Convergence rates for discretized Monge-Amp\`ere equations and quantitative stability of optimal transport

TL;DR

This paper provides a quantitative analysis of the convergence rates for discretized solutions to Monge-Ampère equations, which approximate optimal transport maps, with results applicable to measures with bounded convex support and periodic settings.

Contribution

It introduces explicit L^2 convergence rates for discretized Monge-Ampère solutions, advancing the understanding of numerical approximation of optimal transport maps.

Findings

L^2 convergence rates depend on spatial resolution h

Results apply to measures with bounded convex support

Periodic variants of convergence are established

Abstract

In recent works - both experimental and theoretical - it has been shown how to use computational geometry to efficently construct approximations to the optimal transport map between two given probability measures on Euclidean space, by discretizing one of the measures. Here we provide a quantative convergence analysis for the solutions of the corresponding discretized Monge-Amp\`ere equations. This yields L^{2}-converge rates, in terms of the corresponding spatial resolution h, of the discrete approximations of the optimal transport map, when the source measure is discretized and the target measure has bounded convex support. Periodic variants of the results are also established. The proofs are based on quantitative stability results for optimal transport maps, shown using complex geometry.