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New convergence guarantees for sign-based momentum optimization methods

By PulseAugur Editorial · [1 sources] ·

This paper introduces enhanced convergence guarantees for sign-based momentum methods in optimization. The research demonstrates that signSGD with momentum can achieve improved convergence rates using constant batch sizes, without requiring additional assumptions or large batch sizes. The study also establishes a better convergence rate under the $l_2$-smoothness condition and explores distributed settings, yielding superior convergence rates compared to previous results. AI

IMPACT This research offers theoretical advancements in optimization techniques that could potentially improve the efficiency and convergence of machine learning algorithms.

RANK_REASON Academic paper published on arXiv detailing new theoretical results in optimization methods. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New convergence guarantees for sign-based momentum optimization methods

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Academic paper published on arXiv detailing new theoretical results in optimization methods. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Wei Jiang, Dingzhi Yu, Sifan Yang, Wenhao Yang, Zechao Li, Lijun Zhang ·

    Better Convergence Guarantees for Sign-Based Momentum Methods

    arXiv:2507.12091v2 Announce Type: replace-cross Abstract: This paper presents an improved analysis for sign-based methods with momentum updates. Traditional sign-based methods obtain a convergence rate of $\mathcal{O}(T^{-1/4})$ under the separable smoothness assumption, but they…

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