[SciPy-Dev] GSoC SciPy Optimization Ideas

[Mazen Sayed]

Hello,

I have changed the normalizer to be a user specified argument, also I have
changed the initializer so the user can enter multiple data points, and the
initializer will take the best initial point (that minimize the
unconstrained problem), or the user can let the initializer guess the
initial point by using random search, grid search or global optimizer.

Any modification before submission?

Thanks

https://drive.google.com/file/d/1c9JOJgcq_Ss761rt9SOxnmEtuczuLzAb/view?usp=sharing



On Sun, Apr 11, 2021 at 7:26 PM Mazen Sayed <sayedmazen70 at gmail.com> wrote:

>
> This sounds really good and more reasonable.
>
> Thanks for your help.
>
>
>
> On Sun, Apr 11, 2021 at 7:08 PM Pamphile Roy <roy.pamphile at gmail.com>
> wrote:
>
>> I tend to agree with Daniel here, randomly choosing 50 points in a
>> high-dimensional optimization space is not going to give any advantage. And
>> why 50?
>>
>> The initialization part is one of the most important (and difficult to
>> get right) part of any optimization algorithm, but this is mostly true for
>> global ones: differential evolution, SHGO, Dual Annealing they’re all have
>> their own way. Some of these and many others (especially local algorithms)
>> rely on the user to explicitly pass an initial guess and take it from there.
>>
>>
>> Agreed here, the random sampling must not be totally random in order to
>> cover the parameter space in the most efficient way.
>> The global optimizers all use QMC methods (scipy.stats.qmc) so here you
>> can just rely on these too.
>>
>> Cheers,
>> Pamphile
>>
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>
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