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Distributed Online Mirror Descent Algorithm with Event Triggered Communication
A.K. Paul, A.D. Mahindrakar,
Published in Elsevier B.V.
2022
Volume: 55
   
Issue: 30
Pages: 448 - 453
Abstract
The paper proposes an algorithm that uses distributed online mirror descent algorithm for solving constrained online optimization problem with event triggered communication. The optimization is over a time horizon and the future objective functions are not apriori known to each agent. In the proposed algorithm, the communication between the agents, that happens in a distributed optimization framework, occurs only when the difference between the current state and the state when the last event has been triggered exceeds a threshold. The performance of the algorithm is analysed using a regret function. We establish a bound on the regret and provide sufficient conditions on the step-size and thresholding error such that the regret is sublinear. We demonstrate the reduction in the number of inter-agent communications using our proposed algorithm for an estimation problem in a dynamic environment. © 2022 Elsevier B.V.. All rights reserved.
About the journal
JournalIFAC-PapersOnLine
PublisherElsevier B.V.
ISSN24058963