On the Uniqueness of Kantorovich Potentials
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Abstract (arXiv)
Kantorovich potentials denote the dual solutions of the renowned optimal transportation problem. Uniqueness of these solutions is relevant from both a theoretical and an algorithmic point of view, and has recently emerged as a necessary condition for asymptotic results in the context of statistical and entropic optimal transport. In this work, we challenge the common perception that uniqueness in continuous settings is reliant on the connectedness of the support of at least one of the involved measures, and we provide mild sufficient conditions for uniqueness even when both measures have disconnected support. Since our main finding builds upon the uniqueness of Kantorovich potentials on connected components, we revisit the corresponding arguments and provide generalizations of well-known results. To this end, we introduce the notion of induced regularity and employ it to extend the regularity theory of Kantorovich potentials advanced by Gangbo and McCann (1996) to more general cost functions in ℝd and to geodesic spaces.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jan 2022.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 4.
7 papers.
- 2022Wasserstein Iterative Networks for Barycenter Estimationcited by 23
- 2024Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisationcited by 12
- 2024Improving Neural Optimal Transport via Displacement Interpolationcited by 7
- 2025Overcoming Spurious Solutions in Semi-Dual Neural Optimal Transport: A Smoothing Approach for Learning the Optimal Transport Plancited by 6
- 2025Wasserstein-Cram'er-Rao Theory of Unbiased Estimationcited by 2
- 2025Statistical Inference for Optimal Transport Maps: Recent Advances and Perspectivescited by 1
- 2025KAIROS: Scalable Model-Agnostic Data Valuationcited by 1
- 2025Wasserstein-Cram'er-Rao Theory of Unbiased Estimation10 Nov 2025
- 2025KAIROS: Scalable Model-Agnostic Data Valuation30 Jun 2025
- 2025Statistical Inference for Optimal Transport Maps: Recent Advances and Perspectives23 Jun 2025
- 2025Overcoming Spurious Solutions in Semi-Dual Neural Optimal Transport: A Smoothing Approach for Learning the Optimal Transport Plan7 Feb 2025
- 2024Improving Neural Optimal Transport via Displacement Interpolation3 Oct 2024
- 2024Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation1 Feb 2024
- 2022Wasserstein Iterative Networks for Barycenter Estimation28 Jan 2022