Sampling from a log-concave distribution with projected Langevin Monte Carlo
type: Item
instance of: scholarly article
title: Sampling from a log-concave distribution with projected Langevin Monte Carlo
zbMATH Open document ID: 1397.65010
author: Sébastien Bubeck
author: Ronen Eldan
author: Joseph Lehec
published in: Discrete \& Computational Geometry
publication date: 2018-07-13T00:00:00Z
full work available at URL: https://arxiv.org/abs/1507.02564
review text: The authors establish a bound on the number of steps required for the Projected Langevin Monte Carlo to attain a small total variation distance to the target measure. The properties of reflected Brownian is used to obtain a total variation bound from the Wasserstein bound. The arguments are generalized to an arbitrary smooth potential. Some preliminary experimental comparison between Langevin Monte Carlo and hit-and-run is presented.
Mathematics Subject Classification ID: 65C05
Mathematics Subject Classification ID: 60B10
Mathematics Subject Classification ID: 47N10
Mathematics Subject Classification ID: 68W20
Mathematics Subject Classification ID: 68W25
zbMATH DE Number: 6903499
zbMATH Keywords: Langevin Monte Carlo
zbMATH Keywords: sampling and optimization
zbMATH Keywords: log-concave measures
zbMATH Keywords: rapidly-mixing random walks
MaRDI profile type: Publication
OpenAlex ID: W2963813262
arXiv ID: 1507.02564
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Wikidata QID: Q130021959
DOI: 10.1007/S00454-018-9992-1
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description: scientific article