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# scipy.linalg.lu

`scipy.linalg.``lu`(a, permute_l=0, overwrite_a=0)

  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 : array, shape (M, N) Array to decompose permute_l : boolean Perform the multiplication P*L (Default: do not permute) overwrite_a : boolean Whether to overwrite data in a (may improve performance)
  Returns: | (If permute_l == False) : p : array, shape (M, M) Permutation matrix l : array, shape (M, K) Lower triangular or trapezoidal matrix with unit diagonal. K = min(M, N) u : array, shape (K, N) Upper triangular or trapezoidal matrix (If permute_l == True) : pl : array, shape (M, K) Permuted L matrix. K = min(M, N) u : array, shape (K, N) Upper triangular or trapezoidal matrix

  Notes

  This is a LU factorization routine written for Scipy.

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