title: Numpy Routine for Computing Matrix Minors?
body: <p>I'm interested in using numpy to compute all of the minors of a given square matrix.  Is there a slick way of using array slicing to do this?  I'm imagining that one can rotate the columns, delete the last column, rotate the rows of the resulting matrix and delete the last row, but I haven't found anything in the numpy documentation that indicates this is possible.</p>
  
  <p>(Q:  Why do this?  A: I have a long sequence {M_n} of fairly large matrices, roughly 1,000,000 10,000 x 10,000 matrices, and I want to compute the determinant of each matrix.  Each matrix is obtained from its predecessor by changing just one coefficient.  It's going to be quite a lot faster to compute the determinant of the first matrix in the sequence, and then compute the difference det(M_{n+1}) - det(M_n), which is the product of the changed coefficient and its minor.)</p>