Mirror Langevin diffusions: Convergence rates and Markov chain approximations

Given a strongly convex function u, equip R^d with a Riemannian metric given by the Hessian \nabla^2 u. This is a so-called Hessian manifold. Given a probability density μ one may run a Langevin diffusion intrinsic to the manifold with stationary distribution μ. Such (Hessian) manifold-valued Langevin diffusions are called Mirror Langevin diffusions (MLD) which have recently become popular. One of the questions we explore is whether, given μ, one can choose u to get an exponential convergence to equilibrium for the MLD, especially if μ is not strongly log-concave. Our results are based on Lyapunov function methods and give sufficient conditions for a Poincaré or a log-Sobolev inequality to hold for the MLD. These, in turn, imply exponential convergence. We also introduce a Markov chain approximation to the MLD given by a two step Gibbs sampler with stationary distribution μ. This Markov chain is a variant of the Sinkhorn Markov chain introduced in arXiv:2307.16421 that is conjectured to converge to a time-inhomogeneous generalization of the MLD. Under suitable assumptions, we prove that the Markov chain has a guaranteed convergence rate in χ^2 that is consistent with the diffusion time scale. Our proofs are based on ideas from entropic optimal transport and strong data processing inequalities.