Online distributed ADMM via dual averaging
Online distributed ADMM via dual averaging
复制标题
通过双重平均的在线分布式 ADMM
DOI：
10.1109/cdc.2014.7039496
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
M. Mesbahi
中科院分区：
文献类型：
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作者：
Saghar Hosseini;Airlie Chapman;M. Mesbahi
本文给出了求解线性约束凸优化问题的分布式交替方向乘子法（ADMM）算法的收敛性分析。目标是在决策者网络上分布式优化全局目标函数。全局目标函数由与每个代理相关联的凸成本函数组成。局部成本函数可以被分解为两个凸函数，其中一个随着时间的推移被揭示给决策者，另一个是先验已知的。我们扩展了在线ADMM算法的分布式设置的基础上双平均。然后，我们探讨的收敛速度的性能的算法产生的决定序列的最佳固定的决定在事后。这个表现指标被称为后悔。该算法的遗憾上界作为底层网络拓扑结构和线性约束的函数。在线分布式ADMM算法，然后应用到编队捕获问题。
This paper presents a convergence analysis on a distributed Alternating Direction Method of Multipliers (ADMM) algorithm which solves online convex optimization problems under linear constraints. The goal is to distributively optimize a global objective function over a network of decision makers. The global objective function is composed of convex cost functions associated with each agent. The local cost functions can be broken down into two convex functions, one of which is revealed over time to the decision makers and one known a priori. We extend an online ADMM algorithm to a distributed setting based on dual-averaging. We then explore the rate of convergence of the performance of the sequence of decisions generated by the algorithm to the best fixed decision in hindsight. This performance metric is called regret. An upper bound on the regret of the proposed algorithm is presented as a function of the underlying network topology and linear constraints. The online distributed ADMM algorithm is then applied to a formation acquisition problem.