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# scipy.linalg.cholesky

`scipy.linalg.``cholesky`(a, lower=0, overwrite_a=0)

  Compute the Cholesky decomposition of a matrix.

  Returns the Cholesky decomposition, :lm:`A = L L^*` or :lm:`A = U^* U` of a Hermitian positive-definite matrix :lm:`A`.

  Parameters: | a : array, shape (M, M) Matrix to be decomposed lower : boolean Whether to compute the upper or lower triangular Cholesky factorization (Default: upper-triangular) overwrite_a : boolean Whether to overwrite data in a (may improve performance)
  Returns: | B : array, shape (M, M) Upper- or lower-triangular Cholesky factor of A Raises LinAlgError if decomposition fails :

  Examples

      >>> from scipy import array, linalg, dot
      >>> a = array([[1,-2j],[2j,5]])
      >>> L = linalg.cholesky(a, lower=True)
      >>> L
      array([[ 1.+0.j,  0.+0.j],
             [ 0.+2.j,  1.+0.j]])
      >>> dot(L, L.T.conj())
      array([[ 1.+0.j,  0.-2.j],
             [ 0.+2.j,  5.+0.j]])

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