On identifying sparse representations of consensus networks

Neil Dhingra, Fu Lin, Makan Fardad, Mihailo R. Jovanović

Research output: Chapter in Book/Entry/PoemConference contribution

13 Scopus citations

Abstract

We consider the problem of identifying optimal sparse graph representations of dense consensus networks. The performance of the sparse representation is characterized by the global performance measure which quantifies the difference between the output of the sparse graph and the output of the original graph. By minimizing the sum of this performance measure and a sparsity-promoting penalty function, the alternating direction method of multipliers identifies sparsity structures that strike a balance between the performance measure and the number of edges in the graph. We then optimize the edge weights of sparse graphs over the identified topologies. Two examples are provided to illustrate the utility of the developed approach.

Original languageEnglish (US)
Title of host publication3rd IFAC Workshop on Distributed Estimation and Control in Networked Systems, NECSYS 2012
PublisherIFAC Secretariat
Pages305-310
Number of pages6
Edition26
ISBN (Print)9783902823229
DOIs
StatePublished - 2012
Externally publishedYes
Event3rd IFAC Workshop on Distributed Estimation and Control in Networked Systems, NECSYS 2012 - Santa Barbara, CA, United States
Duration: Sep 14 2012Sep 15 2012

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
Number26
Volume45
ISSN (Print)1474-6670

Other

Other3rd IFAC Workshop on Distributed Estimation and Control in Networked Systems, NECSYS 2012
Country/TerritoryUnited States
CitySanta Barbara, CA
Period9/14/129/15/12

Keywords

  • Alternating direction method of multipliers
  • Cardinality minimization
  • Consensus networks
  • Sparse graph representations
  • Sparsity-promoting optimal control
  • Structured feedback design

ASJC Scopus subject areas

  • Control and Systems Engineering

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