大规模网络中的分布式最小二乘迭代方法综述

Shi Lei, Zhao Liang, Song Wenzhan, Goutham Kamath, WU Yuan, Liu Xuefeng
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引用次数: 3

摘要

石磊,赵亮,宋文展,Goutham Kamath,吴媛,刘雪峰(1)。美国佐治亚州立大学,亚特兰大30302;2. 乔治亚格温内特学院,美国佐治亚州劳伦斯维尔30043;3.美国佐治亚大学,佐治亚州雅典30602;4. 浙江工业大学,浙江杭州310023;5. 许多科学和工程应用涉及求解由某些场测量形成的线性最小二乘系统。在分布式网络物理系统(CPS)中,用于测量的每个传感器节点通常只知道最小二乘系统的部分独立行。为了解决最小二乘问题,必须将所有测量值聚集在一个集中的位置,然后进行计算。由于带宽和时间的限制,这种数据收集和计算效率低下,有时由于数据隐私问题而不可行。迭代法是解决上述问题的天然候选方法,目前已有许多相关研究。然而,大多数提出的解决方案都与集中/平行计算有关,而只有少数具有在分布式网络中应用的潜力。因此,在现实世界的许多应用中,分布式计算是强烈首选或需要的,例如智能电网,目标跟踪等。本文综述了网络中具有代表性的分布最小二乘迭代方法。
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Distributed Least-Squares Iterative Methods in Large-Scale Networks: A Survey
SHI Lei, ZHAO Liang, SONG Wenzhan, Goutham Kamath, WU Yuan, and LIU Xuefeng (1. Georgia State University, Atlanta, GA 30302, USA; 2. Georgia Gwinnett College, Lawrenceville, GA 30043, USA; 3. University of Georgia, Athens, GA 30602, USA; 4. Zhejiang University of Technology, Hangzhou 310023, China; 5. The Hong Kong Polytechnic University, Hong Kong, China) Many science and engineering applications involve solving a linear least ⁃ squares system formed from some field mea⁃ surements. In the distributed cyber ⁃ physical systems (CPS), each sensor node used for measurement often only knows partial independent rows of the least ⁃ squares system. To solve the least ⁃ squares all the measurements must be gath⁃ ered at a centralized location and then perform the computa⁃ tion. Such data collection and computation are inefficient be⁃ cause of bandwidth and time constraints and sometimes are infeasible because of data privacy concerns. Iterative meth⁃ ods are natural candidates for solving the aforementioned problem and there are many studies regarding this. However, most of the proposed solutions are related to centralized/par⁃ allel computations while only a few have the potential to be applied in distributed networks. Thus distributed computa⁃ tions are strongly preferred or demanded in many of the real world applications, e.g. smart⁃grid, target tracking, etc. This paper surveys the representative iterative methods for distrib⁃ uted least⁃squares in networks.
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