From Optimization to Sampling Through Gradient Flows

DOI10.1090/NOTI2717zbMATH Open1522.65103arXiv2302.11449OpenAlexW4379881236MaRDI QIDQ6049130

Author name not available (Why is that?)

Publication date: 16 October 2023

Published in: (Search for Journal in Brave)

Abstract: This article overviews how gradient flows, and discretizations thereof, are useful to design and analyze optimization and sampling algorithms. The interplay between optimization, sampling, and gradient flows is an active research area; our goal is to provide an accessible and lively introduction to some core ideas, emphasizing that gradient flows uncover the conceptual unity behind many optimization and sampling algorithms, and that they give a rich mathematical framework for their rigorous analysis.

Full work available at URL: https://arxiv.org/abs/2302.11449

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