Generalization of Silver Stepsize Schedule to Stochastic Optimization

Luwei Bai; Yang Zeng; Baoyu Zhou

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arXiv · 2511.21917

Generalization of Silver Stepsize Schedule to Stochastic Optimization

Luwei Bai·Yang Zeng·Baoyu Zhou

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Abstract

This work introduces a two-step stepsize schedule for stochastic gradient methods minimizing smooth strongly convex functions. We consider the setting where only stochastic gradient approximations, which are unbiased, of bounded variance, and supported on a finite set, are accessible. When the variance bound is relatively smaller than a ratio of the initial optimality gap, the proposed stepsize schedule achieves better convergence performance compared to the well-regarded constant stepsize {\alpha} = 2/(M+m), where m and M denote the strong convexity and gradient-Lipschitz parameters, respectively. Our stepsize schedule can be viewed as a generalization of the well-known two-step silver stepsize schedule in [J. M. Altschuler and P. A. Parrilo, Journal of the ACM, 72(2):1-38, 2025] from deterministic setting to stochastic optimization.

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Luwei Bai, Yang Zeng, Baoyu Zhou. 2025-11-26. Generalization of Silver Stepsize Schedule to Stochastic Optimization. https://arxiv.org/abs/2511.21917

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