On Apr 29, 5:17 pm, someone <newsbo...@gmail.com> wrote: > On 04/30/2012 12:39 AM, Kiuhnm wrote: > > >> So Matlab at least warns about "Matrix is close to singular or badly > >> scaled", which python (and I guess most other languages) does not... > > > A is not just close to singular: it's singular! > > Ok. When do you define it to be singular, btw? > > >> Which is the most accurate/best, even for such a bad matrix? Is it > >> possible to say something about that? Looks like python has a lot more > >> digits but maybe that's just a random result... I mean.... Element 1,1 = > >> 2.81e14 in Python, but something like 3e14 in Matlab and so forth - > >> there's a small difference in the results... > > > Both results are *wrong*: no inverse exists. > > What's the best solution of the two wrong ones? Best least-squares > solution or whatever? > > >> With python, I would also kindly ask about how to avoid this problem in > >> the future, I mean, this maybe means that I have to check the condition > >> number at all times before doing anything at all ? How to do that? > > > If cond(A) is high, you're trying to solve your problem the wrong way. > > So you're saying that in another language (python) I should check the > condition number, before solving anything? > > > You should try to avoid matrix inversion altogether if that's the case. > > For instance you shouldn't invert a matrix just to solve a linear system. > > What then? > > Cramer's rule?
If you really want to know just about everything there is to know about a matrix, take a look at its Singular Value Decomposition (SVD). I've never used numpy, but I assume it can compute an SVD. -- http://mail.python.org/mailman/listinfo/python-list
