arXiv · 2305.18483
Bringing regularized optimal transport to lightspeed: a splitting method adapted for GPUs
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.
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Jacob Lindbäck, Zesen Wang, Mikael Johansson. 2023-05-29. Bringing regularized optimal transport to lightspeed: a splitting method adapted for GPUs. https://arxiv.org/abs/2305.18483
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