On the Convergence of Projected Alternating Maximization for Equitable and Optimal Transport
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Abstract (arXiv)
This paper studies the equitable and optimal transport (EOT) problem, which has many applications such as fair division problems and optimal transport with multiple agents etc. In the discrete distributions case, the EOT problem can be formulated as a linear program (LP). Since this LP is prohibitively large for general LP solvers, Scetbon etal citescetbon2021equitable suggests to perturb the problem by adding an entropy regularization. They proposed a projected alternating maximization algorithm (PAM) to solve the dual of the entropy regularized EOT. In this paper, we provide the first convergence analysis of PAM. A novel rounding procedure is proposed to help construct the primal solution for the original EOT problem. We also propose a variant of PAM by incorporating the extrapolation technique that can numerically improve the performance of PAM. Results in this paper may shed lights on block coordinate (gradient) descent methods for general optimization problems.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 15 of its 30 references that are in the map. First posted on arXiv 29 Sep 2021.
Builds on
15 of 30 references.
References · 15
- 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,277
- 2017A Distributional Perspective on Reinforcement Learningcited by 743
- 2017Near-linear time approximation algorithms for optimal transport via Sinkhorn iterationcited by 219
- 2018Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn's Algorithmcited by 111
- 2019Subspace Robust Wasserstein Distancescited by 89
- 2019Wasserstein Dependency Measure for Representation Learningcited by 69
- 2017Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descentcited by 66
- 2020Projection Robust Wasserstein Distance and Riemannian Optimizationcited by 45
- 2018Towards Optimal Transport with Global Invariancescited by 33
- 2020A Riemannian Block Coordinate Descent Method for Computing the Projection Robust Wasserstein Distancecited by 27
- 2013Iteration Complexity Analysis of Block Coordinate Descent Methodscited by 17
- 2018Alternating Randomized Block Coordinate Descentcited by 8
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Citing papers per year: 2022: 1; 2023: 1; 2024: 1.
3 papers.