Decentralized Resource Allocation via Dual Consensus ADMM

被引:22
|
作者
Banjac, Goran [1 ]
Rey, Felix [1 ]
Goulart, Paul [2 ]
Lygeros, John [1 ]
机构
[1] Swiss Fed Inst Technol, Automat Control Lab, Phys Str 3, CH-8092 Zurich, Switzerland
[2] Univ Oxford, Dept Engn Sci, Oxford OX1 3PJ, England
来源
2019 AMERICAN CONTROL CONFERENCE (ACC) | 2019年
关键词
ALGORITHM;
D O I
10.23919/acc.2019.8814988
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We consider a resource allocation problem over an undirected network of agents, where edges of the network define communication links. The goal is to minimize the sum of agent-specific convex objective functions, while the agents' decisions are coupled via a convex conic constraint. We derive two methods by applying the alternating direction method of multipliers (ADMM) for decentralized consensus optimization to the dual of our resource allocation problem. Both methods are fully parallelizable and decentralized in the sense that each agent exchanges information only with its neighbors in the network and requires only its own data for updating its decision. We prove convergence of the proposed methods and demonstrate their effectiveness with a numerical example.
引用
收藏
页码:2789 / 2794
页数:6
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