mlsanityData to 7 Oct 2026

On the Convergence of Projected Alternating Maximization for Equitable and Optimal Transport

arXiv 2109.15030 ↗First posted 29 Sep 2021math.OC cs.LGcited by 3

Not on the map. Why?

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

  1. 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,277
  2. 2017A Distributional Perspective on Reinforcement Learningcited by 743
  3. 2017Near-linear time approximation algorithms for optimal transport via Sinkhorn iterationcited by 219
  4. 2018Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn's Algorithmcited by 111
  5. 2019Subspace Robust Wasserstein Distancescited by 89
  6. 2019Wasserstein Dependency Measure for Representation Learningcited by 69
  7. 2017Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descentcited by 66
  8. 2020Projection Robust Wasserstein Distance and Riemannian Optimizationcited by 45
  9. 2018Towards Optimal Transport with Global Invariancescited by 33
  10. 2020A Riemannian Block Coordinate Descent Method for Computing the Projection Robust Wasserstein Distancecited by 27
  11. 2013Iteration Complexity Analysis of Block Coordinate Descent Methodscited by 17
  12. 2018Alternating Randomized Block Coordinate Descentcited by 8
3 more
  1. 2021Projection Robust Wasserstein Barycenterscited by 8
  2. 2015Improved Iteration Complexity Bounds of Cyclic Block Coordinate Descent for Convex Problemscited by 5
  3. 2015Allocating Indivisible Items in Categorized Domainscited by 2

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 1.

3 papers.