> > 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
First of all I want to thank all who have contributed to this discussion. It has been nothing less than inspiring! However, it has drifted to areas in which I lack expertese and interest. My interest is in structural analysis of engineering structures. The structure response is generally characterised by a square matrix with real elements. Actually, the structural engineer has no interest in trying to invert a singular matrix. However he/she is interested (or should be interested :) ) when the square response matrix might approach singularity for this would signal instability. He/She knows what the result of instability would be - a disaster! It is my fault not to have stated the problem with adequate clarity and I intend to do that as soon as I can. Thank you again for all your valuable contributions. Al. -- Algis http://akabaila.pcug.org.au/StructuralAnalysis.pdf _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
