Long-Time Asymptotics of the Sliced-Wasserstein Flow
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Abstract (arXiv)
The sliced-Wasserstein flow is an evolution equation where a probability density evolves in time, advected by a velocity field computed as the average among directions in the unit sphere of the optimal transport displacements from its 1D projections to the projections of a fixed target measure. This flow happens to be the gradient flow in the usual Wasserstein space of the squared sliced-Wasserstein distance to the target. We consider the question whether in long-time the flow converges to the target (providing a positive result when the target is Gaussian) and the question of the long-time limit of the flow map obtained by following the trajectories of each particle. We prove that this limit is in general not the optimal transport map from the starting measure to the target. Both questions come from the folklore about sliced-Wasserstein and had never been properly treated.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 May 2024.
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Citing papers per year: 2025: 2; 2026: 5.
7 papers.
- 2026On the Wasserstein Gradient Flow Interpretation of Drifting Modelscited by 6
- 2025Towards Understanding Gradient Dynamics of the Sliced-Wasserstein Distance via Critical Point Analysiscited by 3
- 2026Convergence Rates for Distribution Matching with Sliced Optimal Transportcited by 2
- 2026Sliced Wasserstein Steering between Gaussian Measurescited by 1
- 2025ReSWD: ReSTIR'd, not shaken. Combining Reservoir Sampling and Sliced Wasserstein Distance for Variance Reductioncited by 0
- 2026AdamFlow: Adam-based Wasserstein Gradient Flows for Surface Registration in Medical Imagingcited by 0
- 2026Distribution Steering via Sliced Optimal Transport Controlcited by 0
- 2026Distribution Steering via Sliced Optimal Transport Control13 Aug 2026
- 2026On the Wasserstein Gradient Flow Interpretation of Drifting Models6 May 2026
- 2026Sliced Wasserstein Steering between Gaussian Measures14 Apr 2026
- 2026AdamFlow: Adam-based Wasserstein Gradient Flows for Surface Registration in Medical Imaging2 Apr 2026
- 2026Convergence Rates for Distribution Matching with Sliced Optimal Transport11 Feb 2026
- 2025ReSWD: ReSTIR'd, not shaken. Combining Reservoir Sampling and Sliced Wasserstein Distance for Variance Reduction1 Oct 2025
- 2025Towards Understanding Gradient Dynamics of the Sliced-Wasserstein Distance via Critical Point Analysis10 Feb 2025