[Numpy-discussion] inversion of large matrices

Anne Archibald aarchiba@physics.mcgill...
Tue Aug 31 13:29:51 CDT 2010

Hi Melissa,

On 30 August 2010 17:42, Melissa Mendonça <melissawm@gmail.com> wrote:

> I've been lurking for a while here but never really introduced myself.
> I'm a mathematician in Brazil working with optimization and numerical
> analysis and I'm looking into scipy/numpy basically because I want to
> ditch matlab.

Welcome to the list! I hope you will find the switch to numpy and
scipy rewarding above and beyond not-being-matlab. Please feel free to
ask questions on the list; as you've probably seen, we get lots of
questions with simple but non-obvious answers, and a few really tough

> I'm just curious as to why you say "scipy.linalg.solve(), NOT
> numpy.linalg.solve()". Can you explain the reason for this? I find
> myself looking for information such as this on the internet but I
> rarely find real documentation for these things, and I seem to have so
> many performance issues with python... I'm curious to see what I'm
> missing here.

I agree that the documentation is a little hard to find one's way
around sometimes. The numpy documentation project has done a wonderful
job of providing detailed documentation for all the functions in
numpy, but there's not nearly as much documentation giving a general
orientation (which functions are better, what the relationship is with
scipy and matplotlib). The scipy documentation project is
unfortunately still getting started.

What sorts of performance issues have you had? python has some
important limitations, but there are often ways to work around them.
Sometimes there isn't an efficient way to do things, though, and in
those cases we'd like to think about whether numpy/scipy can be
improved. (Bulk linear algebra - solving large numbers of small
problems - stands out as an example.) Numerical optimization is
another - we know the optimizers built into scipy have serious
limitations, and welcome ideas on improving them.

> Thanks, and sorry if I hijacked the thread,

No problem, and welcome again to the list.


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