title: Does Scipy recognize the special structure of this matrix to decompose it faster?
body: <p>I have a matrix whose many rows are already in the upper triangular form. I would like to ask if the command <code>scipy.linalg.lu</code> recognize this special structure to faster decompose it. If I decompose this matrix on paper, I only use Gaussian elimination on those rows that are not in the upper triangular form. For example, I will only make transformations on the last row of matrix <code>B</code>.</p>
  
  <pre><code>import numpy as np
  A = np.array([[2, 5, 8, 7, 8],
                [5, 2, 2, 8, 9],
                [7, 5, 6, 6, 10],
                [5, 4, 4, 8, 10]])
  B = np.array([[2, 5, 8, 7, 8],
                [0, 2, 2, 8, 9],
                [0, 0, 6, 6, 10],
                [5, 4, 4, 8, 10]])
  </code></pre>
  
  <p>Because my square matrix is of very large dimension and this procedure is repeated thousands of times. I would like to make use of this special structure to reduce the computational complexity.</p>
  
  <p>Thank you so much for your elaboration!</p>