Bringing regularized optimal transport to lightspeed: a splitting method adapted for GPUs

Jacob Lindbäck, Zesen Wang, Mikael Johansson

Keywords: optimal transport, domain adaptation, splitting methods, gpu computations

TL;DR: We propose a flexible splitting method that can solve regularized OT problems fast on GPU.

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.

Submission Number: 12002