Collision-based Dynamics for Multi-Marginal Optimal Transport
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Abstract (arXiv)
Inspired by the Boltzmann kinetics, we propose a collision-based dynamics with a Monte Carlo solution algorithm that approximates the solution of the multi-marginal optimal transport problem via randomized pairwise swapping of sample indices. The computational complexity and memory usage of the proposed method scale linearly with the number of samples, making it highly attractive for high-dimensional settings. In several examples, we demonstrate the efficiency of the proposed method compared to the state-of-the-art methods.
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References · 15
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2014Deep Learning Face Attributes in the Wildcited by 3,693
- 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,316
- 2015Data-driven Distributionally Robust Optimization Using the Wasserstein Metric: Performance Guarantees and Tractable Reformulationscited by 291
- 2016Stochastic Optimization for Large-scale Optimal Transportcited by 189
- 2022Manifold Interpolating Optimal-Transport Flows for Trajectory Inferencecited by 64
- 2014Multi-marginal optimal transport: theory and applicationscited by 41
- 2020On the representation and learning of monotone triangular transport mapscited by 41
- 2018Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embeddingcited by 28
- 2020A Riemannian Block Coordinate Descent Method for Computing the Projection Robust Wasserstein Distancecited by 27
- 2023Projected Langevin dynamics and a gradient flow for entropic optimal transportcited by 17
- 2020Multi-marginal optimal transport and probabilistic graphical modelscited by 13
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- 2016Robust clustering tools based on optimal transportationcited by 10
- 2022Moment-SoS Methods for Optimal Transport Problemscited by 4
- 2024Optimal Transportation by Orthogonal Coupling Dynamicscited by 2
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