Optimistix Versions Save

Nonlinear optimisation (root-finding, least squares, ...) in JAX+Equinox. https://docs.kidger.site/optimistix/

v0.0.7

2 weeks ago

Features

  • Support for complex numbers! In theory these should now be supported everywhere. In practice we're still considering this a little experimental at the moment, just in case we've missed something. (Huge thanks to @Randl! #53)
  • optimistix.{AbstractGaussNewton, GaussNewton, LevenbergMarquardt, IndirectLevenbergMarquardt, Dogleg} should now all support using reverse-mode autodiff to calculate Jacobians. (#51)

Bugfixes

Other

  • Documentation fixes. (Thanks @ColCarroll! #32)
  • Now compatible with jax_numpy_rank_promotion=raise and jax_numpy_dtype_promotion=strict.

New Contributors

Full Changelog: https://github.com/patrick-kidger/optimistix/compare/v0.0.6...v0.0.7

v0.0.6

5 months ago

Bugfix release!

  • Fixed recompilation happening on every step when iterating step-by-step with a Gauss--Newton solver. (#30)
  • sol.state previously included only the array-valued parts of the state (for sol = optx.{minimise, least_squares, root_find, fixed_point}(...)). It now includes everything.
  • Fixed optx.internal.implicit_jvp misbehaving for non-Optimistix use cases.
  • Fixed optx.{Newton,Chord}(cauchy_termination=False) failing when started close to the solution.

Full Changelog: https://github.com/patrick-kidger/optimistix/compare/v0.0.5...v0.0.6

v0.0.5

7 months ago

Very simple release!

  • Added optimistix.compat.minimize as a replacement for jax.scipy.optimize.minimize. (#14)

Full Changelog: https://github.com/patrick-kidger/optimistix/compare/v0.0.4...v0.0.5

v0.0.4

7 months ago

Hurrah! How exciting.