[SciPy-Dev] Introducing scipy.optimization.tgo; a global optimization routine.

[Stefan Endres]

>Unfortunately when I tried to run the BenchGlobal class today I found a
few issues (which may be related to use of python3) which need to be looked
into.
The unittests in ( test__tgo.py
<https://github.com/Stefan-Endres/tgo/blob/master/test__tgo.py>) are at
least working in both Python 2 and 3, but I can imagine there would be
quite a few issues, is there any way _ to try and fix it?



> a maximum number of function evaluations and a convergence test against a
known global minimum - i.e., by passing a known global minimum as an input
argument to the global optimization algorithm, the algorithm would stop if
the current function value is close enough to the input global minimum.
This should be easy enough to do at least once it's over 2000 since the
local if we only need to evaluate after each local scipy.optimize.minimize
return since those function evaulations , but I'm not sure how to terminate
exactly at 2000 if scipy.optimize.minimize



On 6 April 2016 at 09:11, Andrea Gavana <andrea.gavana at gmail.com> wrote:

> Hi,
>
>
> On Wednesday, 6 April 2016, Andrew Nelson wrote:
>
>> Stefan, thanks for your willingness to contribute to Scipy.
>>
>> There is collection of global minimization problems (>130) in the scipy
>> benchmarking suite. In large part they are based on the Andrea's excellent
>> work.
>> I'd like to see the algorithm run against that collection of problems to
>> check that its performance is good.
>> Unfortunately when I tried to run the BenchGlobal class today I found a
>> few issues (which may be related to use of python3) which need to be looked
>> into.
>>
>> Needless to say, all added functionality needs to have a comprehensive
>> set of tests (not just successful use on benchmarking problems).
>>
>> A.
>>
>> _____________________________________
>> Dr. Andrew Nelson
>>
>>
>> _____________________________________
>>
>
>
> In addition to Andrew's comments,  it would be nice to have some
> termination criteria in TGO: all the algorithms I have tested had (or I
> hacked them to support) a maximum number of function evaluations and a
> convergence test against a known global minimum - i.e., by passing a known
> global minimum as an input argument to the global optimization algorithm,
> the algorithm would stop if the current function value is close enough to
> the input global minimum.
>
> If you take a look at The Rules here:
>
> http://infinity77.net/global_optimization/
>
> You can see why it's so important sometimes to have more than one
> termination criteria...
>
> I am absolutely not familiar with the Python code of TGO, so I am a bit
> reluctant to hack it to add these termination criteria... Any chance we can
> get them in somehow? 😊
>
> Andrea.
>
>
>
> --
>
>
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev at scipy.org
> https://mail.scipy.org/mailman/listinfo/scipy-dev
>
>


-- 
Stefan Endres
Postgraduate Student: Institute of Applied Materials
Department of Chemical Engineering, University of Pretoria
Cell: +27 (0) 82 972 42 89
E-mail: Stefan.C.Endres at gmail.com
St. Number: 11004968
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scipy-dev/attachments/20160406/138d2bb2/attachment.html>