Analysis of kinetic Langevin Monte Carlo under the stochastic exponential Euler discretization from underdamped all the way to overdamped
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Abstract (arXiv)
Simulating the kinetic Langevin dynamics is a popular approach for sampling from distributions, where only their unnormalized densities are available. Various discretizations of the kinetic Langevin dynamics have been considered, where the resulting algorithm is collectively referred to as the kinetic Langevin Monte Carlo (KLMC) or underdamped Langevin Monte Carlo. Specifically, the stochastic exponential Euler discretization, or exponential integrator for short, has previously been studied under strongly log-concave and log-Lipschitz smooth potentials via the synchronous Wasserstein coupling strategy. Existing analyses, however, impose restrictions on the parameters that do not explain the behavior of KLMC under various choices of parameters. In particular, all known results fail to hold in the overdamped regime, suggesting that the exponential integrator degenerates in the overdamped limit. In this work, we revisit the synchronous Wasserstein coupling analysis of KLMC with the exponential integrator. Our refined analysis results in Wasserstein contractions and bounds on the asymptotic bias that hold under weaker restrictions on the parameters, which assert that the exponential integrator is capable of stably simulating the kinetic Langevin dynamics in the overdamped regime, as long as proper time acceleration is applied.
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References · 11
- 2016High-dimensional Bayesian inference via the Unadjusted Langevin Algorithmcited by 144
- 2022Score-Based Diffusion meets Annealed Importance Samplingcited by 51
- 2025Underdamped Diffusion Bridges with Applications to Samplingcited by 28
- 2023Improved Discretization Analysis for Underdamped Langevin Monte Carlocited by 26
- 2019Bounding the error of discretized Langevin algorithms for non-strongly log-concave targetscited by 16
- 2019Schr"odinger Bridge Samplerscited by 14
- 2023Kinetic Langevin MCMC Sampling Without Gradient Lipschitz Continuity -- the Strongly Convex Casecited by 12
- 2024Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlocited by 10
- 2021Multimeasurement Generative Modelscited by 8
- 2023Unbiased Kinetic Langevin Monte Carlo with Inexact Gradientscited by 8
- 2023Reflection coupling for unadjusted generalized Hamiltonian Monte Carlo in the nonconvex stochastic gradient casecited by 3
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