Add OptimizationFrameworks/clnlbeam.jmd

[odow]

This is a model similar to the OPF example, except that it is much simpler and has no type stability issues. This makes it a simpler candidate for benchmarking during development.

It is taken from the JuMP docs: https://jump.dev/JuMP.jl/stable/tutorials/nonlinear/simple_examples/#The-clnlbeam-problem
As a goal, we're looking to solve the N = 1000 case, although the jmd file only goes up to N = 200.

Am I meant to update the Manifest.toml? How does that work.

I get a graph like this, but I was doing stuff on my machine while it executed (and it's really hot here, so the fan was going full bore), so results may differ. At the very least, it's a good candidate model for testing the structural simplification of MTK, and it teases apart different differentiation implementations.

Checklist

• Appropriate tests were added
• Any code changes were done in a way that does not break public API
• All documentation related to code changes were updated
• The new code follows the
  contributor guidelines, in particular the SciML Style Guide and
  COLPRAC.
• Any new documentation only uses public API

Additional context

Add any other context about the problem here.

[odow]

That seemed to work. Here's the CI plot:

[ChrisRackauckas]

Awesome, thanks! While you got this running, can you also share a quick StatProfilerHTML on the AutoSparseReverseDiff(true) one on one of the bigger sizes to see if that confirms overhead dominated by sparsity detection?

[odow]

Seems like almost all the time is spent in hessian_sparsity, so we're on the right track:

[ChrisRackauckas]

Awesome, yup that's the benchmark we've been looking for.

[odow]

Yeah there's too much other stuff going on in OPF. But an improvement here should translate very nicely.

…

On Mon, 22 Jan 2024, 10:21 pm Christopher Rackauckas, < ***@***.***> wrote: Awesome, yup that's the benchmark we've been looking for. — Reply to this email directly, view it on GitHub <#836 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AB6MQJIFBVK5PPWKRSVOXSLYPYVSXAVCNFSM6AAAAABCEJYSF6VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSMBTGU3TCOBXGA> . You are receiving this because you authored the thread.Message ID: ***@***.***>