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

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

We consider the problem of sampling from a high-dimensional target distribution πβ on Rd with density proportional to θ↦e−βU(θ) using explicit numerical schemes based on discretising the Langevin stochastic differential equation (SDE). In recent literature, taming has been proposed and studied as a...

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Bibliographic Details
Main Authors: Neufeld, Ariel, Ng, Matthew Cheng En, Zhang, Ying
Other Authors: School of Physical and Mathematical Sciences
Format: Article
Language:English
Published: 2025
Subjects:
Mathematical Sciences
Modified tamed unadjusted
Langevin algorithm
Online Access:https://hdl.handle.net/10356/182195
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Institution: Nanyang Technological University
Language: English

Internet

https://hdl.handle.net/10356/182195

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