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# numpy.linalg.cholesky

`numpy.linalg.``cholesky`(a)

  Cholesky decomposition.

  Return the Cholesky decomposition, of a Hermitian positive-definite matrix .

  Parameters: | a : array_like, shape (M, M) Hermitian (symmetric, if it is real) and positive definite input matrix.
  Returns: | L : array_like, shape (M, M) Lower-triangular Cholesky factor of A.
  Raises: | LinAlgError : If the decomposition fails.

  Notes

  The Cholesky decomposition is often used as a fast way of solving

  First, we solve for in

  and then for in

  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]])

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