Synchronization and convergence of linear dynamics in random directed networks

By Chai Wah Wu. 2006

243 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Molecular Biology

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Citations in the corpus

Information consensus in multivehicle cooperative control (2007, background): “Information consensus is also studied from a stochastic point of view in [34]–[36], which consider a random network, in which the existence of an information channel between a pair of vehicles at each time is probabilistic and independent of other channels, resulting in a time-varying undirected com”
Stochastic Consensus Seeking With Noisy and Directed Inter-Agent Communication: Fixed and Randomly Varying Topologies (2010, background): “Third, convergence results are proved with both measurement noise and random independent link failures; for related random graph based modeling, see [14], [20], [24], [25], [31], [34].”
Distributed Consensus Algorithms in Sensor Networks With Imperfect Communication: Link Failures and Channel Noise (2007, methodology): “We present two different compromises to this tradeoff: theA−ND algorithm modifies conventional consensus by forcing the weights to satisfy apersistencecondition (slowly decaying to zero”
Distributed Control of Robotic Networks: A Mathematical Approach to Motion Coordination Algorithms (2009, background): “and Wu, C. [2003] Distributed algorithms for dynamic reassignment, in IEEE Conf.”
Stochastic consensus over noisy networks with Markovian and arbitrary switches (2010, background): “Tahbaz-Salehi and Jadbabaie (2008) considered averaging with a sequence of independent coefficient matrices, which indicates that the overall network topology evolves independently; such modelling was also adopted for linear synchronization (Wu, 2006).”
Distributed Control of Robotic Networks (2009, background): “and Wu, C. [2003] Distributed algorithms for dynamic reassignment, in IEEE Conf.”
Consensus Over Ergodic Stationary Graph Processes (2010, background): “Wu [10] and Porfiri and Stilwell [11] extend the results of [9] to more general settings.”
Distributed Subgradient Methods for Convex Optimization Over Random Networks (2011, methodology): “[5]), or due to the unpredictability in the environment that the information exchange takes place (see Hatano and Mesbahi [9], Wu [25], Tahbaz-Salehi and Jadbabaie [21] and Fagnani and Zampieri [7]).”
A tutorial on modeling and analysis of dynamic social networks. Part I (2017, background): “t; 0 (in probability) P lim k→∞ |xi(k) −xj(k)| = 0 = 1 (almost surely) E|xi(k) −xj(k)|p −−−→ k→∞ 0 (in the p-th moment) The following fundamental result [177, 188, 189] (see also special cases in [190, 191]) extends Proposition 3 to the case of randomized French-DeGroot model, reducing it to a det”
A Necessary and Sufficient Condition for Consensus Over Random Networks (2008, background): “Over the past few years, they have attracted a significant amount of attention in various contexts such as motion coordination of autonomous agents [5], [6], distributed computation of averages and least squares among sensors [7]–[9], and rendezvous problems [10].”
Asynchronous Consensus Protocols Using Nonlinear Paracontractions Theory (2008, methodology): “Another side contribution is that we formulate and address the consensus problem in a geometric framework, where the problem is typically addressed using matrix theory [12], [22], Lyapunov functions [11], [21], or graph theory [19].”