[SciPy-Dev] Consideration of differential evolution minimizer being added to scipy.optimize.

[Petr Baudis]

  Hi!

On Tue, Mar 04, 2014 at 09:42:36PM +0100, Ralf Gommers wrote:
> Andrea Gavana has posted a nice set of benchmarks before:
> http://article.gmane.org/gmane.comp.python.scientific.devel/18383, you
> could contact him to add your algorithm (or do a similar comparison
> yourself). Seeing your code in a comparison like
> http://infinity77.net/global_optimization/multidimensional.html would be
> useful.

  Another interesting benchmark might be the COCO benchmark of BBOB
workshops which is often used in academia for global optimization
performance comparisons:

	http://coco.gforge.inria.fr/doku.php

Though it focuses on black-box optimization.  I plan to publish a
performance graph for all SciPy's optimizers wrapped in basinhopping
as benchmarked within COCO after the end of March (a month of deadlines
for me), if noone beats me to it.

  (My long-term work focuses on online portfolio algorithms, i.e. such
that can dynamically switch between minimization methods based on their
performance so far when optimizing the function.  My hope is to
eventually find some that could be beneficial enough to be worth
including in SciPy.  A work-in-progress framework I'm using so far is
https://github.com/pasky/cocopf )

> Another question is if we think this is in scope for scipy.optimize, given
> that PyGMO has this same algorithm and a number of similar ones.

  I know that as SciPy user, I would appreciate having at least a single
reference, high-performance population-based algorithm in scipy.optimize.
Whether to go with the contributed DE code or use some more
sophisticated approach to choose a suitable one (I believe the top
state-of-art are the CMA-ES variants?), I don't know.

				Petr "Pasky" Baudis