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

[Andrea Gavana]

On Tuesday, March 4, 2014, Andrea Gavana <andrea.gavana at gmail.com> wrote:

> Hi,
>
> On Tuesday, March 4, 2014, Petr Baudis  wrote:
>
>>   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 )
>
>
>
> I like your approach, hopefully the published results for the
> benchmarks will include the number of function evaluations as the most
> prominent parameter instead of the usual, math-standard (and completely
> useless) CPU time/elapsed time/runtime for Alan algorithm.
>

Oh I hate this autocorrect thing... "Alan" should read "an".



>
>
>> > 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.
>>
>>
> I have run my benchmark against CMA-ES as well, you can see the comparison
> results here:
>
> http://infinity77.net/global_optimization/multidimensional.html
>
> The current Python wrapper for CMA-ES does not work for Univariate
> problems.
>
> Andrea.
>
>
>
>>                                 Petr "Pasky" Baudis
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>>
>
>
> --
>
>

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