title: numpy SVD giving wrong values?
body: <p>I am trying to get accustomed to doing singular value decomposition with numpy. I decided to do the SVD on a matrix from an example to understand how it works. <a href="http://www.d.umn.edu/~mhampton/m4326svd_example.pdf" rel="nofollow noreferrer">I am following this pdf</a>, where A = [[3, 2, 2], [2, 3, -2]]. When I run the svd, however, I get something different for the matrices U and V then what is provided in the pdf. It is the same matrix, except the signs have been flipped. Now, since the matrices are both linear operators and the signs have been flipped on both it is technically still correct, the flipping cancels out. But why is it this way?</p>