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 -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.
Keywords
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}
}