Document Type
Conference Proceeding
Publication Version
Submitted Manuscript
Link to Published Version
http://dx.doi.org/10.1109/ICASSP.2016.7472455
Publication Date
1-1-2016
DOI
10.1109/ICASSP.2016.7472455
Conference Title
2016 IEEE International Conference on Acoustics, Speech and Signal Processing
Conference Date
March 20-25, 2016
Geolocation
(31.230416, 121.473701)
Abstract
Abstract: This paper considers multi-agent distributed optimization with quantized communication which is needed when inter-agent communications are subject to finite capacity and other practical constraints. To minimize the global objective formed by a sum of local convex functions, we develop a quantized distributed algorithm based on the alternating direction method of multipliers (ADMM). Under certain convexity assumptions, it is shown that the proposed algorithm converges to a consensus within log1+η Ω iterations, where η > 0 depends on the network topology and the local objectives, and O is a polynomial fraction depending on the quantization resolution, the distance between initial and optimal variable values, the local objectives, and the network topology. We also obtain a tight upper bound on the consensus error which does not depend on the size of the network.
Rights
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Citation Information
Shengyu Zhu, Mingyi Hong and Biao Chen. "Quantized consensus ADMM for multi-agent distributed optimization" Shanghai, China
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (2016)
Available at: http://works.bepress.com/mingyi_hong/30/
This is a manuscript of a proceeding from the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (2016), doi:10.1109/ICASSP.2016.7472455. Posted with permission.