# Asymptotic distribution and convergence rates of stochastic algorithms for entropic optimal transportation between probability measures

## TL;DR

This paper analyzes the convergence and distribution of stochastic algorithms for estimating entropic optimal transportation costs, specifically Sinkhorn divergences, using a Robbins-Monro approach with theoretical guarantees and practical experiments.

## Contribution

It establishes almost sure convergence, asymptotic normality, and convergence rates for a new recursive estimator of Sinkhorn divergence in semi-discrete and discrete settings.

## Key findings

- Proves almost sure convergence of the estimator.
- Derives asymptotic normality results.
- Provides numerical experiments demonstrating effectiveness.

## Abstract

This paper is devoted to the stochastic approximation of entropically regularized Wasserstein distances between two probability measures, also known as Sinkhorn divergences. The semi-dual formulation of such regularized optimal transportation problems can be rewritten as a non-strongly concave optimisation problem. It allows to implement a Robbins-Monro stochastic algorithm to estimate the Sinkhorn divergence using a sequence of data sampled from one of the two distributions. Our main contribution is to establish the almost sure convergence and the asymptotic normality of a new recursive estimator of the Sinkhorn divergence between two probability measures in the discrete and semi-discrete settings. We also study the rate of convergence of the expected excess risk of this estimator in the absence of strong concavity of the objective function. Numerical experiments on synthetic and real datasets are also provided to illustrate the usefulness of our approach for data analysis.