English(EN) Optimal Competitive Ratios for Chasing Convex Functions with Quadratic Growth

新算法实现了凸函数追踪的最优竞争比

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-06 05:00

研究人员为凸函数追踪问题开发了新算法，该问题涉及学习者和对手。对于欧几里得运动，一种称为裁剪近端下降（Clipped Proximal Descent, CPD）的新方法对具有二次增长的光滑凸函数实现了 O(kappa^(1/3)) 的竞争比，改进了先前的界限。对于平方欧几里得运动，通过对正则化在线平衡下降（Regularized Online Balanced Descent, R-OBD）的精炼分析和匹配的下界，已经确定了具有二次增长的凸函数的无量纲最优竞争比。 AI

影响为与机器学习相关的优化算法建立了新的理论界限。

排序理由该集群包含一篇详细介绍机器学习新算法和理论结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Linzhe He, Sifan Yang, Wei Jiang, Lijun Zhang · 2026-11-06 05:00

具有二次增长的凸函数追逐的最优竞争比

arXiv:2611.08078v1 Announce Type: new Abstract: Chasing convex function has attracted much attention in recent years, which can be formulated as a game between a learner and an adversary. In each round, the adversary reveals a convex function, and the learner selects a decision a…

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具有二次增长的凸函数追逐的最优竞争比

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