Distributed average consensus with least-mean-square deviation

By Lin Xiao, Stephen P. Boyd, Seung-Jean Kim. J. Parallel Distributed Comput., 2007

1,170 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/5359fb2362ee22a18a5cc1bf9ff7f69d7ce533bf

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In Computer Networks and Communications

In Statistical and Nonlinear Physics

Citations in the corpus

Sensor Networks With Random Links: Topology Design for Distributed Consensus (2007, methodology): “ distributed algorithm that has been considered by Tsitsiklis in his PhD thesis, [1], see also [2], found application recently in several areas, and is the subject of active research, e.g,, [3], [4], [5], [6]. This topology design for sensor networks has not received much attention in the literature”
EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization (2014, background): “(iii) Metropolis constant edge weight matrix [3,34], for some small positive (cid:15) > 0.”
Distributed Synchronization in Networks of Agent Systems With Nonlinearities and Random Switchings (2013, background): “In [47], the problem of ﬁnding the edge weights that result in the least mean-square deviation in a steady state of a stochastic model for distributed average consensus is investigated.”
A Decentralized Proximal-Gradient Method With Network Independent Step-Sizes and Separated Convergence Rates (2017, background): “Some examples include distributed average consensus [2]–[4], distributed spectrum sensing [5], information control [6], [7], power systems control [8], [9], statistical inference and learning [10]–[12].”
Design of Optimal Sparse Feedback Gains via the Alternating Direction Method of Multipliers (2011, background): “Since the leaderless network with only relative information exchange is not asymptotically stable, the average of all states x̄ := (1/N)1x in the presence of stochastic disturbances undergoes a random walk [33].”
Resilient Distribution System by Microgrids Formation After Natural Disasters (2016, methodology): “In [20] and [30], an improved Metropolis–Hastings method was proposed to adapt to changes of system configuration as”
DEXTRA: A Fast Algorithm for Optimization Over Directed Graphs (2017, background): “1See [26]–[29], for additional information on average-consensus problems.”
A Framework for Parallel and Distributed Training of Neural Networks (2016, background): “The double stochasticity condition in (9) can be achieved according to a variety of predefined strategies, including the Metropolis-Hastings criterion (Xiao et al., 2007), or by optimizing a cost function with respect to the spectral properties of the graph (Xiao and Boyd, 2004).”
Distributed Compressive Spectrum Sensing in Cooperative Multihop Cognitive Networks (2011, methodology): “local copy to consent with a weighted average of all neighboring decisions, a step reminiscent of the updating formula in the decentralized consensus averaging technique [15].”
Consensus problem of high‐order multi‐agent systems with external disturbances: An H∞ analysis approach (2010, background): “In [23], a stochastic model was considered for the average consensus of discrete-time multi-agent systems, in which each new updated value was corrupted by an additive noise with zero mean.”
Distributed large-scale tensor decomposition (2014, methodology): “Then, distributed algorithms are obtained using the concept of average consensus that has been extensively studied in computer science and is a central topic for load balancing (with divisible tasks) in parallel computers [9].”
Upper bounds on algebraic connectivity via convex optimization (2006, methodology): “(see [ XBK05 ]). This is a convex function of L, so our method can be used to nd a lower bound on it.”
Optimal Synchronization for Networks of Noisy Double Integrators (2011, background): “[15], but only for first order consensus dynamics.”
EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization (2014, background): “Examples and works include decentralized averaging [7, 15, 34], learning”
A geometrically convergent method for distributed optimization over time-varying graphs (2016, background): “Examples and works include distributed averaging [1], [2], distributed spectrum sensing [3], formation control [4], [5], power system control [6], [7], statistical inference and learning [8]–[10], and so on.”
Diffusion Strategies for Distributed Kalman Filtering and Smoothing (2010, background): “consensus-based solutions, several averaging iterations are generally required for each new measurement in order to obtain the estimate [24]–[26].”