Accelerated convergence to equilibrium and reduced asymptotic variance for Langevin dynamics using Stratonovich perturbations
DOI10.1016/j.crma.2019.04.008OpenAlexW2963888320WikidataQ128024008 ScholiaQ128024008MaRDI QIDQ2419644
Grigorios A. Pavliotis, Gilles Vilmart, Assyr Abdulle
Publication date: 14 June 2019
Published in: Comptes Rendus. Mathématique. Académie des Sciences, Paris (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1903.03024
Stability and convergence of numerical methods for ordinary differential equations (65L20) Computational methods for stochastic equations (aspects of stochastic analysis) (60H35) Numerical solutions to stochastic differential and integral equations (65C30)
Related Items (3)
Cites Work
- Unnamed Item
- Variance reduction using nonreversible Langevin samplers
- Improving the convergence of reversible samplers
- Optimal non-reversible linear drift for the convergence to equilibrium of a diffusion
- Accelerating diffusions
- Accelerating Gaussian diffusions
- An introduction to MCMC for machine learning
- Using perturbed underdamped Langevin dynamics to efficiently sample from probability distributions
- Inverse problems: A Bayesian perspective
- Irreversible Langevin samplers and variance reduction: a large deviations approach
- Convergence of Numerical Time-Averaging and Stationary Measures via Poisson Equations
- Stochastic Processes and Applications
This page was built for publication: Accelerated convergence to equilibrium and reduced asymptotic variance for Langevin dynamics using Stratonovich perturbations