Rieoptax: Riemannian Optimization in JAX
Optimization and Control
2022-10-11 v1 Machine Learning
Mathematical Software
Abstract
We present Rieoptax, an open source Python library for Riemannian optimization in JAX. We show that many differential geometric primitives, such as Riemannian exponential and logarithm maps, are usually faster in Rieoptax than existing frameworks in Python, both on CPU and GPU. We support various range of basic and advanced stochastic optimization solvers like Riemannian stochastic gradient, stochastic variance reduction, and adaptive gradient methods. A distinguishing feature of the proposed toolbox is that we also support differentially private optimization on Riemannian manifolds.
Cite
@article{arxiv.2210.04840,
title = {Rieoptax: Riemannian Optimization in JAX},
author = {Saiteja Utpala and Andi Han and Pratik Jawanpuria and Bamdev Mishra},
journal= {arXiv preprint arXiv:2210.04840},
year = {2022}
}