优化研究确立了新的梯度下降下界

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-03 04:00

研究人员在光滑凸优化中确立了梯度下降的新下界，改进了现有的理论极限。该研究证明了 $\Omega(n^{-1.6342})$ 的非即时下界和 $\Omega(n^{-1.2408})$ 的即时下界。这些发现代表了对先前下界的改进，并清楚地区分了不同设置下可实现的收敛速率。 AI

影响改进了对训练人工智能模型至关重要的优化算法的理论理解。

排序理由详细介绍优化算法理论进展的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 Nederlands(NL) · Yuhan Ye, Kaizhao Liu · 2026-09-03 04:00

改进的梯度下降下界超越Nesterov

arXiv:2609.02855v1 Announce Type: cross Abstract: We study how far gradient descent (GD) can be accelerated by predetermined stepsizes in smooth convex optimization. Going beyond the classical $\Omega(n^{-2})$ first-order oracle lower bound of Nemirovsky and Yudin, we prove an $\…