English

On the Hopf-Cole Transform for Control-affine Schr\"{o}dinger Bridge

Optimization and Control 2025-03-26 v1 Artificial Intelligence Machine Learning Systems and Control Systems and Control Machine Learning

Abstract

The purpose of this note is to clarify the importance of the relation ggσσ\boldsymbol{gg}^{\top}\propto \boldsymbol{\sigma\sigma}^{\top} in solving control-affine Schr\"{o}dinger bridge problems via the Hopf-Cole transform, where g,σ\boldsymbol{g},\boldsymbol{\sigma} are the control and noise coefficients, respectively. We show that the Hopf-Cole transform applied to the conditions of optimality for generic control-affine Schr\"{o}dinger bridge problems, i.e., without the assumption ggσσ\boldsymbol{gg}^{\top}\propto\boldsymbol{\sigma\sigma}^{\top}, gives a pair of forward-backward PDEs that are neither linear nor equation-level decoupled. We explain how the resulting PDEs can be interpreted as nonlinear forward-backward advection-diffusion-reaction equations, where the nonlinearity stem from additional drift and reaction terms involving the gradient of the log-likelihood a.k.a. the score. These additional drift and reaction vanish when ggσσ\boldsymbol{gg}^{\top}\propto\boldsymbol{\sigma\sigma}^{\top}, and the resulting boundary-coupled system of linear PDEs can then be solved by dynamic Sinkhorn recursions. A key takeaway of our work is that the numerical solution of the generic control-affine Schr\"{o}dinger bridge requires further algorithmic development, possibly generalizing the dynamic Sinkhorn recursion or otherwise.

Keywords

Cite

@article{arxiv.2503.17640,
  title  = {On the Hopf-Cole Transform for Control-affine Schr\"{o}dinger Bridge},
  author = {Alexis Teter and Abhishek Halder},
  journal= {arXiv preprint arXiv:2503.17640},
  year   = {2025}
}