English(EN) A First-Order Learning Algorithm for Online Resource Allocation with Constant Regret

新的学习算法在资源分配中实现常数遗憾

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员为在线资源分配问题开发了一种新的对偶一阶学习算法。该算法相对于事后最优值实现了常数遗憾，这意味着其性能会随着时间的推移而最小化下降，而与问题的持续时间无关。与以前的方法不同，它不需要求解线性规划或做出非退化假设，为动态环境中的资源管理提供了一种更有效且广泛适用的方法。 AI

影响该算法为动态资源分配提供了一种更有效的方法，有可能改进管理计算或数据资源的AI系统。

排序理由该集群包含一篇详细介绍新算法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Menglong Li, Jiawei Zhang · 2026-09-10 04:00

面向在线资源分配的具有恒定遗憾的一阶学习算法

arXiv:2609.05895v1 Announce Type: new Abstract: We study a finite-horizon online resource allocation problem with initial resource capacities proportional to the horizon. In each period, a request type is observed and one action is chosen from a finite menu. Each action earns a r…