Contractive kinetic Langevin samplers beyond global Lipschitz continuity

Fuente: arXiv
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Main Authors: Lytras, Iosif, Mertikopoulos, Panayotis
Format: Preprint
Published: 2025
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author Lytras, Iosif
Mertikopoulos, Panayotis
author_facet Lytras, Iosif
Mertikopoulos, Panayotis
contents In this paper, we examine the problem of sampling from log-concave distributions with (possibly) superlinear gradient growth under kinetic (underdamped) Langevin algorithms. Using a carefully tailored taming scheme, we propose two novel discretizations of the kinetic Langevin SDE, and we show that they are both contractive and satisfy a log-Sobolev inequality. Building on this, we establish a series of non-asymptotic bounds in $2$-Wasserstein distance between the law reached by each algorithm and the underlying target measure.
format Preprint
id arxiv_https___arxiv_org_abs_2509_12031
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Contractive kinetic Langevin samplers beyond global Lipschitz continuity
Lytras, Iosif
Mertikopoulos, Panayotis
Probability
Numerical Analysis
Machine Learning
math.PR, math.NA
In this paper, we examine the problem of sampling from log-concave distributions with (possibly) superlinear gradient growth under kinetic (underdamped) Langevin algorithms. Using a carefully tailored taming scheme, we propose two novel discretizations of the kinetic Langevin SDE, and we show that they are both contractive and satisfy a log-Sobolev inequality. Building on this, we establish a series of non-asymptotic bounds in $2$-Wasserstein distance between the law reached by each algorithm and the underlying target measure.
title Contractive kinetic Langevin samplers beyond global Lipschitz continuity
topic Probability
Numerical Analysis
Machine Learning
math.PR, math.NA
url https://arxiv.org/abs/2509.12031