[Matrix-SIG] Nonlinear optimization routines anyone?

[Paul F. Dubois]

The conjugate gradient algorithm is probably about twenty lines or less of
matrix/vector statements in Python, assuming you have a preconditioner you
can express that way. So just code it up in Python. It will be fast enough,
all the hard work is in the dot products and matrix multiplies.

----Original Message-----
From: David Ascher <da@ski.org>
To: Janne Sinkkonen <janne@avocado.pc.helsinki.fi>
Cc: matrix-sig@python.org <matrix-sig@python.org>
Date: Monday, March 15, 1999 9:46 AM
Subject: Re: [Matrix-SIG] Nonlinear optimization routines anyone?


>> does anybody has neatly packaged nonlinear optimization routines
>> implemented in NumPy? I'd like to have either conjugate gradients or
>> BFGS. Explicitly calculating the Hessian is out of question (because
>> of the size and complexity of the problem).
>>
>> I thought translating part of the matlab codes of C. T. Kelley
>> (http://www4.ncsu.edu/eos/users/c/ctkelley/www/matlab_darts.html) to
>> Numerical Python unless something already implemented emerges.
>
>I've never found a routine to package, and I've looked a fair bit. There's
>COOOL (http://timna.mines.edu/cwpcodes/coool/) which looked interesting,
>but it seemed extremely non-portable (couldn't get it to compile on Win32,
>requires expect, etc. etc.).
>
>--david
>
>
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