patrick-kidger/optimistix
Nonlinear optimisation (root-finding, least squares, ...) in JAX+Equinox. https://docs.kidger.site/optimistix/
Optimistix helps developers using JAX with advanced numerical computations by providing tools for solving complex mathematical problems like root-finding, minimization, and least squares. It takes mathematical functions and equations as input and produces precise solutions for these problems, enabling efficient development of scientific computing and machine learning applications. This tool is for JAX developers, machine learning engineers, and researchers who need robust and flexible optimization capabilities.
553 stars. Used by 2 other packages. Actively maintained with 3 commits in the last 30 days. Available on PyPI.
Use this if you are a JAX developer needing to implement sophisticated nonlinear solvers for tasks such as implicit integration, constrained optimization, or finding fixed points in your models.
Not ideal if you are looking for simple, out-of-the-box machine learning optimizers like SGD or Adam without needing the deep customizability of nonlinear solvers.
Stars
553
Forks
45
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 09, 2026
Commits (30d)
3
Dependencies
5
Reverse dependents
2
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