Attend LIONS Seminar with guest Nicholas Loizou, March 27

Join the Learning, Information, Optimization, Networks and Statistics, or LIONS, group for a seminar with Nicolas Loizou, assistant professor in the Department of Applied Mathematics and Statistics and the Mathematical Institute for Data Science, or MINDS, at Johns Hopkins University. He holds secondary appointments in the Departments of Computer Science and Electrical and Computer Engineering and is a member of Johns Hopkins Data Science Institute and Ralph O’Connor Sustainable Energy Institute.

Extragradient methods are a fundamental class of algorithms for solving min-max optimization problems and variational inequalities. While the classical theory is largely developed under smoothness and other relatively restrictive assumptions, many problems arising in modern machine learning call for analysis in weaker regularity regimes and in stochastic large-scale settings.

In this talk, we present new convergence results for deterministic and stochastic extragradient methods beyond the classical framework. In particular, we establish convergence guarantees under the (L0, L1)-Lipschitz condition and derive new step-size rules that expand the range of provably convergent regimes.

We also introduce Polyak-type step sizes for deterministic and stochastic extragradient methods, leading to adaptive variants with favorable theoretical properties and practical performance. Our results focus primarily on monotone problems, with extensions to selected structured non-monotone settings. We conclude with numerical experiments that illustrate the theory and the empirical behavior of the proposed methods.

Visit the LIONS Seminar website for more information.

LIONS Seminar with Nicholas Loizou
1:30–2:30 p.m.
Goldwater Center for Science and Engineering (GWC) 487, Tempe campus [map]
Attend on Zoom