Newton's method for optimal transport problem on graphs

TL;DR

This paper develops a Newton's method tailored for dynamical optimal transport on graphs, addressing issues of cycle redundancies and density positivity, and demonstrates its effectiveness across various graph types.

Contribution

Introduces a finite-difference Newton method with a spanning-tree gauge to solve nonlinear optimal transport systems on graphs, ensuring well-posedness and positivity.

Findings

Reduces cycle redundancies via a spanning-tree gauge.

Guarantees density positivity with upwind discretization and CFL condition.

Successfully applies to lattices, random graphs, and social networks.

Abstract

In this paper, we study dynamical optimal transport on a connected graph from the perspective of the Benamou-Brenier formulation, where densities are assigned to vertices and velocities to edges. However, directly using Newton's method on the resulting nonlinear systems encounters two potential difficulties: (i) if the graph contains cycles, edge variables are not unique, and (ii) there is no guarantee that the density variables remain positive. To address these challenges, we introduce a finite-difference-type Newton method that eliminates cycle-induced redundancies through a spanning-tree gauge, resulting in a reduced set of independent variables and a well-posed, sparse linear system. For the lattice graph arising from the continuous optimal transport problem, density positivity can also be guaranteed by using an upwind discretization subject to a CFL-type condition. We further demonstrate the versatility of the proposed scheme by applying it to a range of problems, including optimal transport on lattices and random graphs, inverse optimal transport problems, and social network analysis.