English(EN) Dec-BFTRL: Squre-Root Regret for Decentralized Online Upper-Linearizable Optimization under Separation Access with Application to Continuous Submodular Maximization

新算法Dec-BFTRL解决了去中心化在线优化问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员推出了一种用于去中心化在线优化的新算法Dec-BFTRL。该方法专为上可线性化收益设计，特别适用于连续次模最大化问题。Dec-BFTRL在T轮中实现了大约O(sqrt(T))的网络聚合遗憾，其中每个代理都采用邻居混合步骤和分离预言机调用。 AI

影响引入了一种适用于次模最大化问题的新优化技术。

排序理由该集群包含一篇详细介绍新算法的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Yiyang Lu, Mohammad Pedramfar, Vaneet Aggarwal · 2026-09-01 04:00

Dec-BFTRL：分离访问下分布式在线上线性可优化问题的平方根遗憾及其在连续次模最大化中的应用

arXiv:2608.30271v1 Announce Type: cross Abstract: We study decentralized online optimization of upper-linearizable payoffs over an action set under efficient separation access, with applications to online continuous diminishing-return (DR) submodular maximization. We propose Dece…

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Dec-BFTRL：分离访问下分布式在线上线性可优化问题的平方根遗憾及其在连续次模最大化中的应用

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