Imperial College London

Professor Grigorios A. Pavliotis

Faculty of Natural SciencesDepartment of Mathematics

Professor of Applied Mathematics



+44 (0)20 7594 8564g.pavliotis Website




736aHuxley BuildingSouth Kensington Campus






BibTex format

author = {Duncan, AB and Pavliotis, GA and Lelievre, T},
doi = {10.1007/s10955-016-1491-2},
journal = {Journal of Statistical Physics},
pages = {457--491},
title = {Variance Reduction using Nonreversible Langevin Samplers},
url = {},
volume = {163},
year = {2016}

RIS format (EndNote, RefMan)

AB - A standard approach to computing expectations with respect to a given target measure is to introduce an overdamped Langevin equation which is reversible with respect to the target distribution, and to approximate the expectation by a time-averaging estimator. As has been noted in recent papers, introducing an appropriately chosen nonreversiblecomponent to the dynamics is beneficial, both in terms of reducing the asymptotic variance and of speeding up convergence to the target distribution. In this paper we present a detailed study of the dependence of the asymptotic variance on the deviation from reversibility. Our theoretical findings are supported by numerical simulations.
AU - Duncan,AB
AU - Pavliotis,GA
AU - Lelievre,T
DO - 10.1007/s10955-016-1491-2
EP - 491
PY - 2016///
SN - 1572-9613
SP - 457
TI - Variance Reduction using Nonreversible Langevin Samplers
T2 - Journal of Statistical Physics
UR -
UR -
VL - 163
ER -