A Pontryagin Maximum Principle in Wasserstein Spaces for Constrained Optimal Control Problems
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Abstract (arXiv)
In this paper, we prove a Pontryagin Maximum Principle for constrained optimal control problems in the Wasserstein space of probability measures. The dynamics, is described by a transport equation with non-local velocities and is subject to end-point and running state constraints. Building on our previous work, we combine the classical method of needle-variations from geometric control theory and the metric differential structure of the Wasserstein spaces to obtain a maximum principle stated in the so-called Gamkrelidze form.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Oct 2018.
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Citing papers per year: 2024: 2; 2025: 3; 2026: 2.
7 papers.
- 2024Non-geodesically-convex optimization in the Wasserstein spacecited by 5
- 2025DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient Flowscited by 4
- 2024Dataset Distillation from First Principles: Integrating Core Information Extraction and Purposeful Learningcited by 3
- 2025Hessian-guided Perturbed Wasserstein Gradient Flows for Escaping Saddle Pointscited by 2
- 2026Convergence Rates for Distribution Matching with Sliced Optimal Transportcited by 2
- 2025Maximum Principle of Optimal Probability Density Controlcited by 0
- 2026Difference of Convex Programming in the Wasserstein Space with Applications to MMD Optimizationcited by 0
- 2026Difference of Convex Programming in the Wasserstein Space with Applications to MMD Optimization26 Jun 2026
- 2026Convergence Rates for Distribution Matching with Sliced Optimal Transport11 Feb 2026
- 2025Hessian-guided Perturbed Wasserstein Gradient Flows for Escaping Saddle Points21 Sep 2025
- 2025Maximum Principle of Optimal Probability Density Control23 May 2025
- 2025DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient Flows3 Mar 2025
- 2024Dataset Distillation from First Principles: Integrating Core Information Extraction and Purposeful Learning2 Sep 2024
- 2024Non-geodesically-convex optimization in the Wasserstein space1 Jun 2024