  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.
