English

Convergence Rates for Regularized Optimal Transport via Quantization

Optimization and Control 2023-06-22 v3 Probability Machine Learning

Abstract

We study the convergence of divergence-regularized optimal transport as the regularization parameter vanishes. Sharp rates for general divergences including relative entropy or LpL^{p} regularization, general transport costs and multi-marginal problems are obtained. A novel methodology using quantization and martingale couplings is suitable for non-compact marginals and achieves, in particular, the sharp leading-order term of entropically regularized 2-Wasserstein distance for all marginals with finite (2+δ)(2+\delta)-moment.

Keywords

Cite

@article{arxiv.2208.14391,
  title  = {Convergence Rates for Regularized Optimal Transport via Quantization},
  author = {Stephan Eckstein and Marcel Nutz},
  journal= {arXiv preprint arXiv:2208.14391},
  year   = {2023}
}

Comments

Forthcoming in 'Mathematics of Operations Research'

R2 v1 2026-06-28T00:25:29.210Z