Skip to main navigation Skip to search Skip to main content

Distributed Online Convex Optimization with Adaptive Event-Triggered Scheme

  • Wei Suo
  • , Wenling Li*
  • *Corresponding author for this work
  • Beihang University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper is concerned with the distributed online convex optimization problem in which a series of agents try to track the minimizer of a global convex function. A consensus then adaptive with gradient exchange (CTAGE) algorithm is proposed where the gradient information exchanged among agents is adopted to facilitate reaching consensus. To alleviate the communication overhead among agents, an adaptive event-Triggered scheme (AETS) is adopted, which can dynamically adjust the threshold. Specifically, the threshold would hold when there are consecutive triggers, and it would decrease while the triggering condition is not satisfied for several steps. Then, theoretical results indicate that dynamic regret of CTAGE can reach sublinear upper bound. Finally, a target tracking example is utilized to demonstrate the usefulness of the proposed algorithm.

Original languageEnglish
Title of host publicationProceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages33-38
Number of pages6
ISBN (Electronic)9798350321050
DOIs
StatePublished - 2023
Event12th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2023 - Xiangtan, China
Duration: 12 May 202314 May 2023

Publication series

NameProceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023

Conference

Conference12th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2023
Country/TerritoryChina
CityXiangtan
Period12/05/2314/05/23

Keywords

  • Adaptive event-Triggered scheme
  • Distributed online optimization
  • Dynamic regret

Fingerprint

Dive into the research topics of 'Distributed Online Convex Optimization with Adaptive Event-Triggered Scheme'. Together they form a unique fingerprint.

Cite this