[SciPy-Dev] GSoC SciPy Optimization Ideas

[Daniel Schmitz]

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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