Publication
Title
Smoothing unadjusted Langevin algorithms for nonsmooth composite potential functions
Author
Abstract
This paper addresses a gradient-based Markov Chain Monte Carlo (MCMC) method to sample from the posterior distribution of problems with nonsmooth potential functions. Following the Bayesian paradigm, our potential function will be some of two convex functions, where one of which is smooth. We first approximate the potential function by the so-called forward-backward envelope function, which is a real-valued smooth function with the same critical points as the original one. Then, we incorporate this smoothing technique with the unadjusted Langevin algorithm (ULA), leading to smoothing ULA, called SULA. We next establish non-asymptotic convergence results of SULA under mild assumption on the original potential function. We finally report some numerical results to establish the promising performance of SULA on both synthetic and real chemoinformatics data.
Language
English
Source (journal)
Applied mathematics and computation. - New York, N.Y.
Publication
New York, N.Y. : 2024
ISSN
0096-3003
DOI
10.1016/J.AMC.2023.128377
Volume/pages
464 (2024) , p. 1-18
Article Reference
128377
ISI
001091525500001
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 04.12.2023
Last edited 08.12.2023
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