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https://github.com/patrick-kidger/lineax
Linear solvers in JAX and Equinox. https://docs.kidger.site/lineax
https://github.com/patrick-kidger/lineax
equinox jax linear-algebra
Last synced: 26 days ago
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Linear solvers in JAX and Equinox. https://docs.kidger.site/lineax
- Host: GitHub
- URL: https://github.com/patrick-kidger/lineax
- Owner: patrick-kidger
- License: apache-2.0
- Created: 2023-05-09T18:36:57.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-09-29T23:56:47.000Z (about 1 month ago)
- Last Synced: 2024-10-05T01:26:19.896Z (about 1 month ago)
- Topics: equinox, jax, linear-algebra
- Language: Python
- Homepage:
- Size: 177 KB
- Stars: 345
- Watchers: 9
- Forks: 23
- Open Issues: 27
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
Lineax
Lineax is a [JAX](https://github.com/google/jax) library for linear solves and linear least squares. That is, Lineax provides routines that solve for $x$ in $Ax = b$. (Even when $A$ may be ill-posed or rectangular.)
Features include:
- PyTree-valued matrices and vectors;
- General linear operators for Jacobians, transposes, etc.;
- Efficient linear least squares (e.g. QR solvers);
- Numerically stable gradients through linear least squares;
- Support for structured (e.g. symmetric) matrices;
- Improved compilation times;
- Improved runtime of some algorithms;
- Support for both real-valued and complex-valued inputs;
- All the benefits of working with JAX: autodiff, autoparallelism, GPU/TPU support, etc.
## Installation
```bash
pip install lineax
```
Requires Python 3.9+, JAX 0.4.13+, and [Equinox](https://github.com/patrick-kidger/equinox) 0.11.0+.
## Documentation
Available at [https://docs.kidger.site/lineax](https://docs.kidger.site/lineax).
## Quick examples
Lineax can solve a least squares problem with an explicit matrix operator:
```python
import jax.random as jr
import lineax as lx
matrix_key, vector_key = jr.split(jr.PRNGKey(0))
matrix = jr.normal(matrix_key, (10, 8))
vector = jr.normal(vector_key, (10,))
operator = lx.MatrixLinearOperator(matrix)
solution = lx.linear_solve(operator, vector, solver=lx.QR())
```
or Lineax can solve a problem without ever materializing a matrix, as done in this
quadratic solve:
```python
import jax
import lineax as lx
key = jax.random.PRNGKey(0)
y = jax.random.normal(key, (10,))
def quadratic_fn(y, args):
return jax.numpy.sum((y - 1)**2)
gradient_fn = jax.grad(quadratic_fn)
hessian = lx.JacobianLinearOperator(gradient_fn, y, tags=lx.positive_semidefinite_tag)
solver = lx.CG(rtol=1e-6, atol=1e-6)
out = lx.linear_solve(hessian, gradient_fn(y, args=None), solver)
minimum = y - out.value
```
## Citation
If you found this library to be useful in academic work, then please cite: ([arXiv link](https://arxiv.org/abs/2311.17283))
```bibtex
@article{lineax2023,
title={Lineax: unified linear solves and linear least-squares in JAX and Equinox},
author={Jason Rader and Terry Lyons and Patrick Kidger},
journal={
AI for science workshop at Neural Information Processing Systems 2023,
arXiv:2311.17283
},
year={2023},
}
```
(Also consider starring the project on GitHub.)
## 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.
[Optimistix](https://github.com/patrick-kidger/optimistix): root finding, minimisation, fixed points, and least squares.
[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.