Cholesky decomposition.
Return the Cholesky decomposition, A = L L^* of a Hermitian positive-definite matrix A.
Parameters : | a : array_like, shape (M, M)
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Returns : | L : array_like, shape (M, M)
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Raises : | LinAlgError :
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Notes
The Cholesky decomposition is often used as a fast way of solving
A \mathbf{x} = \mathbf{b}.
First, we solve for \mathbf{y} in
L \mathbf{y} = \mathbf{b},
and then for \mathbf{x} in
L^* \mathbf{x} = \mathbf{y}.
Examples
>>> A = np.array([[1,-2j],[2j,5]])
>>> L = np.linalg.cholesky(A)
>>> L
array([[ 1.+0.j, 0.+0.j],
[ 0.+2.j, 1.+0.j]])
>>> np.dot(L, L.T.conj())
array([[ 1.+0.j, 0.-2.j],
[ 0.+2.j, 5.+0.j]])