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Complexity
Volume 2017, Article ID 5385708, 12 pages
https://doi.org/10.1155/2017/5385708
Research Article

Event-Triggered Discrete-Time Distributed Consensus Optimization over Time-Varying Graphs

1Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
2State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing, China

Correspondence should be addressed to Huaqing Li; moc.621@yppah_ilgniqauh

Received 5 November 2016; Accepted 28 March 2017; Published 21 May 2017

Academic Editor: Sigurdur F. Hafstein

Copyright © 2017 Qingguo Lü and Huaqing Li. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper focuses on a class of event-triggered discrete-time distributed consensus optimization algorithms, with a set of agents whose communication topology is depicted by a sequence of time-varying networks. The communication process is steered by independent trigger conditions observed by agents and is decentralized and just rests with each agent’s own state. At each time, each agent only has access to its privately local Lipschitz convex objective function. At the next time step, every agent updates its state by applying its own objective function and the information sent from its neighboring agents. Under the assumption that the network topology is uniformly strongly connected and weight-balanced, the novel event-triggered distributed subgradient algorithm is capable of steering the whole network of agents asymptotically converging to an optimal solution of the convex optimization problem. Finally, a simulation example is given to validate effectiveness of the introduced algorithm and demonstrate feasibility of the theoretical analysis.