[SciPy-Dev] Global Optimization Benchmarks

Sat Jan 9 03:30:34 EST 2021

On Sat, 9 Jan 2021 at 09.12, Andrea Gavana <andrea.gavana at gmail.com> wrote:

> Hi Daniel,
>
> On Sat, 9 Jan 2021 at 08.37, Daniel Schmitz <
> danielschmitzsiegen at googlemail.com> wrote:
>
>> Awesome work, Andrea!
>>
>
> Thank you, I’m happy you liked it :-) . I have been told to try and
> condense the work in a paper and submit it to the Journal of Global
> Optimization, although I’m currently doubting that such a prestigious
> publication will accept a publication from a nobody like me.
>
>
>> Would it be possible for you to make your implementations of mcs and
>> biteopt publicly available?
>>
>
> For BiteOpt I might be able to - after a bit of polishing, nothing major.
> For both I’ll have to ask for permission from my employer, as it was a
> little tour the force getting MCS right.
>

That said, there is a publicly available Python wrapper for BiteOpt here:

https://github.com/leonidk/biteopt

I haven’t used it myself, I decided to rewrite the algorithm in Python as I
wanted to understand what the code was doing and I wanted to add a couple
of (minor) modifications in order to try and make the solver more efficient.

Andrea.

>
> And more out of curiosity, not directly related to scipy: since you work
>> in an industry setting, did you compare these open source optimizers
>> against commercial ones like knitro?
>>
>
> Unfortunately we don’t have any commercial solver available - as far as I
> know - but I would love to redo the work using other solvers (open source
> or not). The very interesting exercise from Sahinidis et al:
>
> https://link.springer.com/content/pdf/10.1007/s10898-012-9951-y.pdf
>
> Although limited to a single set of benchmarks, show that commercial
> solvers like Tomlab are very efficient, tightly followed by MCS. It would
> be nice to try and apply the solvers in KNITRO, Tomlab, GlobSol (others?)
> to all the 16 benchmarks to see where open source stands, but I have no
> idea how it could be done as I have no licenses for those.
>
> Andrea.
>
>
>
>> Cheers,
>>
>> Daniel
>>
>> On Fri, 8 Jan 2021 at 12:28, Ralf Gommers <ralf.gommers at gmail.com> wrote:
>>
>>>
>>>
>>> On Fri, Jan 8, 2021 at 12:20 PM Andrea Gavana <andrea.gavana at gmail.com>
>>> wrote:
>>>
>>>> Hi Ralf,
>>>>
>>>> On Fri, 8 Jan 2021 at 12:15, Ralf Gommers <ralf.gommers at gmail.com>
>>>> wrote:
>>>>
>>>>>
>>>>>
>>>>> On Fri, Jan 8, 2021 at 11:35 AM Andrea Gavana <andrea.gavana at gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Hi Ralf,
>>>>>>
>>>>>> On Fri, 8 Jan 2021 at 11:07, Ralf Gommers <ralf.gommers at gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> On Fri, Jan 8, 2021 at 10:21 AM Andrea Gavana <
>>>>>>> andrea.gavana at gmail.com> wrote:
>>>>>>>
>>>>>>>> Dear SciPy Developers & Users,
>>>>>>>>
>>>>>>>>     long time no see :-) . I thought to start 2021 with a bit of a
>>>>>>>> bang, to try and forget how bad 2020 has been... So I am happy to present
>>>>>>>> you with a revamped version of the Global Optimization Benchmarks from my
>>>>>>>> previous exercise in 2013.
>>>>>>>>
>>>>>>>
>>>>>>> Hi Andrea, this is awesome! Thanks for sharing!
>>>>>>>
>>>>>>
>>>>>> I am happy you like it :-) .
>>>>>>
>>>>>>
>>>>>>
>>>>>>> This could be really useful to link to and use as a guide for
>>>>>>> providing recommendations for solvers to use in the scipy.optimize
>>>>>>> tutorials. It's good to see that SciPy overall is much more competitive
>>>>>>> than it was in 2013. Overall it seems SHGO is our most accurate solver, and
>>>>>>> making it faster seems worthwhile. That shouldn't be very difficult, given
>>>>>>> that it's all pure Python still.
>>>>>>>
>>>>>>
>>>>>> I have to say that, compared to back in 2013, the addition of SHGO
>>>>>> and DualAnnealing to SciPy has made the global optimization world in SciPy
>>>>>> much more powerful, pretty much at the top of what can currently be done
>>>>>> with open source solvers.
>>>>>>
>>>>>>
>>>>>>> MCS isn't open source, but both DIRECT and BiteOpt are MIT-licensed
>>>>>>> and seem the best candidates to be considered for inclusion in SciPy.
>>>>>>>
>>>>>>
>>>>>> I couldn't find a license restriction for MCS, but maybe I haven't
>>>>>> looked hard enough... Do you have a link for it? I am just curious.
>>>>>>
>>>>>
>>>>> MCS itself doesn't contain any license information, but it depends on
>>>>> MINQ which has a link in "All versions of MINQ are licensed" on this page:
>>>>> https://www.mat.univie.ac.at/~neum/software/minq/. It's only free for
>>>>> non-commercial use.
>>>>>
>>>>
>>>>
>>>> Ah, OK, thank you, I didn't think about that. Of course, assuming SciPy
>>>> had another, different "bound constrained indefinite quadratic programming"
>>>> module then we could easily swap it :-) .
>>>>
>>>
>>> MCS and MINQ are from the same author, so I'd expect the same
>>> restriction to apply to MCS though. We could ask for permission to license
>>> all that under BSD/MIT, sometimes that works - the author seems like the
>>> typical academic who doesn't understand open source licensing. In the past
>>> we've had success with explaining; given how much extra exposure/users MCS
>>> gets if it would be included in SciPy, it may be worth doing if someone is
>>> motivated to work on integrating MCS into SciPy.
>>>
>>> Cheers,
>>> Ralf
>>>
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