Optimal Transport on Discrete Domains

TL;DR

This paper reviews recent advances in scalable numerical optimal transport methods for discrete domains, combining PDE and convex analysis insights to handle large-scale problems in graphics and machine learning.

Contribution

It synthesizes state-of-the-art techniques that enable efficient solutions to large-scale discrete optimal transport problems, highlighting theoretical foundations and open research challenges.

Findings

Scalable algorithms solve large optimal transport problems efficiently.

Integration of PDE and convex analysis improves model robustness.

Identification of open problems guides future research directions.

Abstract

Inspired by the matching of supply to demand in logistical problems, the optimal transport (or Monge--Kantorovich) problem involves the matching of probability distributions defined over a geometric domain such as a surface or manifold. In its most obvious discretization, optimal transport becomes a large-scale linear program, which typically is infeasible to solve efficiently on triangle meshes, graphs, point clouds, and other domains encountered in graphics and machine learning. Recent breakthroughs in numerical optimal transport, however, enable scalability to orders-of-magnitude larger problems, solvable in a fraction of a second. Here, we discuss advances in numerical optimal transport that leverage understanding of both discrete and smooth aspects of the problem. State-of-the-art techniques in discrete optimal transport combine insight from partial differential equations (PDE) with convex analysis to reformulate, discretize, and optimize transportation problems. The end result is a set of theoretically-justified models suitable for domains with thousands or millions of vertices. Since numerical optimal transport is a relatively new discipline, special emphasis is placed on identifying and explaining open problems in need of mathematical insight and additional research.