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

[Andrew Nelson]

The tests in test__tgo.py mostly look like benchmarks. For an example of
the kind of tests that are required see test__differentialevolution.py in
the scipy.optimize module.
On 6 Apr 2016 5:25 pm, "Stefan Endres" <stefan.c.endres at gmail.com> wrote:

> >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.
>>
>>
>>
>> --
>>
>>
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>>
>>
>
>
> --
> 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
>
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>
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