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New algorithm Dec-BFTRL tackles decentralized online optimization

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Dec-BFTRL, a novel algorithm for decentralized online optimization. This method is designed for upper-linearizable payoffs and is particularly applicable to continuous submodular maximization problems. Dec-BFTRL achieves an expected network-aggregate regret of approximately O(sqrt(T)) over T rounds, with each agent employing neighbor-mixing steps and separation-oracle calls. AI

IMPACT Introduces a new optimization technique applicable to submodular maximization problems.

RANK_REASON The cluster contains a research paper detailing a new algorithm. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.AI →

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

New algorithm Dec-BFTRL tackles decentralized online optimization

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The cluster contains a research paper detailing a new algorithm. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yiyang Lu, Mohammad Pedramfar, Vaneet Aggarwal ·

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

    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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