Distributed Optimisation under a Weight-unbalanced Digraph

P. Pradhan, Arijit Sen, Mangal Kothari, K. Rajawat
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Abstract

Distributed optimisation (DO) problems in application to networked multi-agent system (MAS) have been an intriguing topic for research among different research communities. Despite the substantial number of works in this field over the past decade, the existing literature lacks a generalised algorithm for solving the DO problem for a strongly-connected weight unbalanced graph. This paper presents an information state dynamics for optimising the convex global objective function in a distributed manner under a strongly-connected weight-unbalanced digraph. Unlike few of the existing algorithms, the proposed dynamics does not require any global information, rather it solely depends on the locally available information and the information that the agent receives from the neighbours only. Numerical simulations with a comparative study with the state-of-the-art algorithm solving a DO problem under a weight-unbalanced digraph are also demonstrated in the paper to prove the claims.
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权重不平衡有向图下的分布式优化
分布式优化(DO)问题在网络多智能体系统(MAS)中的应用一直是学术界研究的热点。尽管在过去十年中在该领域有大量的工作,但现有文献缺乏解决强连接权不平衡图的DO问题的通用算法。本文提出了一种在强连接权不平衡有向图下以分布式方式优化凸全局目标函数的信息状态动力学方法。与现有的一些算法不同,该算法不需要任何全局信息,而是完全依赖于局部可用信息和代理仅从邻居处接收的信息。通过数值模拟,并与最先进的算法在加权不平衡有向图下求解DO问题进行了比较研究,以证明本文的观点。
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