Convergence rates of discrete-time stochastic approximation consensus algorithms: Graph-related limit bounds-Reference-Cited by-同舟云学术

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Convergence rates of discrete-time stochastic approximation consensus algorithms: Graph-related limit bounds

Published:2018-02 Issue: Volume:112 Page:9-17
ISSN:0167-6911
Container-title:Systems & Control Letters
language:en
Short-container-title:Systems & Control Letters

Author:

Tang Huaibin^ORCID,Li Tao^ORCID

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Young Scholars Program of Shandong University, Shanghai Rising-Star Program

Shu Guang project of Shanghai Municipal Education Commission and Shanghai Education Development Foundation

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Mechanical Engineering,General Computer Science,Control and Systems Engineering

Reference27 articles.

1. Distributed average consensus with dithered quantization;Aysal;IEEE Trans. Signal Process.,2008

2. Coordination and consensus of networked agents with noisy measurements: stochastic algorithms and asymptotic behavior;Huang;SIAM J. Control Optim.,2009

3. Mean square average-consensus under measurement noises and fixed topologies: necessary and sufficient conditions;Li;Automatica,2009

4. Distributed consensus algorithms in sensor networks: quantized data and random link failures;Kar;IEEE Trans. Signal Process.,2010

5. Distributed consensus for multi-agent systems with delays and noises in transmission channels;Liu;Automatica,2011

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1. Discrete-time stochastic consensus: A Kalman-filter-based two-time-scale protocol;Systems & Control Letters;2023-04

2. Asymptotic properties of dual averaging algorithm for constrained distributed stochastic optimization;Systems & Control Letters;2022-07

3. An improved consensus algorithm for MAS with directed topology and binary-valued communication;Neurocomputing;2022-01

4. Graph partitions and the controllability of directed signed networks;Science China Information Sciences;2019-03-01

5. Multi-agent system self-tuning consensus algorithm;Journal of Physics: Conference Series;2019-03

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