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https://github.com/patrick-kidger/optimistix
Nonlinear optimisation (root-finding, least squares, ...) in JAX+Equinox. https://docs.kidger.site/optimistix/
https://github.com/patrick-kidger/optimistix
deep-learning equinox jax neural-networks optimisation optimization
Last synced: 25 days ago
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Nonlinear optimisation (root-finding, least squares, ...) in JAX+Equinox. https://docs.kidger.site/optimistix/
- Host: GitHub
- URL: https://github.com/patrick-kidger/optimistix
- Owner: patrick-kidger
- License: apache-2.0
- Created: 2023-10-03T17:59:45.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-09-30T00:41:50.000Z (about 1 month ago)
- Last Synced: 2024-10-05T01:26:29.439Z (about 1 month ago)
- Topics: deep-learning, equinox, jax, neural-networks, optimisation, optimization
- Language: Python
- Homepage:
- Size: 843 KB
- Stars: 311
- Watchers: 8
- Forks: 15
- Open Issues: 31
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
Optimistix
Optimistix is a [JAX](https://github.com/google/jax) library for nonlinear solvers: root finding, minimisation, fixed points, and least squares.
Features include:
- interoperable solvers: e.g. autoconvert root find problems to least squares problems, then solve using a minimisation algorithm.
- modular optimisers: e.g. use a BFGS quadratic bowl with a dogleg descent path with a trust region update.
- using a PyTree as the state.
- fast compilation and runtimes.
- interoperability with [Optax](https://github.com/deepmind/optax).
- all the benefits of working with JAX: autodiff, autoparallelism, GPU/TPU support etc.
## Installation
```bash
pip install optimistix
```
Requires Python 3.9+ and JAX 0.4.14+ and [Equinox](https://github.com/patrick-kidger/equinox) 0.11.0+.
## Documentation
Available at [https://docs.kidger.site/optimistix](https://docs.kidger.site/optimistix).
## Quick example
```python
import jax.numpy as jnp
import optimistix as optx
# Let's solve the ODE dy/dt=tanh(y(t)) with the implicit Euler method.
# We need to find y1 s.t. y1 = y0 + tanh(y1)dt.
y0 = jnp.array(1.)
dt = jnp.array(0.1)
def fn(y, args):
return y0 + jnp.tanh(y) * dt
solver = optx.Newton(rtol=1e-5, atol=1e-5)
sol = optx.fixed_point(fn, solver, y0)
y1 = sol.value # satisfies y1 == fn(y1)
```
## Citation
If you found this library to be useful in academic work, then please cite: ([arXiv link](https://arxiv.org/abs/2402.09983))
```bibtex
@article{optimistix2024,
title={Optimistix: modular optimisation in JAX and Equinox},
author={Jason Rader and Terry Lyons and Patrick Kidger},
journal={arXiv:2402.09983},
year={2024},
}
```
## See also: other libraries in the JAX ecosystem
**Always useful**
[Equinox](https://github.com/patrick-kidger/equinox): neural networks and everything not already in core JAX!
[jaxtyping](https://github.com/patrick-kidger/jaxtyping): type annotations for shape/dtype of arrays.
**Deep learning**
[Optax](https://github.com/deepmind/optax): first-order gradient (SGD, Adam, ...) optimisers.
[Orbax](https://github.com/google/orbax): checkpointing (async/multi-host/multi-device).
[Levanter](https://github.com/stanford-crfm/levanter): scalable+reliable training of foundation models (e.g. LLMs).
**Scientific computing**
[Diffrax](https://github.com/patrick-kidger/diffrax): numerical differential equation solvers.
[Lineax](https://github.com/patrick-kidger/lineax): linear solvers.
[BlackJAX](https://github.com/blackjax-devs/blackjax): probabilistic+Bayesian sampling.
[sympy2jax](https://github.com/patrick-kidger/sympy2jax): SymPy<->JAX conversion; train symbolic expressions via gradient descent.
[PySR](https://github.com/milesCranmer/PySR): symbolic regression. (Non-JAX honourable mention!)
**Awesome JAX**
[Awesome JAX](https://github.com/n2cholas/awesome-jax): a longer list of other JAX projects.
## Credit
Optimistix was primarily built by Jason Rader (@packquickly): [Twitter](https://twitter.com/packquickly); [GitHub](https://github.com/packquickly); [Website](https://www.packquickly.com/).