Non-asymptotic convergence bounds for modified tamed unadjusted Langevin algorithm in non-convex setting

DOI10.1016/j.jmaa.2024.128892MaRDI QIDQ6640899

Could not fetch data.

Publication date: 20 November 2024

Published in: Journal of Mathematical Analysis and Applications (Search for Journal in Brave)

zbMATH Keywords

high-dimensional samplingLangevin SDEnon-asymptotic convergence boundsmodified tamed unadjusted Langevin algorithmsuper-linearly growing diffusion coefficients

Mathematics Subject Classification ID

Monte Carlo methods (65C05) Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10)

Cites Work

• Title not available (Why is that?)
• Strong convergence of an explicit numerical method for SDEs with nonglobally Lipschitz continuous coefficients
• A note on tamed Euler approximations
• Laplace's method revisited: Weak convergence of probability measures
• An introduction to MCMC for machine learning
• The tamed unadjusted Langevin algorithm
• User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient
• High-dimensional Bayesian inference via the unadjusted Langevin algorithm
• Nonasymptotic convergence analysis for the unadjusted Langevin algorithm
• Ergodicity for SDEs and approximations: locally Lipschitz vector fields and degenerate noise.
• Nonasymptotic bounds for sampling algorithms without log-concavity
• Strong and weak divergence in finite time of Euler's method for stochastic differential equations with non-globally Lipschitz continuous coefficients
• Quantitative Harris-type theorems for diffusions and McKean–Vlasov processes
• On Stochastic Gradient Langevin Dynamics with Dependent Data Streams: The Fully Nonconvex Case
• Partial differential equations and stochastic methods in molecular dynamics
• Theoretical Guarantees for Approximate Sampling from Smooth and Log-Concave Densities
• MCMC methods for functions: modifying old algorithms to make them faster
• Taming Neural Networks with TUSLA: Nonconvex Learning via Adaptive Stochastic Gradient Langevin Algorithms
• Rapid convergence of the unadjusted Langevin algorithm: isoperimetry suffices
• Kinetic Langevin MCMC sampling without gradient Lipschitz continuity -- the strongly convex case

Related Items (1)

Linear implicit approximations of invariant measures of semi-linear SDEs with non-globally Lipschitz coefficients

This page was built for publication: Non-asymptotic convergence bounds for modified tamed unadjusted Langevin algorithm in non-convex setting

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6640899)