On the Last Iterate Convergence of Momentum Methods

Xiaoyu Li, Mingrui Liu, Francesco Orabona

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


SGD with Momentum (SGDM) is a widely used family of algorithms for large-scale optimization of machine learning problems. Yet, when optimizing generic convex functions, no advantage is known for any SGDM algorithm over plain SGD. Moreover, even the most recent results require changes to the SGDM algorithms, like averaging of the iterates and a projection onto a bounded domain, which are rarely used in practice. In this paper, we focus on the convergence rate of the last iterate of SGDM. For the first time, we prove that for any constant momentum factor, there exists a Lipschitz and convex function for which the last iterate of SGDM suffers from a suboptimal convergence rate of Ω(ln √TT) after T iterations. Based on this fact, we study a class of (both adaptive and non-adaptive) Follow-The-Regularized-Leader-based SGDM algorithms with increasing momentum and shrinking updates. For these algorithms, we show that the last iterate has optimal convergence O(√1T) for unconstrained convex stochastic optimization problems without projections onto bounded domains nor knowledge of T. Further, we show a variety of results for FTRL-based SGDM when used with adaptive stepsizes. Empirical results are shown as well.
Original languageEnglish (US)
Title of host publicationProceedings of Machine Learning Research
PublisherML Research Press
Number of pages19
StatePublished - Jan 1 2022
Externally publishedYes

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Generated from Scopus record by KAUST IRTS on 2023-09-25


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