Forward-Euler time-discretization for Wasserstein gradient flows can be wrong
Not on the map. Why?
Abstract (arXiv)
In this note, we examine the forward-Euler discretization for simulating Wasserstein gradient flows. We provide two counter-examples showcasing the failure of this discretization even for a simple case where the energy functional is defined as the KL divergence against some nicely structured probability densities. A simple explanation of this failure is also discussed.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 10 of its 24 references that are in the map. First posted on arXiv 12 Jun 2024.
Builds on
10 of 24 references.
References · 10
- 2015Learning with a Wasserstein Losscited by 194
- 2016Distributionally Robust Stochastic Optimization with Wasserstein Distancecited by 182
- 2021Large-Scale Wasserstein Gradient Flowscited by 76
- 2021Projected Wasserstein gradient descent for high-dimensional Bayesian inferencecited by 24
- 2020Dataset Dynamics via Gradient Flows in Probability Spacecited by 18
- 2024Wasserstein Gradient Flows for Moreau Envelopes of f-Divergences in Reproducing Kernel Hilbert Spacescited by 15
- 2023MonoFlow: Rethinking Divergence GANs via the Perspective of Wasserstein Gradient Flowscited by 12
- 2023Estimating the Rate-Distortion Function by Wasserstein Gradient Descentcited by 5
- 2023High order spatial discretization for variational time implicit schemes: Wasserstein gradient flows and reaction-diffusion systemscited by 1
- 2024Wasserstein gradient flow for optimal probability measure decompositioncited by 1
Built on by
Citing papers per year: 2024: 2; 2025: 0; 2026: 1.
3 papers.
- 2026Difference of Convex Programming in the Wasserstein Space with Applications to MMD Optimization26 Jun 2026
- 2024Linear convergence of proximal descent schemes on the Wasserstein space22 Nov 2024
- 2024Using Linearized Optimal Transport to Predict the Evolution of Stochastic Particle Systems3 Aug 2024