Local Exponential Stability of Mean-Field Langevin Descent-Ascent in Wasserstein Space
Geuntaek Seo, Minseop Shin, Pierre Monmarch\'e, Beomjun Choi

TL;DR
This paper proves that the mean-field Langevin descent-ascent dynamics in Wasserstein space are locally exponentially stable around the equilibrium for certain nonconvex-nonconcave zero-sum games, with convergence rate analysis.
Contribution
It establishes the local exponential stability of the mean-field Langevin descent-ascent dynamics in Wasserstein space, answering an open question and providing a spectral analysis-based proof.
Findings
The equilibrium is locally exponentially stable with convergence at an exponential rate.
A coercivity estimate for entropy near equilibrium is established via spectral analysis.
The analysis reveals a local displacement convex-concave structure driving contraction.
Abstract
We study the mean-field Langevin descent-ascent (MFL-DA), a coupled optimization dynamics on the space of probability measures for entropically regularized two-player zero-sum games. Although the associated mean-field objective admits a unique mixed Nash equilibrium, the long-time behavior of the original MFL-DA for general nonconvex-nonconcave payoffs has remained largely open. Answering an open question posed by Wang and Chizat (COLT 2024), we provide a partial resolution by proving that this equilibrium is locally exponentially stable: if the initialization is sufficiently close in Wasserstein metric, the dynamics trends to the equilibrium at an exponential rate. The key to our analysis is to establish a coercivity estimate for the entropy near equilibrium via spectral analysis of the linearized operator. We show that this coercivity effectively reveals a local displacement…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsStochastic Gradient Optimization Techniques · Risk and Portfolio Optimization · Geometric Analysis and Curvature Flows
