[SciPy-User] Optimization, Matlab/Octave and Duplication
Sun Nov 20 22:37:06 CST 2011
On Sun, Nov 20, 2011 at 18:42, Felipe Schneider
> Hi all,
> I'm new to SciPy and even scientific computing in general, but I have
> some basic experience with Octave. Reading about Python and how it can
> be amazing but still very fast and all, I started to get some interest
> on SciPy/NumPy. I would like to ask some questions, hoping it's the
> right place to make them:
> 1. It seems Octave is really slow compared to the SciPy approach. I
> would like to know if it's due to some low level coding, Cython, etc.
> How's the procedure when someone wants to, say, create a new routine
> for SciPy?
Octave is based on C. Scipy is based mostly on Fortran libraries
(LAPACK, BLAS), which in numerical calculations is much faster than C.
> 2. Is there anything similar to Matlab's Toolboxes or Octave's
> Octave-Forge? Or is it all a huge pack?
Have you looked at Sage? It is basically Mathematica, but with Numpy,
etc, as a backend.
> 3. I was searching for a LP solver and it seems SciPy doesn't have it!
> But there's cvxopt, am I wrong? So, there's no future plans on this
> area, I presume, i. e., no LP solver for SciPy? I would like to have a
> general answer (i. e., "when and when not should SciPy has this/that
> funtionality?"), which leads to my third question...
Sage has that as far as I recall.
> 4. It seems that there are way more than one implemention in the
> Python world for a lot of things, am I wrong? How come? Why
> reinventing the wheel? Where does SciPy stand on this matter?
Python is build around usability and readability of the code. There
are many ways to do one thing so many different people can use and
many different paths lead to the same result. Stiff programming
boundaries are a pain for new user to get into, but python tries to
eliminate that. Scipy as far as I can tell is a little stiffer than
regular python because of the specific function that it calls, but
with python being the language you use it is much for flexible than
lets say F77.
> Really thanks,
> SciPy-User mailing list
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