Convergence in Multiagent Coordination, Consensus, and Flocking

By Vincent D. Blondel, Julien M. Hendrickx, Alexander Olshevsky, John N. Tsitsiklis. Proceedings of the 44th IEEE Conference on Decision and Control, 2005

987 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/6121952a96103fc2187ebbab470e2d0db94efc95

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Citations in the corpus

Reaching a Consensus in a Dynamically Changing Environment: Convergence Rates, Measurement Delays, and Asynchronous Events (2008, methodology): “A comparison is made between the results of [ 8 ] and the main result of this paper on measurement delays, namely Theorem 2. To model dynamics when delays are present requires a somewhat difierent type of stochastic \∞ocking matrix" than that which is appropriate in the delay-free case.”
Constrained Consensus and Optimization in Multi-Agent Networks (2008, methodology): “Following Tsitsiklis [1] (see also Blondel et al. [24]), we adopt the following assumptions on the weight vectors , and on information exchange. for all and .”
Notes on averaging over acyclic digraphs and discrete coverage control (2006, methodology): “, see [4], [7], [12]), as th e averaging protocol :”
Diffusion Least-Mean Squares With Adaptive Combiners: Formulation and Performance Analysis (2010, background): “Table I shows examples of static combination rules, which keep constant; see, e.g., [15] and [ 17 ]‐[22].”
Distributed Optimization Over Time-Varying Directed Graphs (2015, background): “Specifically, we have the following properties of the matrices A(t) (see [2], [8], [15], [19], [33] for proofs of this and similar statements).”
Performance of a Distributed Stochastic Approximation Algorithm (2012, neutral): “There is a rich literature on distributed estimation and optimization algorithms, see [7],[8], [9], [10], [11], [12], [13] as a non exhaustive list.”
Coordination and Consensus of Networked Agents with Noisy Measurements: Stochastic Algorithms and Asymptotic Behavior (2009, background): “…spatially distributed agents, a basic consensus model consists of a time-invariant network in which each agent updates its state by forming a convex combination of the states of its neighbors and itself [23] [6] [50], such that the iterates of all individual states converge to a common value.”
Diffusion recursive least-squares for distributed estimation over adaptive networks (2008, methodology): “Distributed estimation algorithms of the consensus type were also proposed in [7]–[9].”
On the Convergence Time of Asynchronous Distributed Quantized Averaging Algorithms (2010, methodology): “Here, we assume that the communication graph be undirected and satisfies the following connectivity assumption also used in [3], [11], [14], [16].”
Convergence Speed of Unsteady Distributed Consensus: Decay Estimate Along the Settling Spanning-Trees (2006, background): “To the best of our knowledge, previous results are centrally based on the existence of a lower bound of the nonzero entries associated to such matrices, and most of them on the existence of self-loops, see [9] and the surveys in [6, 29] (see however the contributions in [31, 32] where the assumption”
Agreeing Asynchronously (2008, background): “As already noted, various versions of asynchronous consensus have been studied before [10], [13]–[15], [18].”
A Necessary and Sufficient Condition for Consensus Over Random Networks (2008, background): “The underlying graph of such systems at a given time instance is derived from a random graph process, independent of other time instances.”
Linear Time Average Consensus on Fixed Graphs and Implications for Decentralized Optimization and Multi-Agent Control (2014, positive): “A number of papers studied the convergence speed of discrete-time (not necessarily average) consensus for a wide class of updates over time-varying directed graphs; see [82, 40, 10, 9, 5, 13, 12, 30].”