共 27 条
Event-triggered zero-gradient-sum distributed consensus optimization over directed networks
被引:198
作者:
Chen, Weisheng
[1
]
Ren, Wei
[2
]
机构:
[1] Xidian Univ, Sch Aerosp Sci & Technol, Xian 710071, Peoples R China
[2] Univ Calif Riverside, Dept Elect & Comp Engn, Riverside, CA 92521 USA
来源:
基金:
美国国家科学基金会;
中国国家自然科学基金;
关键词:
Consensus;
Event-triggered scheme;
Distributed optimization;
Directed network;
CONVEX-OPTIMIZATION;
ALGORITHMS;
D O I:
10.1016/j.automatica.2015.11.015
中图分类号:
TP [自动化技术、计算机技术];
学科分类号:
0812 ;
摘要:
This paper focuses on the event-triggered zero-gradient-sum algorithms for a distributed convex optimization problem over directed networks. The communication process is driven by trigger conditions monitored by nodes. The proposed trigger conditions are decentralized and just depend on each node's own state. In the continuous-time case, we propose an algorithm based on a sample-based monitoring scheme. In the discrete-time case, we propose a new event-triggered zero-gradient-sum algorithm which is suitable for more general network models. It is proved that two proposed event-triggered algorithms are exponentially convergent if the design parameters are chosen properly and the network topology is strongly connected and weight-balanced. Finally, we illustrate the advantages of the proposed algorithms by numerical simulation. (C) 2015 Elsevier Ltd. All rights reserved.
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页码:90 / 97
页数:8
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