无中心优化的算子分裂方法

在某些多智能体系统中，由于受到通讯等因素的限制，单个智能体只能进行本地计算，再与相邻智能体交换数据.与传统的并行和分布式计算不同，这种数据交换方式不再使用中心节点或者共享内存，而仅限于相邻节点之间.这种通过局部数据交换而实现全网目标的方式叫做无中心计算.比如，从任意的多个数开始，所有智能体通过不断地计算其局部平均，就都能收敛到这些数的平均值.无中心计算有不易形成通讯和计算瓶颈的优点，更适合分布的节点，因此受到一些应用的欢迎. 本文介绍求解一致最优化问题的若干无中心算法.一致最优化问题的目标是全网所有节点的变量收敛到同一个、并使所有目标函数之和最小的值.我们可以通过推广求平均的无中心方法去实现这个目标，但是得到算法比普通（有中心的）优化算法收敛得更慢，有阶数差距.近年来，一些新的无中心算法弥补了这个阶数差距.本文采用算子分裂的统一框架，以比这些算法原文更为简单的形式介绍这些方法.

OPERATOR SPLITTING METHODS FOR DECENTRALIZED OPTIMIZATION

Many problems in multi-agent systems, due to communication restrictions, need to be solved in a decentralized manner. There is no data fusion center, so we must rely on shortdistance communication between adjacent nodes to achieve the goal of the whole network. Compared with traditional (centralized) computing, decentralized computing is more suitable for distributed data, less subject to communication and computing bottlenecks, and easier to realize in some applications. This article overviews the formulations and methods of decentralized consensus optimization. The objective of consensus optimization is that all the variables of the nodes converge to the same vector that minimizes the sum of their objective functions. This problem is solved by calculations at each node and data exchanges between adjacent nodes. Naive decentralized algorithms are much slower than their centralized counterparts. In order to make up for this gap, we review some recent methods through a unified framework of operator splitting.