[SciPy-Dev] GSoC SciPy Optimization Ideas

[Mazen Sayed]

Hey Dr. Schmitz,

For the first point, yes that is what I mean, First we'll reformulate the
problem to unconstrained, then we will use an existing  algorithm.

For the second point, I was thinking of generating 50 random points using
the user defined bounds, then substitute in the objective, and choose the
initial that minimizes our goal.

Thanks,
Mazen




On Sun, Apr 11, 2021 at 3:09 PM Daniel Schmitz <
danielschmitzsiegen at googlemail.com> wrote:

> Hey Sayed,
>
> my two cents, not being a CoreDeveloper but a python developer interested
> in Optimization algorithms.
>
> The automatic reformulation of the constrained problem into an
> unconstrained problem sounds similar to nlopt's augmented lagrangian:
> https://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/#augmented-lagrangian-algorithm
> . I think this would be a great addition to scipy.optimize. I imagine that
> you would pass the reformulated objective to minimize then and just reuse
> the existing algorithms.
>
> One objection to your idea about "smart initialization": why exactly 50
> points and how exactly would they be sampled if no bounds are provided?
> Theoretically, a grid search over samples generated by for example latin
> hypercube sampling within a bounded volume could be a better initialization
> than a random guess. But I am not sure that this is in many cases a good
> idea. If you have no idea how to initialize your optimizer, I would go for
> one of the global optimizers.
>
> Best,
>
> Daniel
>
> On Sun, 11 Apr 2021 at 14:41, Mazen Sayed <sayedmazen70 at gmail.com> wrote:
>
>> Dear,
>>
>> I hope this email finds you well, this is my proposal for scipy.optimize
>> project, I'm really interested to work on this project.
>>
>> Thanks
>>
>>
>> https://drive.google.com/file/d/12Q6NnorN74VkuQw_HRx2kuY-FIoK90V0/view?usp=sharing
>>
>>
>>
>>
>>
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