On Sun, Jan 30, 2011 at 04:15:34PM +0100, Sturla Molden wrote: > Den 30.01.2011 07:28, skrev Algis Kabaila: > > Why not simply numply.linalg.cond? This gives the condition > > number directly (and presumably performs the inspection of > > sv's). Or do you think that sv's give more useful information?
> You can use the singular value decomposition to invert the matrix, solve > linear systems and solve least squares problems. Looking at the topic > you don't just want to compute condition numbers, but invert the > ill-conditioned (nearly singular) matrix. And if you are trying to solve a least-squares, I think that you should be using a ridge (or Tikhonov) regularisation: http://en.wikipedia.org/wiki/Tikhonov_regularization read in particular the paragraph above the table of content: you are most likely interested in Gamma = alpha identity, where you set alpha to be say 1% (or .1%) of the largest eigenvalue of A^t A. Gael _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
