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Distributed Strategies for Generating Weight-Balanced and Doubly Stochastic Digraphs
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Weight-balanced and doubly stochastic digraphs are two classes of digraphs that play an essential role in a variety of cooperative control problems, including formation control, distributed averaging, and optimization. We refer to a digraph as doubly stochasticable (weight-balanceable) if it admits a doubly stochastic (weight-balanced) adjacency matrix. This paper studies the characterization of both classes of digraphs, and introduces distributed algorithms to compute the appropriate set of weights in each case.
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“However, in a general directed graph, construction of a doubly stochastic matrix needs a weight balancing procedure and it is costly [22].”
From Achieving Geometric Convergence for Distributed Optimization Over Time-Varying Graphs (2017) · cites this paper as [22]
Experimental — accuracy not yet measuredIn the citing paper
“the works (Gharesifard and Cort´ es, 2012; Rikos et al., 2014) present provably correct distributed strategies that, given a directed communication topology, allow a network of agents to find such weight edge assignments.”
From Event-triggered communication and control of networked systems for multi-agent consensus (2019) · cites this paper as Gharesifard and Cortés, 2012
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Cites
Consensus Problems in Networks of Agents With Switching Topology and Time-Delays
Olfati-Saber, R., & Murray, R. M. (2004) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2004.834113 · Cited by 12,788 · cited 1×
“Such coordination problems have also become a major research area in engineering because of the connections with distributed robotics, networked systems, and autonomy, see e.g., [6, 25, 33] and references therein.”
Consensus and Cooperation in Networked Multi-Agent Systems
Olfati-Saber, R., Fax, J. A., & Murray, R. M. (2007) · Proceedings of the IEEE · doi:10.1109/jproc.2006.887293 · Cited by 10,522 · cited 1×
“Such coordination problems have also become a major research area in engineering because of the connections with distributed robotics, networked systems, and autonomy, see e.g., [6, 25, 33] and references therein.”
Diestel, R. (1997) · Cited by 5,082
(1992) · Lecture notes in computer science · doi:10.1007/bfb0022433 · Cited by 3,986
Distributed Subgradient Methods for Multi-Agent Optimization
Nedic, A., & Ozdaglar, A. (2009) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2008.2009515 · Cited by 3,719
Strogatz, S. H. (2000) · Physica D Nonlinear Phenomena · doi:10.1016/s0167-2789(00)00094-4 · Cited by 3,117
Self-Organization in Biological Systems
Sneyd, J., Theraula, G., Bonabeau, E., Deneubourg, J.-L., & Franks, N. R. (2001) · doi:10.1515/9780691212920 · Cited by 2,949
Biggs, N. (1974) · doi:10.1017/cbo9780511608704 · Cited by 2,940
Graph Theoretic Methods in Multiagent Networks
Mesbahi, M., & Egerstedt, M. (2010) · Princeton University Press eBooks · doi:10.1515/9781400835355 · Cited by 2,897
Distributed Consensus in Multi-vehicle Cooperative Control
Ren, W., & Beard, R. W. (2008) · Communications and Control Engineering · doi:10.1007/978-1-84800-015-5 · Cited by 2,826
Research works printed but not linked: 2
- Birkhoff, 1946, Tres observaciones sobre el algebra lineal, Universidad Nacional de Tucumán, Revista A, 5, 147
- Diestel, 2005, Vol. 173
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Metadata
- Authors
- Gharesifard, B., & Cortés, J.
- Affiliations
- University of California San Diego (US)
- Corresponding author
- Gharesifard, Bahman
- Venue
- European Journal of Control
- Publisher
- Elsevier BV
- Published
- 2012-01
- Volume
- 18
- Issue
- 6
- Pages
- 539-557
- ISSN
- 0947-3580 (ISSN-L)
- OpenAlex
- W2124972943
- Cited by
- 171
- References
- 34
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 11 · 2023: 11 · 2022: 10 · 2021: 13 · 2020: 16 · 2019: 11 · 2018: 17 · 2017: 12 · 2016: 10 · 2015: 12 · 2014: 12 · 2013: 8 · 2012: 7
- FWCI
- 16.38
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- European Journal of Control (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (0917166, 0908508)
- Division of Civil, Mechanical and Manufacturing Innovation
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Distributed Control Multi-Agent Systems
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Opinion Dynamics and Social Influence
- Social Sciences › Decision Sciences › Management Science and Operations Research › Game Theory and Applications
- Keywords
- distributed dynamical systems, directed graphs, formation control, distributed optimization, self-loops
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