.png){kind=icon}
返回
Distributed consensus-based multi-agent convex optimization via gradient tracking technique
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jfranklin.2019.01.050.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper considers solving a class of optimization problems over a network of agents, in which the cost function is expressed as the sum of individual objectives of the agents. The underlying communication graph is assumed to be undirected and connected. A distributed algorithm in which agents employ time-varying and heterogeneous step-sizes is proposed by combining consensus of multi-agent systems with gradient tracking technique. The algorithm not only drives the agents' iterates to a global and consensual minimizer but also finds the optimal value of the cost function. When the individual objectives are convex and smooth, we prove that the algorithm converges at a rate of O(1/root t) if the homogeneous step-size does not exceed some upper bound, and it accelerates to O(1/t) if the homogeneous step-size is sufficiently small. When at least one of the individual objectives is strongly convex and all are smooth, we prove that the algorithm converges at a linear rate of O(lambda(t)) with 0 < lambda < 1 even though the step-sizes are time-varying and heterogeneous. Two numerical examples are provided to demonstrate the efficiency of the proposed algorithm and to validate the theoretical findings. (C) 2019 Published by Elsevier Ltd on behalf of The Franklin Institute.
Keyword:
SUBGRADIENT METHODS
AVERAGE CONSENSUS
CONVERGENCE
ALGORITHMS
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
J
IF:
3.7
论文数:
6.5K
被引数:
1.5W
机构
引用论文
Application of a cholinesterase biosensor to screen for organophosphorus pesticides extracted from soil.png)
Talanta
TalantaIF0
PREAI
分享收藏Impaired B Cell Development and Proliferation in Absence of Phosphoinositide 3-Kinase p85αScience
ScienceIF0
PREAI
分享收藏分享收藏
查看更多内容