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This research advances the theoretical understanding of optimal transport by characterizing the convergence conditions for Monge maps within semi-dual formulations, directly benefiting machine learning applications that rely on these mathematical structures. The work clarifies the computational dynamics between updating transport maps versus dual potentials, offering practical insights for improving numerical algorithms in generative modeling and data processing tasks.
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Stability of the Monge Map in Semi-Dual Optimal Transport

arXiv1 min read

arXiv:2605.05569v4 Announce Type: replace-cross Abstract: This paper shows that the semi-dual formulation of the optimal transport problem has a degenerate saddle-point structure, and that its numerical solution is equivalent to solving a constrained optimization problem. We derive necessary and sufficient conditions for the convergence of Monge maps without requiring optimality of the dual potential. This analysis helps explain why, in practice, numerical algorithms often require more iterations to update the transport map than the potential.

Abstract:This paper shows that the semi-dual formulation of the optimal transport problem has a degenerate saddle-point structure, and that its numerical solution is equivalent to solving a constrained optimization problem. We derive necessary and sufficient conditions for the convergence of Monge maps without requiring optimality of the dual potential. This analysis helps explain why, in practice, numerical algorithms often require more iterations to update the transport map than the potential.

Optimization and Control (math.OC); Machine Learning (cs.LG)

arXiv:2605.05569 [math.OC]

(or arXiv:2605.05569v4 [math.OC] for this version)

https://doi.org/10.48550/arXiv.2605.05569 Focus to learn more arXiv-issued DOI via DataCite

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Full text under the publisher's open licence · 166 words

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