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Nearly Tight Convergence Bounds for Semi-discrete Entropic Optimal Transport

Artificial Intelligence 2022-05-05 v2 Numerical Analysis Numerical Analysis Statistics Theory Statistics Theory

Abstract

We derive nearly tight and non-asymptotic convergence bounds for solutions of entropic semi-discrete optimal transport. These bounds quantify the stability of the dual solutions of the regularized problem (sometimes called Sinkhorn potentials) w.r.t. the regularization parameter, for which we ensure a better than Lipschitz dependence. Such facts may be a first step towards a mathematical justification of annealing or ε\varepsilon-scaling heuristics for the numerical resolution of regularized semi-discrete optimal transport. Our results also entail a non-asymptotic and tight expansion of the difference between the entropic and the unregularized costs.

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Cite

@article{arxiv.2110.12678,
  title  = {Nearly Tight Convergence Bounds for Semi-discrete Entropic Optimal Transport},
  author = {Alex Delalande},
  journal= {arXiv preprint arXiv:2110.12678},
  year   = {2022}
}