Projected Langevin dynamics and a gradient flow for entropic optimal transport
Giovanni Conforti, Daniel Lacker, Soumik Pal

TL;DR
This paper introduces a diffusion process that samples from entropic optimal transport couplings, converging exponentially fast to the optimal solution and viewed as a Wasserstein gradient flow on the space of couplings.
Contribution
It develops a Langevin-type dynamics constrained to the set of couplings for entropic optimal transport, with proven exponential convergence and geometric interpretation.
Findings
The process remains in the set of couplings if initialized there.
It converges exponentially fast to the entropic optimal transport solution.
The dynamics can be viewed as a Wasserstein gradient flow on the space of couplings.
Abstract
The classical (overdamped) Langevin dynamics provide a natural algorithm for sampling from its invariant measure, which uniquely minimizes an energy functional over the space of probability measures, and which concentrates around the minimizer(s) of the associated potential when the noise parameter is small. We introduce analogous diffusion dynamics that sample from an entropy-regularized optimal transport, which uniquely minimizes the same energy functional but constrained to the set of couplings of two given marginal probability measures and on , and which concentrates around the optimal transport coupling(s) for small regularization parameter. More specifically, our process satisfies two key properties: First, the law of the solution at each time stays in if it is initialized there. Second, the long-time limit is the unique…
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Taxonomy
TopicsAdvanced Neuroimaging Techniques and Applications · Generative Adversarial Networks and Image Synthesis · Markov Chains and Monte Carlo Methods
