The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Distributed Strategies for Generating Weight-Balanced and Doubly Stochastic Digraphs
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Fast linear iterations for distributed averaging
Xiao, L., & Boyd, S. (2004) · Systems & Control Letters · doi:10.1016/j.sysconle.2004.02.022 · Cited by 2,706
Boyd, S., Ghosh, A., Prabhakar, B., & Shah, D. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.874516 · Cited by 2,439
A new approach to the maximum-flow problem
Goldberg, A. V., & Tarjan, R. E. (1988) · Journal of the ACM · doi:10.1145/48014.61051 · Cited by 1,882
Distributed Control of Robotic Networks: A Mathematical Approach to Motion Coordination Algorithms
Bullo, F., Cortés, J., & Martínez, S. (2009) · Princeton University Press eBooks · doi:10.1515/9781400831470 · Cited by 1,509
Self-citationDistributed Computing: A Locality-Sensitive Approach
Peleg, D. (2000) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9780898719772 · Cited by 1,487
Leader-following coordination of multi-agent systems with coupling time delays
Hu, J., & Hong, Y. (2007) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2006.08.015 · Cited by 945
Dynamical aspects of animal grouping: Swarms, schools, flocks, and herds
OKUBO, A. (1986) · Advances in Biophysics · doi:10.1016/0065-227x(86)90003-1 · Cited by 880
Introduction to Distributed Algorithms
Tel, G. (2000) · Cambridge University Press eBooks · doi:10.1017/cbo9781139168724 · Cited by 859
On Distributed Convex Optimization Under Inequality and Equality Constraints
Zhu, M., & Martinez, S. (2012) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2011.2167817 · Cited by 759
Self-Organized Fish Schools: An Examination of Emergent Properties
Parrish, J. K., Viscido, S. V., & Grünbaum, D. (2002) · The Biological Bulletin · doi:10.2307/1543482 · Cited by 716
Distributed algorithms for reaching consensus on general functions
Cortés, J. (2008) · Automatica · doi:10.1016/j.automatica.2007.07.022 · Cited by 469
Self-citationA Randomized Incremental Subgradient Method for Distributed Optimization in Networked Systems
Johansson, B., Rabi, M., & Johansson, M. (2010) · SIAM Journal on Optimization · doi:10.1137/08073038x · Cited by 336
Decentralized Control of Connectivity for Multi-Agent Systems
Gennaro, M. C. D., & Jadbabaie, A. (2006) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2006.377041 · Cited by 322
The Dynamics of Herds: From Individuals to Aggregations
Gueron, S., Levin, S. A., & Rubenstein, D. I. (1996) · Journal of Theoretical Biology · doi:10.1006/jtbi.1996.0144 · Cited by 316
Stable Flocking of Multiple Inertial Agents on Balanced Graphs
Lee, D., & Spong, M. W. (2007) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2007.902752 · Cited by 278
Shi, H., Wang, L., & Chu, T. (2005) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2005.10.012 · Cited by 178
Algebraic connectivity of directed graphs
Wu, C. W. (2005) · Linear and Multilinear Algebra · doi:10.1080/03081080500054810 · Cited by 161
When does a digraph admit a doubly stochastic adjacency matrix?
Gharesifard, B., & Cortés, J. (2010) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.2010.5530578 · Cited by 85
Self-citationSelf-organization of Front Patterns in Large Wildebeest Herds
Gueron, S., & Levin, S. A. (1993) · Journal of Theoretical Biology · doi:10.1006/jtbi.1993.1206 · Cited by 83
Zhu, M., & Martı́nez, S. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1001.2612 · Cited by 45
Upper bounds on algebraic connectivity via convex optimization
Ghosh, A., & Boyd, S. (2006) · Linear Algebra and its Applications · doi:10.1016/j.laa.2006.03.006 · Cited by 37
On a Class of Directed Graphs—With an Application to Traffic-Flow Problems
Hooi-Tong, L. (1970) · Operations Research · doi:10.1287/opre.18.1.87 · Cited by 34
Distributed strategies for making a digraph weight-balanced
Gharesifard, B., & Cortés, J. (2009) · Allerton Conference on Communication, Control, and Computing · doi:10.1109/allerton.2009.5394936 · Cited by 31
Self-citationA Distributed Algorithm for the Maximum Flow Problem
Lavallée, I. (2006) · International Symposium on Parallel and Distributed Computing · doi:10.1109/ispdc.2005.4 · Cited by 26
Topology design for fast convergence of network consensus algorithms
Cao, M., & Wu, C. W. (2007) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2007.378145 · Cited by 22
The Contraction Coefficient of a Complete Gossip Sequence,
Liu, J., Morse, A. S., Anderson, B. D. O., & Yu, C. B. (2010) · Springer eBooks · doi:10.1007/978-3-642-11278-2_19 · Cited by 9
Maximum flow problems under special nonnegative lower bounds on arc flows
Bornstein, C. T., Ribeiro, A. S., & Lucena, A. (2001) · Electronic Notes in Discrete Mathematics · doi:10.1016/s1571-0653(04)00226-4 · Cited by 1
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
Cited by
Achieving Geometric Convergence for Distributed Optimization Over Time-Varying Graphs
Nedić, A., Olshevsky, A., & Shi, W. (2017) · SIAM Journal on Optimization · doi:10.1137/16m1084316 · Cited by 1,069
“While the subgradient-push eliminates the requirement of graph balancing [22], it suffers from a slow sublinear(2) convergence rate even for strongly convex smooth functions due to its employment of diminishing step-size [41].”
Event-triggered communication and control of networked systems for multi-agent consensus
Nowzari, C., Garcia, E., & Cortés, J. (2019) · Automatica · doi:10.1016/j.automatica.2019.03.009 · Cited by 655
Self-citationTutorial on dynamic average consensus: the problem, its applications,\n and the algorithms
Kia, S. S., Scoy, B. V., Cortés, J., Freeman, R. A., Lynch, K., & Martı́nez, S. (2018) · eScholarship (California Digital Library) · Cited by 404
Self-citationQin, J., Fu, W., Gao, H., & Zheng, W. X. (2016) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2016.2526683 · Cited by 338
Distributed Optimization for Control
Nedić, A., & Liu, J. (2018) · Annual Review of Control, Robotics, and Autonomous Systems · doi:10.1146/annurev-control-060117-105131 · Cited by 302
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
