Entropic regularization of continuous optimal transport problems
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Abstract (arXiv)
We analyze continuous optimal transport problems in the so-called Kantorovich form, where we seek a transport plan between two marginals that are probability measures on compact subsets of Euclidean space. We consider the case of regularization with the negative entropy with respect to the Lebesgue measure, which has attracted attention because it can be solved by the very simple Sinkhorn algorithm. We first analyze the regularized problem in the context of classical Fenchel duality and derive a strong duality result for a predual problem in the space of continuous functions. However, this problem may not admit a minimizer, which prevents obtaining primal-dual optimality conditions. We then show that the primal problem is naturally analyzed in the Orlicz space of functions with finite entropy in the sense that the entropically regularized problem admits a minimizer if and only if the marginals have finite entropy. We then derive a dual problem in the corresponding dual space, for which existence can be shown by purely variational arguments and primal-dual optimality conditions can be derived. For marginals that do not have finite entropy, we finally show Gamma-convergence of the regularized problem with smoothed marginals to the original Kantorovich problem.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Jun 2019.
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Citing papers per year: 2021: 5; 2022: 1; 2023: 2; 2024: 1; 2025: 1.
10 papers.
- 2021Low-Rank Sinkhorn Factorizationcited by 67
- 2023Energy-guided Entropic Neural Optimal Transportcited by 26
- 2022Low-rank Optimal Transport: Approximation, Statistics and Debiasingcited by 21
- 2021Semi-Discrete Optimal Transport: Hardness, Regularization and Numerical Solutioncited by 13
- 2023Energy-Guided Continuous Entropic Barycenter Estimation for General Costscited by 12
- 2024The Synergy Between Optimal Transport Theory and Multi-Agent Reinforcement Learningcited by 6
- 2021Optimal transport with f-divergence regularization and generalized Sinkhorn algorithmcited by 4
- 2021Multivariate rank via entropic optimal transport: sample efficiency and generative modelingcited by 4
- 2021Image reconstruction in light-sheet microscopy: spatially varying deconvolution and mixed noisecited by 2
- 2025Sparse Partial Optimal Transport via Quadratic Regularizationcited by 1
- 2025Sparse Partial Optimal Transport via Quadratic Regularization11 Aug 2025
- 2024The Synergy Between Optimal Transport Theory and Multi-Agent Reinforcement Learning18 Jan 2024
- 2023Energy-Guided Continuous Entropic Barycenter Estimation for General Costs2 Oct 2023
- 2023Energy-guided Entropic Neural Optimal Transport12 Apr 2023
- 2022Low-rank Optimal Transport: Approximation, Statistics and Debiasing24 May 2022
- 2021Multivariate rank via entropic optimal transport: sample efficiency and generative modeling29 Oct 2021
- 2021Image reconstruction in light-sheet microscopy: spatially varying deconvolution and mixed noise8 Aug 2021
- 2021Optimal transport with f-divergence regularization and generalized Sinkhorn algorithm29 May 2021
- 2021Semi-Discrete Optimal Transport: Hardness, Regularization and Numerical Solution10 Mar 2021
- 2021Low-Rank Sinkhorn Factorization8 Mar 2021