Accelerating distributed average consensus by exploring the information of second-order neighbors

The problem of accelerating distributed average consensus by using the information of second-order neighbors in both the discrete- and continuous-time cases is addressed in this Letter. In both two cases, when the information of second-order neighbors is used in each iteration, the network will conv...

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Bibliographic Details
Published in:Physics letters. A 2010-05, Vol.374 (24), p.2438-2445
Main Authors: Yuan, Deming, Xu, Shengyuan, Zhao, Huanyu, Chu, Yuming
Format: Article
Language:English
Subjects:
Algorithms
Average consensus
Computer simulation
Convex optimization
Networks
Optimization
Second-order neighbors
Solid state physics
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Summary:The problem of accelerating distributed average consensus by using the information of second-order neighbors in both the discrete- and continuous-time cases is addressed in this Letter. In both two cases, when the information of second-order neighbors is used in each iteration, the network will converge with a speed faster than the algorithm only using the information of first-order neighbors. Moreover, the problem of using partial information of second-order neighbors is considered, and the edges are not chosen randomly from second-order neighbors. In the continuous-time case, the edges are chosen by solving a convex optimization problem which is formed by using the convex relaxation method. In the discrete-time case, for small network the edges are chosen optimally via the brute force method. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed algorithm.
ISSN:0375-9601
1873-2429
DOI:10.1016/j.physleta.2010.03.053