Paper Info
Title
Cloud-Based Optimization: A Quasi-Decentralized Approach To Multi-Agent Coordination
Abstract
New architectures and algorithms are needed to reflect the mixture of local and global information that is available as multi-agent systems connect over the cloud. We present a novel architecture for multi-agent coordination where the cloud is assumed to be able to gather information from all agents, perform centralized computations, and disseminate the results in an intermittent manner. This architecture is used to solve a multi-agent optimization problem in which each agent has a local objective function unknown to the other agents and in which the agents are collectively subject to global inequality constraints. Leveraging the cloud, a dual problem is formulated and solved by finding a saddle point of the associated Lagrangian.
Year
DOI
Venue
2014
10.1109/CDC.2014.7040430
2014 IEEE 53RD ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC)
Field
DocType
ISSN
Mathematical optimization,Architecture,Saddle point,Computer science,Global information,Dissemination,Duality (optimization),Optimization problem,Cloud computing,Distributed computing,Computation
Conference
0743-1546
Citations 
PageRank 
References 
5
0.49
13
Authors
2
Name
Order
Citations
PageRank
Hale, M.T.1176.84
Magnus Egerstedt22862384.94