Morisi, Rita and Gnecco, Giorgio and Bemporad, Alberto A hierarchical consensus method for the approximation of the consensus state, based on clustering and spectral graph theory. Engineering Applications of Artificial Intelligence, 56. 157 - 174. ISSN 0952-1976 (2016)
Full text not available from this repository.Abstract
A hierarchical method for the approximate computation of the consensus state of a network of agents is investigated. The method is motivated theoretically by spectral graph theory arguments. In a first phase, the graph is divided into a number of subgraphs with good spectral properties, i.e., a fast convergence toward the local consensus state of each subgraph. To find the subgraphs, suitable clustering methods are used. Then, an auxiliary graph is considered, to determine the final approximation of the consensus state in the original network. A theoretical investigation is performed of cases for which the hierarchical consensus method has a better performance guarantee than the non-hierarchical one (i.e., it requires a smaller number of iterations to guarantee a desired accuracy in the approximation of the consensus state of the original network). Moreover, numerical results demonstrate the effectiveness of the hierarchical consensus method for several case studies modeling real-world networks.
| Item Type: | Article |
|---|---|
| Identification Number: | https://doi.org/10.1016/j.engappai.2016.08.018 |
| Additional Information: | SCOPUS ID: 2-s2.0-84987968759 |
| Uncontrolled Keywords: | Consensus problem; Approximation; Hierarchical consensus; Clustering; Spectral graph theory |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Research Area: | Computer Science and Applications |
| Depositing User: | Caterina Tangheroni |
| Date Deposited: | 04 Oct 2016 09:40 |
| Last Modified: | 04 Oct 2016 09:40 |
| URI: | http://eprints.imtlucca.it/id/eprint/3547 |
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