scipy.linalg.cholesky¶
- scipy.linalg.cholesky(a, lower=False, overwrite_a=False, check_finite=True)[source]¶
Compute the Cholesky decomposition of a matrix.
Returns the Cholesky decomposition, \(A = L L^*\) or \(A = U^* U\) of a Hermitian positive-definite matrix A.
- Parameters
- a(M, M) array_like
Matrix to be decomposed
- lowerbool, optional
Whether to compute the upper- or lower-triangular Cholesky factorization. Default is 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
Upper- or lower-triangular Cholesky factor of a.
- Raises
- LinAlgErrorif decomposition fails.
Examples
>>> from scipy.linalg import cholesky >>> a = np.array([[1,-2j],[2j,5]]) >>> L = cholesky(a, lower=True) >>> L array([[ 1.+0.j, 0.+0.j], [ 0.+2.j, 1.+0.j]]) >>> L @ L.T.conj() array([[ 1.+0.j, 0.-2.j], [ 0.+2.j, 5.+0.j]])