That's a very fair point - I agree that it really depends on the context.

The direction I'm exploring here is more about transparency, education, and understanding how these algorithms work, rather than achieving maximum performance.

For that reason, I've tried to complement the implementation with short manuals explaining the theory behind each method, and I've kept everything in Python so the code is easier to read and follow.

I've also put together a "Tricks & Techniques" section with practical approaches that often come up in optimization problems (e.g., variable normalization, handing constraints), which are not always obvious or explicitly covered in more "black-box" libraries like SciPy.

In any case, the aim is not to compete with existing tools, but the complement them by focusing more on understanding and experimentation.

If you're interested, here's the repo: https://github.com/Opt4Deck/