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Relative Lipschitzness in Extragradient Methods and a Direct Recipe for Acceleration

Authors Michael B. Cohen, Aaron Sidford, Kevin Tian

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Author Details

Michael B. Cohen
  • Massachusetts Institute of Technoolgy, Cambridge, MA, USA
Aaron Sidford
  • Stanford University, CA, USA
Kevin Tian
  • Stanford University, CA, USA

Funding

Researchers supported in part by Microsoft Research Faculty Fellowship, NSF CAREER Award CCF-1844855, NSF Grant CCF-1955039, a PayPal research gift, and a Sloan Research Fellowship.

Acknowledgements

The existence of an extragradient algorithm in the primal-dual formulation of smooth minimization directly achieving accelerated rates is due to discussions with the first author, Michael B. Cohen. The second and third authors are indebted to him, and this work is dedicated in his memory. We also thank our collaborators in concurrent works, Yair Carmon and Yujia Jin, for many helpful and encouraging conversations throughout the duration of this project, Jelena Diakonikolas, Jonathan Kelner, and Jonah Sherman for helpful conversations, and anonymous reviewers for multiple helpful comments on earlier versions of the paper.

Cite AsGet BibTex

Michael B. Cohen, Aaron Sidford, and Kevin Tian. Relative Lipschitzness in Extragradient Methods and a Direct Recipe for Acceleration. In 12th Innovations in Theoretical Computer Science Conference (ITCS 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 185, pp. 62:1-62:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/LIPIcs.ITCS.2021.62

Abstract

We show that standard extragradient methods (i.e. mirror prox [Arkadi Nemirovski, 2004] and dual extrapolation [Yurii Nesterov, 2007]) recover optimal accelerated rates for first-order minimization of smooth convex functions. To obtain this result we provide fine-grained characterization of the convergence rates of extragradient methods for solving monotone variational inequalities in terms of a natural condition we call relative Lipschitzness. We further generalize this framework to handle local and randomized notions of relative Lipschitzness and thereby recover rates for box-constrained 𝓁_∞ regression based on area convexity [Jonah Sherman, 2017] and complexity bounds achieved by accelerated (randomized) coordinate descent [Zeyuan {Allen Zhu} et al., 2016; Yurii Nesterov and Sebastian U. Stich, 2017] for smooth convex function minimization.

Subject Classification

ACM Subject Classification
  • Mathematics of computing → Convex optimization
Keywords
  • Variational inequalities
  • minimax optimization
  • acceleration
  • 𝓁_∞ regression

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