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# scipy.linalg.lu

`scipy.linalg.``lu`(a, permute_l=False, overwrite_a=False, check_finite=True)

  Compute pivoted LU decompostion of a matrix.

  The decomposition is:

      A = P L U

  where P is a permutation matrix, L lower triangular with unit diagonal elements, and U upper triangular.

  Parameters : | a : (M, N) array_like Array to decompose permute_l : bool Perform the multiplication P*L (Default: do not permute) overwrite_a : bool Whether to overwrite data in a (may improve performance) check_finite : boolean, optional Whether to check the input matrixes contain only finite numbers. Disabling may give a performance gain, but may result to problems (crashes, non-termination) if the inputs do contain infinities or NaNs.
  Returns : | **(If permute_l == False)** : p : (M, M) ndarray Permutation matrix l : (M, K) ndarray Lower triangular or trapezoidal matrix with unit diagonal. K = min(M, N) u : (K, N) ndarray Upper triangular or trapezoidal matrix **(If permute_l == True)** : pl : (M, K) ndarray Permuted L matrix. K = min(M, N) u : (K, N) ndarray Upper triangular or trapezoidal matrix

  Notes

  This is a LU factorization routine written for Scipy.

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