Yanir A. Rubinstein

2papers

2 Papers

NAApr 20, 2016
Optimal transport via a Monge-Ampère optimization problem

Michael Lindsey, Yanir A. Rubinstein

We rephrase Monge's optimal transportation (OT) problem with quadratic cost--via a Monge-Ampère equation--as an infinite-dimensional optimization problem, which is in fact a convex problem when the target is a log-concave measure with convex support. We define a natural finite-dimensional discretization to the problem and associate a piecewise affine convex function to the solution of this discrete problem. The discrete problems always admit a solution, which can be obtained by standard convex optimization algorithms whenever the target is a log-concave measure with convex support. We show that under suitable regularity conditions the convex functions retrieved from the discrete problems converge to the convex solution of the original OT problem furnished by Brenier's theorem. Also, we put forward an interpretation of our convergence result that suggests applicability to the convergence of a wider range of numerical methods for OT. Finally, we demonstrate the practicality of our convergence result by providing visualizations of OT maps as well as of the dynamic OT problem obtained by solving the discrete problem numerically.

46.2FAApr 10
Quantitative Stability and Numerical Resolution of the Moment Measure Problem

Guillaume Bonnet, Yanir A. Rubinstein

The moment measure problem consists in finding a convex function $ψ$ whose moment measure, i.e., the pushforward by $\nabla ψ$ of the measure with density $e^{-ψ(\,\cdot\,)}$, is prescribed. It is highly non-linear and less understood than the related optimal transport problem. We establish a quantitative stability estimate for this problem. This estimate validates, as well as leads us to introduce, an approach to the numerical resolution of the moment measure problem inspired by semi-discrete optimal transport, consisting in approximating the prescribed measure by a finitely supported one. We describe a Newton method for solving the discrete problem thus obtained, and perform numerical experiments, studying the experimental rates of convergence of the approximation beyond the predictions of the stability estimate.