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Convergence Rate of Discrete-Time Stochastic Approximation Type Consensus Algorithms ?

机译:离散时间随机近似类型共识算法的收敛速度

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In this paper, we study the convergence rate of the distributed stochastic approximation (SA) type algorithm for the discrete-time multi-agent consensus with communication noises. Basic results of algebraic graph theory and probability limit theory are used to study the closed-form solution of the consensus error. Under mild conditions on the decreasing step size and the network topology, we give upper bounds for the mean square and almost sure convergence rates of the consensus errors. Furthermore, for the case with balanced graphs, the exact convergence rate is provided for the mean square of the consensus error.
机译:本文研究具有通信噪声的离散时间多智能体共识的分布式随机逼近(SA)型算法的收敛速度。利用代数图论和概率极限理论的基本结果研究共识误差的闭式解。在逐步减小步长和网络拓扑的温和条件下,我们给出均方值的上限,并且确定共识误差的收敛速度。此外,对于具有平衡图的情况,为共识误差的均方根提供了精确的收敛速度。

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