Displacement smoothness of entropic optimal transport
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Abstract (arXiv)
The function that maps a family of probability measures to the solution of the dual entropic optimal transport problem is known as the Schr"odinger map. We prove that when the cost function is Ck+1 with k∈ mathbbN^* then this map is Lipschitz continuous from the L2-Wasserstein space to the space of Ck functions. Our result holds on compact domains and covers the multi-marginal case. We also include regularity results under negative Sobolev metrics weaker than Wasserstein under stronger smoothness assumptions on the cost. As applications, we prove displacement smoothness of the entropic optimal transport cost and the well-posedness of certain Wasserstein gradient flows involving this functional, including the Sinkhorn divergence and a multi-species system.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Oct 2022.
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Citing papers per year: 2023: 2; 2024: 2; 2025: 5; 2026: 4.
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- 2026One-Step Generative Modeling via Wasserstein Gradient Flowscited by 18
- 2023Doubly Regularized Entropic Wasserstein Barycenterscited by 10
- 2024Progressive Entropic Optimal Transport Solverscited by 10
- 2023Estimating the Rate-Distortion Function by Wasserstein Gradient Descentcited by 5
- 2024A semiconcavity approach to stability of entropic plans and exponential convergence of Sinkhorn's algorithmcited by 4
- 2025DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient Flowscited by 4
- 2025New universal operator approximation theorem for encoder-decoder architecturescited by 3
- 2025Schr"odinger bridge for generative AI: Soft-constrained formulation and convergence analysiscited by 2
- 2025Stability of the Kim--Milman flow mapcited by 1
- 2025Hessian stability and convergence rates for entropic and Sinkhorn potentials via semiconcavitycited by 0
- 2026Near-Lipschitz stability of the Kim--Milman flow mapcited by 0
- 2026Uniform Statistical Convergence of Empirical Sinkhorn Potentials with Exponential and Polynomial Dependence on the Regularization Parametercited by 0
1 more
- 2026Low-Budget Active Learning through Entropic Optimal Transport1 Oct 2026
- 2026Uniform Statistical Convergence of Empirical Sinkhorn Potentials with Exponential and Polynomial Dependence on the Regularization Parameter29 Aug 2026
- 2026Near-Lipschitz stability of the Kim--Milman flow map22 Jun 2026
- 2026One-Step Generative Modeling via Wasserstein Gradient Flows12 May 2026
- 2025Stability of the Kim--Milman flow map3 Nov 2025
- 2025Schr"odinger bridge for generative AI: Soft-constrained formulation and convergence analysis13 Oct 2025
- 2025Hessian stability and convergence rates for entropic and Sinkhorn potentials via semiconcavity15 Apr 2025
- 2025New universal operator approximation theorem for encoder-decoder architectures31 Mar 2025
- 2025DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient Flows3 Mar 2025
- 2024A semiconcavity approach to stability of entropic plans and exponential convergence of Sinkhorn's algorithm12 Dec 2024
- 2024Progressive Entropic Optimal Transport Solvers7 Jun 2024
- 2023Estimating the Rate-Distortion Function by Wasserstein Gradient Descent29 Oct 2023
1 more
- 2023Doubly Regularized Entropic Wasserstein Barycenters21 Mar 2023
- 2026One-Step Generative Modeling via Wasserstein Gradient Flowscited by 18
- 2026One-Step Generative Modeling via Wasserstein Gradient Flows12 May 2026
- 2023Doubly Regularized Entropic Wasserstein Barycenterscited by 10
- 2024Progressive Entropic Optimal Transport Solverscited by 10
- 2023Estimating the Rate-Distortion Function by Wasserstein Gradient Descentcited by 5
- 2024A semiconcavity approach to stability of entropic plans and exponential convergence of Sinkhorn's algorithmcited by 4
- 2025DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient Flowscited by 4
- 2025New universal operator approximation theorem for encoder-decoder architecturescited by 3
- 2025Schr"odinger bridge for generative AI: Soft-constrained formulation and convergence analysiscited by 2
- 2025Stability of the Kim--Milman flow mapcited by 1
- 2025Hessian stability and convergence rates for entropic and Sinkhorn potentials via semiconcavitycited by 0
- 2026Near-Lipschitz stability of the Kim--Milman flow mapcited by 0
- 2026Uniform Statistical Convergence of Empirical Sinkhorn Potentials with Exponential and Polynomial Dependence on the Regularization Parametercited by 0
- 2026Low-Budget Active Learning through Entropic Optimal Transportcited by 0
- 2026Low-Budget Active Learning through Entropic Optimal Transport1 Oct 2026
- 2026Uniform Statistical Convergence of Empirical Sinkhorn Potentials with Exponential and Polynomial Dependence on the Regularization Parameter29 Aug 2026
- 2026Near-Lipschitz stability of the Kim--Milman flow map22 Jun 2026
- 2025Stability of the Kim--Milman flow map3 Nov 2025
- 2025Schr"odinger bridge for generative AI: Soft-constrained formulation and convergence analysis13 Oct 2025
- 2025Hessian stability and convergence rates for entropic and Sinkhorn potentials via semiconcavity15 Apr 2025
- 2025New universal operator approximation theorem for encoder-decoder architectures31 Mar 2025
- 2025DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient Flows3 Mar 2025
- 2024A semiconcavity approach to stability of entropic plans and exponential convergence of Sinkhorn's algorithm12 Dec 2024
- 2024Progressive Entropic Optimal Transport Solvers7 Jun 2024
- 2023Estimating the Rate-Distortion Function by Wasserstein Gradient Descent29 Oct 2023
- 2023Doubly Regularized Entropic Wasserstein Barycenters21 Mar 2023