A McKean-Pontrygin maximum principle for entropic-regularized optimal transport

Fuente: arXiv
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Autore principale: Reich, Sebastian
Natura: Preprint
Pubblicazione: 2026
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_version_ 1866915903393759232
author Reich, Sebastian
author_facet Reich, Sebastian
contents This note outlines a mean-field approach to dynamic optimal transport problems based on the recently proposed McKean-Pontryagin maximum principle. Key aspects of the proposed methodology include i) avoidance of sampling over stochastic paths, ii) a fully variational approach leading to constrained Hamiltonian equations of motion, and iii) a unified treatment of deterministic and stochastic optimal transport problems. We also discuss connections to well-known dynamic formulations in terms of forward-backward stochastic differential equations and extensions beyond classical entropic-regularized transport problems.
format Preprint
id arxiv_https___arxiv_org_abs_2603_30019
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle A McKean-Pontrygin maximum principle for entropic-regularized optimal transport
Reich, Sebastian
Optimization and Control
Numerical Analysis
This note outlines a mean-field approach to dynamic optimal transport problems based on the recently proposed McKean-Pontryagin maximum principle. Key aspects of the proposed methodology include i) avoidance of sampling over stochastic paths, ii) a fully variational approach leading to constrained Hamiltonian equations of motion, and iii) a unified treatment of deterministic and stochastic optimal transport problems. We also discuss connections to well-known dynamic formulations in terms of forward-backward stochastic differential equations and extensions beyond classical entropic-regularized transport problems.
title A McKean-Pontrygin maximum principle for entropic-regularized optimal transport
topic Optimization and Control
Numerical Analysis
url https://arxiv.org/abs/2603.30019