NeurIPS 2023poster4 citations
Bringing regularized optimal transport to lightspeed: a splitting method adapted for GPUs
Jacob Lindbäck, Zesen Wang, Mikael Johansson
Abstract
We present an efficient algorithm for regularized optimal transport. In contrast to previous methods, we use the Douglas-Rachford splitting technique to develop an efficient solver that can handle a broad class of regularizers. The algorithm has strong global convergence guarantees, low per-iteration cost, and can exploit GPU parallelization, making it considerably faster than the state-of-the-art for many problems. We illustrate its competitiveness in several applications, including domain adaptation and learning of generative models.
optimal transportdomain adaptationsplitting methodsgpu computations
BibTeX
@inproceedings{
lindb{\"a}ck2023bringing,
title={Bringing regularized optimal transport to lightspeed: a splitting method adapted for {GPU}s},
author={Jacob Lindb{\"a}ck and Zesen Wang and Mikael Johansson},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=bmdnWIuypV}
}