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

Understand

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.