Distributed Subgradient Method With Edge-Based Event-Triggered Communication
Distributed Subgradient Method With Edge-Based Event-Triggered Communication
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DOI：
10.1109/tac.2018.2800760
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发表时间：
2018-02
影响因子：
6.8
通讯作者：
Y. Kajiyama;N. Hayashi;S. Takai
中科院分区：
文献类型：
--
作者：
Y. Kajiyama;N. Hayashi;S. Takai
本文提出了一种分布式次梯度方法，用于事件触发通信的约束优化。在所提出的方法中，每个智能体都有对最佳解决方案的估计作为状态，并通过基于共识的次梯度算法以及对公共约束集的投影来迭代更新它。当当前状态与上次触发状态之间的差异超过阈值时，通过基于边缘的触发机制来进行本地通信。我们证明，在步长和触发条件阈值的递减和可求和条件下，所有智能体的状态渐近收敛到最优解之一。我们还研究了每个代理的时间平均状态的收敛速度。仿真结果表明，与时间触发算法相比，所提出的事件触发算法可以减少通信次数。
This paper proposes a distributed subgradient method for constrained optimization with event-triggered communications. In the proposed method, each agent has an estimate of an optimal solution as a state and iteratively updates it by a consensus-based subgradient algorithm with a projection to a common constraint set. The local communications are carried out by the edge-based triggering mechanism when the difference between the current state and the last triggered state exceeds a threshold. We show that the states of all agents asymptotically converge to one of the optimal solutions under a diminishing and summability condition on a stepsize and a threshold for a trigger condition. We also investigate the convergence rate with respect to the time-averaged state of each agent. The simulation results show that the proposed event-triggered algorithm can reduce the number of communications compared to the time-triggered algorithms.