Here is the GitHub: https://github.com/patrick-kidger/optimistix

The elevator pitch is Optimistix is really fast, especially to compile. It plays nicely with Optax for first-order gradient-based methods, and takes a lot of design inspiration from Equinox (https://github.com/patrick-kidger/equinox), representing the state of all the solvers as standard JAX PyTrees.

For those familiar with classical nonlinear unconstrained optimisation, Optimistix does some pretty nifty new things. It introduces new abstractions for modular optimisers, allowing users can mix-and-match different optimisation techniques easily. For example, creating a BFGS optimiser with Levenberg-Marquardt style Tikhnov dampning takes less than 10 lines of code in Optimistix.

I'm using Optimistix as a tool for my own research, and continue to work on it as part of my PhD (supervised by Patrick Kidger.) I would love for some more people to try it, so let me know what you think!