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Applying Zvonkin's transform, the exponential convergence in Wasserstein distance for a class of functional SDEs with H\"older continuous drift is obtained. This combining with log-Harnack inequality implies the same convergence in the…

Probability · Mathematics 2018-11-06 Xing Huang

In this article, exponential contraction in Wasserstein distance for heat semigroups of diffusion processes on Riemannian manifolds is established under curvature conditions where Ricci curvature is not necessarily required to be…

Differential Geometry · Mathematics 2020-01-20 Li-Juan Cheng , Anton Thalmaier , Shao-Qin Zhang

The existence and uniqueness of stationary distributions and the exponential convergence in $L^p$-Wasserstein distance are derived for distribution dependent SDEs from associated decoupled equations. To establish the exponential…

Probability · Mathematics 2022-03-14 Shao-Qin Zhang

The convergence to the stationary regime is studied for Stochastic Differential Equations driven by an additive Gaussian noise and evolving in a semi-contractive environment, i.e. when the drift is only contractive out of a compact set but…

Probability · Mathematics 2020-06-04 Fabien Panloup , Alexandre Richard

We describe conditions on non-gradient drift diffusion Fokker-Planck equations for its solutions to converge to equilibrium with a uniform exponential rate in Wasserstein distance. This asymptotic behaviour is related to a functional…

Probability · Mathematics 2012-09-19 François Bolley , Ivan Gentil , Arnaud Guillin

We consider contractivity for diffusion semigroups w.r.t. Kantorovich ($L^1$ Wasserstein) distances based on appropriately chosen concave functions. These distances are inbetween total variation and usual Wasserstein distances. It is shown…

Probability · Mathematics 2015-10-20 Andreas Eberle

We study the existence and long-time asymptotics of weak solutions to a system of two nonlinear drift-diffusion equations that has a gradient flow structure in the Wasserstein distance. The two equations are coupled through a…

Analysis of PDEs · Mathematics 2021-12-14 Lisa Beck , Daniel Matthes , Martina Zizza

The following type exponential convergence is proved for (non-degenerate or degenerate) McKean-Vlasov SDEs: $$W_2(\mu_t,\mu_\infty)^2 +{\rm Ent}(\mu_t|\mu_\infty)\le c {\rm e}^{-\lambda t} \min\big\{W_2(\mu_0, \mu_\infty)^2,{\rm…

Probability · Mathematics 2024-10-01 Panpan Ren , Feng-Yu Wang

We consider elliptic diffusion processes on $\mathbb R^d$. Assuming that the drift contracts distances outside a compact set, we prove that, at a sufficiently high temperature, the Markov semi-group associated to the process is a…

Probability · Mathematics 2023-07-20 Pierre Monmarché

In this note, we consider a Stochastic Differential Equation under a strong confluence and Lipschitz continuity assumption of the coefficients. For the unique stationary solution, we study the rate of convergence of its empirical measure…

Probability · Mathematics 2025-02-12 Jean-Francois Chassagneux , Gilles Pagès

Let $P_t$ be the (Neumann) diffusion semigroup $P_t$ generated by a weighted Laplacian on a complete connected Riemannian manifold $M$ without boundary or with a convex boundary. It is well known that the Bakry-Emery curvature is bounded…

Probability · Mathematics 2016-11-24 Feng-Yu Wang

We study the quantitative convergence of drift-diffusion PDEs that arise as Wasserstein gradient flows of linearly convex functions over the space of probability measures on ${\mathbb R}^d$. In this setting, the objective is in general not…

Optimization and Control · Mathematics 2025-07-17 Lénaïc Chizat , Maria Colombo , Xavier Fernández-Real

By using the spectrum of the underlying symmetric diffusion operator, the convergence in $L^p$-Wasserstein distance $\mathbb W_p (p\ge 1)$ is characterized for the empirical measure $\mu_t$ of non-symmetric subordinated diffusion processes…

Probability · Mathematics 2023-02-28 Feng-Yu Wang

We suggest that the tools of contraction analysis for deterministic systems can be applied towards studying the convergence behavior of stochastic dynamical systems in the Wasserstein metric. In particular, we consider the case of Ito…

Optimization and Control · Mathematics 2019-03-01 Jake Bouvrie , Jean-Jacques Slotine

We show the $L^2$-Wasserstein contraction for the transition kernel of a discretised diffusion process, under a contractivity at infinity condition on the drift and a sufficiently high diffusivity requirement. This extends recent results…

Probability · Mathematics 2023-10-25 Linshan Liu , Mateusz B. Majka , Pierre Monmarché

Being concerned with ergodicity of McKean--Vlasov SDEs, we establish a general result on exponential ergodicity in the $L^1$-Wasserstein distance. The result is successfully applied to non-degenerate and multiplicative Brownian motion…

Probability · Mathematics 2025-01-23 Xing Huang , Huaiqian Li , Liying Mu

This work focuses on the quantitative contraction rates for McKean-Vlasov stochastic differential equations (SDEs) with multiplicative noise. Under suitable conditions on the coefficients of the SDE, this paper derives explicit quantitative…

Probability · Mathematics 2025-09-30 Dan Noelck

In this paper, we derive exponential ergodicity in relative entropy for general kinetic SDEs under a partially dissipative condition. It covers non-equilibrium situations where the forces are not of gradient type and the invariant measure…

Probability · Mathematics 2025-07-10 Xing Huang , Eva Kopfer , Pierre Monmarché , Panpan Ren

The fact that a Markov diffusion semi-group on $\mathbb R^d$ contracts the $L^p$ Wasserstein distance, which has been extensively used to establish uniform-in-time stability estimates (e.g. with respect to numerical discretization errors),…

Probability · Mathematics 2026-04-06 Pierre Monmarché

We present a framework that allows for the non-asymptotic study of the $2$-Wasserstein distance between the invariant distribution of an ergodic stochastic differential equation and the distribution of its numerical approximation in the…

Machine Learning · Statistics 2021-09-27 J. M. Sanz-Serna , Konstantinos C. Zygalakis
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