eprintid: 3547 rev_number: 5 eprint_status: archive userid: 69 dir: disk0/00/00/35/47 datestamp: 2016-10-04 09:40:34 lastmod: 2016-10-04 09:40:34 status_changed: 2016-10-04 09:40:34 type: article metadata_visibility: show creators_name: Morisi, Rita creators_name: Gnecco, Giorgio creators_name: Bemporad, Alberto creators_id: creators_id: giorgio.gnecco@imtlucca.it creators_id: alberto.bemporad@imtlucca.it title: A hierarchical consensus method for the approximation of the consensus state, based on clustering and spectral graph theory ispublished: pub subjects: QA75 divisions: CSA full_text_status: none keywords: Consensus problem; Approximation; Hierarchical consensus; Clustering; Spectral graph theory note: SCOPUS ID: 2-s2.0-84987968759 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). 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Comput., 17 (2007), pp. 395–416 citation: 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)