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Dynamic Regret Analysis for Event-Triggered Distributed Online Optimization Algorithm
Makoto YAMASHITA Naoki HAYASHI Shigemasa TAKAI
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2021/02/01
Online ISSN: 1745-1337
Type of Manuscript: Special Section PAPER (Special Section on Mathematical Systems Science and its Applications)
online convex optimization, multi-agent systems, event-triggered communication,
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This paper considers a distributed subgradient method for online optimization with event-triggered communication over multi-agent networks. At each step, each agent obtains a time-varying private convex cost function. To cooperatively minimize the global cost function, these agents need to communicate each other. The communication with neighbor agents is conducted by the event-triggered method that can reduce the number of communications. We demonstrate that the proposed online algorithm achieves a sublinear regret bound in a dynamic environment with slow dynamics.