[SciPy-User] Optimisation of a rough function

[Dominique Orban]

> Date: Sat, 2 Apr 2011 11:41:17 +0200
> Subject: Re: [SciPy-User] Optimisation of a rough function
> On Fri, Apr 01, 2011 at 11:04:15PM -0700, David Baddeley wrote:
>> there are enough optimisation gurus here that hopefully someone might
>> have some ideas. I'm trying to optimise a goal function that has a well
>> defined minimum, but also a certain amount of roughness (see attached
>> figure for an example). Most of the time (80%) optimize.fmin seems to
>> work, but it sometimes gets stuck in the roughness. optimise.anneal
>> works, but needs many more function evaluations, making it a bit
>> impractical (the goal is to be semi-interactive & the fmin
>> implementation already struggles). The goal function is quite complex
>> and already written in c. I'm really after some form of solver which
>> will skip over all the little bumps, but still benefits from using the
>> gradient information. Should probably also mention that it will usually
>> have ~ 10 parameters, rather than the 2 parameter case shown, but that
>> the same features should be present.

You have a noisy optimization problem. Not sure if it will solve your
problem but you want to look into implicit filtering:
http://www4.ncsu.edu/~ctk/imfil.html

(don't know if there's a Python version of this).

The method is described in Tim Kelley's book:

http://www.siam.org/books/kelley/fr18/index.php

-- 
Dominique