Implement Shubert-Piyavskii for multimodal deterministic global optimization

[Luis-Varona]

The Shubert-Piyavskii method is a deterministic global optimization algorithm applicable to multimodal functions that are Lipschitz-continuous. Unlike Brent's method and golden-section search, Shubert-Piyavskii is equally effective for functions with several modes, only requiring a Lipschitz constant (equivalent to having a bounded derivative for differentiable functions). It is also capable of provably coming within an arbitrary epsilon of the true global maximum/minimum. If possible, I'd like to implement it and create a PR.

[stefan-k]

Hi @Luis-Varona , this sounds interesting! Feel free to create a PR! Unfortunately my time currently is limited therefore I cannot guarantee a timely review, but I will do my best.

[Luis-Varona]

Sure, will do! :)

[Luis-Varona]

Just finishing up the test suite and docs :)

[Luis-Varona]

PR is now complete at https://github.com/argmin-rs/argmin/pull/588 and ready for review. No need to rush to review it, of course :) Hope this is useful!

[Luis-Varona]

Just added bugfixes due to some failed CI tests; #588 should all be finalized running smoothly now.