论文标题
在多代理网络上的分布式聚合优化
Distributed Aggregative Optimization over Multi-Agent Networks
论文作者
论文摘要
本文提出了一个用于分布式优化的新框架，称为分布式聚合优化，该框架允许局部目标函数不仅取决于其自己的决策变量，而且还取决于所有其他代理的决策变量的总结函数的平均值。为了解决这个问题，提出和分析了一种分布式算法，称为分布式梯度跟踪（DGT），其中全局目标函数强烈凸出，并且通信图是平衡且连接的。结果表明，算法可以以线性速率收敛到最佳变量。提供了一个数值示例来证实理论结果。
This paper proposes a new framework for distributed optimization, called distributed aggregative optimization, which allows local objective functions to be dependent not only on their own decision variables, but also on the average of summable functions of decision variables of all other agents. To handle this problem, a distributed algorithm, called distributed gradient tracking (DGT), is proposed and analyzed, where the global objective function is strongly convex, and the communication graph is balanced and strongly connected. It is shown that the algorithm can converge to the optimal variable at a linear rate. A numerical example is provided to corroborate the theoretical result.
