DPOT: A DeepParticle method for Computation of Optimal Transport with convergence guarantee

• URL: http://arxiv.org/abs/2506.23429v1
• Date: Sun, 29 Jun 2025 23:32:39 GMT
• Title: DPOT: A DeepParticle method for Computation of Optimal Transport with convergence guarantee
• Authors: Yingyuan Li, Aokun Wang, Zhongjian Wang,
• Abstract summary: We propose a novel machine learning approach to compute the optimal transport map between two continuous distributions from their unpaired samples.<n>The proposed method leads to a min-min optimization during training and does not impose any restriction on the network structure.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this work, we propose a novel machine learning approach to compute the optimal transport map between two continuous distributions from their unpaired samples, based on the DeepParticle methods. The proposed method leads to a min-min optimization during training and does not impose any restriction on the network structure. Theoretically we establish a weak convergence guarantee and a quantitative error bound between the learned map and the optimal transport map. Our numerical experiments validate the theoretical results and the effectiveness of the new approach, particularly on real-world tasks.

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