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STORM optimization algorithm convergence analyzed under varied geometries

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This paper presents a convergence analysis for the Stochastic Recursive Momentum (STORM) optimization algorithm. The authors explore STORM's performance under various geometric conditions, particularly when the standard average smoothness assumption is not met. They derive new convergence rates for nonconvex, convex, and strongly convex objectives, demonstrating STORM's effectiveness with optimal or near-optimal rates across these scenarios. AI

RANK_REASON The item is an academic paper detailing theoretical convergence analysis of an optimization algorithm. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

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STORM optimization algorithm convergence analyzed under varied geometries

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The item is an academic paper detailing theoretical convergence analysis of an optimization algorithm. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Wei Jiang, Yibo Wang, Wenhao Yang, Rui Yan, Lijun Zhang, Zechao Li ·

    Convergence Analysis of STORM Under Different Geometries

    arXiv:2610.01599v1 Announce Type: cross Abstract: Stochastic recursive momentum (STORM) achieves fast convergence for nonconvex optimization via the variance reduction effect, but existing analyses rely on the strong average smoothness assumption. In this paper, we study the conv…

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