Extragradient Methods: O(1/K) Last-Iterate Convergence for Monotone Variational Inequalities
Gauthier Gidel – Université de Montréal, Canada
The Extragradient [Korpelevich, 1976] and the Past Extragradient [Popov, 1980] methods are among the most popular methods for solving saddle point and variational inequalities problems (VIP). Recently, they have known a surge of interest with the emergence of variational inequality formulation for machine learning such as Generative Adversarial Networks. Despite their long history and significant attention in the optimization community, there remain important open problems about their convergence in the monotone setting. In this talk, we present how we resolved some of these open questions and derived the first last-iterate O(1/K) convergence rates for monotone and Lipschitz VIP without any additional assumptions on the operator.
The central part of our analysis is based on Performance Estimation Problems and computer-aided proofs. In the presentation, I will pay special attention to this approach and explain the main non-trivial issues we faced to get the final proofs via numerical computations.
[Korpelevich, 1976] Korpelevich, G. M. (1976). The extragradient method for finding saddle points and other problems. Matecon, 12:747–756.
[Popov, 1980] Popov, L. D. (1980). A modification of the arrow-hurwicz method for search of saddle points. Mathematical notes of the Academy of Sciences of the USSR, 28(5):845–848.
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