Unadjusted Langevin algorithm with multiplicative noise: Total variation and Wasserstein bounds
Abstract
In this paper, we focus on non-asymptotic bounds related to the Euler scheme of an ergodic diffusion with a possibly multiplicative diffusion term (non-constant diffusion coefficient). More precisely, the objective of this paper is to control the distance of the standard Euler scheme with decreasing step ({usually called Unadjusted Langevin Algorithm in the Monte Carlo literature}) to the invariant distribution of such an ergodic diffusion. In an appropriate Lyapunov setting and under {uniform} ellipticity assumptions on the diffusion coefficient, we establish (or improve) such bounds for Total Variation and -Wasserstein distances in both multiplicative and additive and frameworks. These bounds rely on weak error expansions using {Stochastic Analysis} adapted to decreasing step setting.
Cite
@article{arxiv.2012.14310,
title = {Unadjusted Langevin algorithm with multiplicative noise: Total variation and Wasserstein bounds},
author = {Gilles Pages and Fabien Panloup},
journal= {arXiv preprint arXiv:2012.14310},
year = {2022}
}