title: Variance Reduction Using Nonreversible Langevin Samplers.
abstract: 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 [30, 37, 61, 72], introducing an appropriately chosen nonreversible component 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.
authors.affiliation: Department of Mathematics, South Kensington Campus, Imperial College London, London, SW7 2AZ England.
authors.forename: A B
authors.identifier: 0000-0001-5762-164X
authors.initials: AB
authors.lastname: Duncan
authors.affiliation: CERMICS, Ecole des ponts, Universit Paris-Est, 6-8 avenue Blaise Pascal, 77455 Marne la Valle Cedex 2, France.
authors.forename: T
authors.initials: T
authors.lastname: Lelièvre
authors.affiliation: Department of Mathematics, South Kensington Campus, Imperial College London, London, SW7 2AZ England.
authors.forename: G A
authors.initials: GA
authors.lastname: Pavliotis