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August 2020 Nonasymptotic bounds for sampling algorithms without log-concavity
Mateusz B. Majka, Aleksandar Mijatović, Łukasz Szpruch
Ann. Appl. Probab. 30(4): 1534-1581 (August 2020). DOI: 10.1214/19-AAP1535


Discrete time analogues of ergodic stochastic differential equations (SDEs) are one of the most popular and flexible tools for sampling high-dimensional probability measures. Non-asymptotic analysis in the $L^{2}$ Wasserstein distance of sampling algorithms based on Euler discretisations of SDEs has been recently developed by several authors for log-concave probability distributions. In this work we replace the log-concavity assumption with a log-concavity at infinity condition. We provide novel $L^{2}$ convergence rates for Euler schemes, expressed explicitly in terms of problem parameters. From there we derive nonasymptotic bounds on the distance between the laws induced by Euler schemes and the invariant laws of SDEs, both for schemes with standard and with randomised (inaccurate) drifts. We also obtain bounds for the hierarchy of discretisation, which enables us to deploy a multi-level Monte Carlo estimator. Our proof relies on a novel construction of a coupling for the Markov chains that can be used to control both the $L^{1}$ and $L^{2}$ Wasserstein distances simultaneously. Finally, we provide a weak convergence analysis that covers both the standard and the randomised (inaccurate) drift case. In particular, we reveal that the variance of the randomised drift does not influence the rate of weak convergence of the Euler scheme to the SDE.


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Mateusz B. Majka. Aleksandar Mijatović. Łukasz Szpruch. "Nonasymptotic bounds for sampling algorithms without log-concavity." Ann. Appl. Probab. 30 (4) 1534 - 1581, August 2020.


Received: 1 September 2018; Revised: 1 August 2019; Published: August 2020
First available in Project Euclid: 4 August 2020

MathSciNet: MR4132634
Digital Object Identifier: 10.1214/19-AAP1535

Primary: 65C05 , 65C30 , 65C40
Secondary: 60H10 , 60J22 , 62H12

Keywords: coupling , Euler schemes , Langevin Monte Carlo , Markov chain Monte Carlo , multi-level Monte Carlo , sampling

Rights: Copyright © 2020 Institute of Mathematical Statistics


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Vol.30 • No. 4 • August 2020
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