From Schrodinger Bridge to Optimal Transport over Sub-Riemannian Manifolds

TL;DR

This paper develops a numerically feasible approach to optimal transport constrained by sub-Riemannian geometry, using entropic regularization and Schrödinger bridges, with practical algorithms and numerical demonstrations.

Contribution

It introduces a regularized formulation and a Sinkhorn-type algorithm for sub-Riemannian optimal transport, bridging stochastic and deterministic methods.

Findings

Smooth, strictly positive transition densities under bracket-generating hypotheses

A practical Sinkhorn-type algorithm for Schrödinger potentials

Recovery of deterministic optimal transport as noise vanishes

Abstract

We study the least-energy way to reshape a probability distribution when motion is constrained to a horizontal bundle, that is, optimal transport and distribution steering in sub-Riemannian geometry, motivated by density control over underactuated systems. To obtain a continuous and numerically tractable formulation, we introduce an entropic regularization by adding small noise aligned with the control directions and study the associated Schrodinger bridge problem. The resulting reference process is a degenerate diffusion on the sub-Riemannian manifold. Under bracket-generating hypotheses we obtain smooth, strictly positive transition densities and a forward--backward characterization of the optimal bridge. This leads to a practical Sinkhorn-type algorithm for the Schrodinger potentials and, as the noise level vanishes, a recovery of the deterministic sub-Riemannian optimal transport problem. We demonstrate with a numerical example.