Linear Stochastic Approximation Algorithms and Group Consensus Over Random Signed Networks
| Autor: | Xiaoming Duan, Francesco Bullo, Wenjun Mei, Ge Chen |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Stochastic process Multi-agent system Approximation algorithm 02 engineering and technology Stochastic approximation Computer Science Applications 020901 industrial engineering & automation Rate of convergence Control and Systems Engineering Convergence (routing) Noise (video) Electrical and Electronic Engineering Algorithm Real number |
| Zdroj: | IEEE Transactions on Automatic Control. 64:1874-1889 |
| ISSN: | 2334-3303 0018-9286 |
| DOI: | 10.1109/tac.2018.2867257 |
| Popis: | This paper studies linear stochastic approximation (SA) algorithms and their application to multiagent systems in engineering and sociology. As main contribution, we provide necessary and sufficient conditions for convergence of linear SA algorithms to a deterministic or random final vector. We also characterize the system convergence rate, when the system is convergent. Moreover, differing from non-negative gain functions in traditional SA algorithms, this paper considers also the case when the gain functions are allowed to take arbitrary real numbers. Using our general treatment, we provide necessary and sufficient conditions to reach consensus and group consensus for first-order discrete-time multiagent system over random signed networks and with state-dependent noise. Finally, we extend our results to the setting of multidimensional linear SA algorithms and characterize the behavior of the multidimensional Friedkin–Johnsen model over random interaction networks. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|