NDLI: Augmented distributed gradient methods for multi-agent optimization under uncoordinated constant stepsizes

Content Provider	IEEE Xplore Digital Library
Author	Xu, Jinming Zhu, Shanying Soh, Yeng Chai Xie, Lihua
Copyright Year	2015
Description	Author affiliation: School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798 (Xu, Jinming; Zhu, Shanying; Soh, Yeng Chai; Xie, Lihua)
Abstract	We consider distributed optimization problems in which a number of agents are to seek the optimum of a global objective function through merely local information sharing. The problem arises in various application domains, such as resource allocation, sensor fusion and distributed learning. In particular, we are interested in scenarios where agents use uncoordinated (different) constant stepsizes for local optimization. According to most existing works, using this kind of stepsize rule for update, which is necessary in asynchronous scenarios, will lead to some gap (error) between the estimated result and the exact optimum. To deal with this issue, we develop a new augmented distributed gradient method (termed Aug-DGM) based on consensus theory. The proposed algorithm not only allows for using uncoordinated stepsizes but also, most importantly, be able to seek the exact optimum even with constant stepsizes assuming that the global objective function has Lipschitz gradient. A simple numerical example is provided to illustrate the effectiveness of the algorithm.
Starting Page	2055
Ending Page	2060
File Size	282445
Page Count	6
File Format	PDF
e-ISBN	9781479978861
DOI	10.1109/CDC.2015.7402509
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-15
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Linear programming Distributed algorithms Convergence Gradient methods Heuristic algorithms Algorithm design and analysis
Content Type	Text
Resource Type	Article

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