@inproceedings{
rader2023lineax,
title={Lineax: unified linear solves and linear least-squares in {JAX} and Equinox},
author={Jason Rader and Terry Lyons},
booktitle={NeurIPS 2023 AI for Science Workshop},
year={2023},
url={https://openreview.net/forum?id=uWx1utwxxw}
}
We introduce Lineax, a library bringing linear solves and linear least-squares to the JAX+Equinox scientific computing ecosystem. Lineax uses general linear operators, and unifies linear solves and least-squares into a single, autodifferentiable API. Solvers and operators are user-extensible, without requiring the user to implement any custom derivative rules to get differentiability. Lineax is available at https://github.com/$\textbf{anonymised}$/lineax.
Lineax: unified linear solves and linear least-squares in JAX and Equinox
Jason Michael Rader Terry Lyons Patrick Kidger