# Schr"odinger bridge with transport relaxation

arXiv: https://arxiv.org/abs/2602.08118 · HTML: https://mlsanity.com/paper/2602.08118/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2026.

## Built on by (most cited first, 2 of 2)

- [Learning Generative Dynamics with Soft Law Constraints: A McKean-Vlasov FBSDE Approach](https://mlsanity.com/paper/2605.08928/) (2026)
- [Deep-MKV-TS: Path-Dependent McKean--Vlasov Control for Financial Time Series Generation](https://mlsanity.com/paper/2608.19394/) (2026)

## Abstract (arXiv)

Motivated by modern machine learning applications where we only have access to empirical measures constructed from finite samples, we relax the marginal constraints of the classical Schr"odinger bridge problem by penalizing the transport cost between the bridge's marginals and the prescribed marginals. We derive a duality formula for this transport-relaxed bridge and demonstrate that it reduces to a finite-dimensional concave optimization problem when the prescribed marginals are discrete and the reference distribution is absolutely continuous. We establish the existence and uniqueness of solutions for both the primal and dual problems. Moreover, as the penalty blows up, we characterize the limiting bridge as the solution to a discrete Schr"odinger bridge problem and identify a leading-order logarithmic divergence. Finally, we propose gradient ascent and Sinkhorn-type algorithms to numerically solve the transport-relaxed Schr"odinger bridge, establishing a linear convergence rate for both algorithms.