, M) array_like: Matrix to be decomposed
lowerbool, optional: Whether to compute the upper or lower triangular Cholesky factorization (Default: upper-triangular)
overwrite_abool, optional: Whether to overwrite data in a (may improve performance)
check_finitebool, 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

c(M, M) ndarray: Matrix whose upper or lower triangle contains the Cholesky factor of a. Other parts of the matrix contain random data.
lowerbool: Flag indicating whether the factor is in the lower or upper triangle

Raises

LinAlgError: Raised if decomposition fails.

See also

cho_solve: Solve a linear set equations using the Cholesky factorization of a matrix.

Examples

>>> from scipy.linalg import cho_factor
>>> A = np.array([[9, 3, 1, 5], [3, 7, 5, 1], [1, 5, 9, 2], [5, 1, 2, 6]])
>>> c, low = cho_factor(A)
>>> c
array([[3.        , 1.        , 0.33333333, 1.66666667],
       [3.        , 2.44948974, 1.90515869, -0.27216553],
       [1.        , 5.        , 2.29330749, 0.8559528 ],
       [5.        , 1.        , 2.        , 1.55418563]])
>>> np.allclose(np.triu(c).T @ np. triu(c) - A, np.zeros((4, 4)))
True

scipy.linalg.cholesky_banded

scipy.linalg.cho_solve