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2016 IEEE International Conference on Acoustics, Speech and Signal Processing
March 20-25, 2016
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.
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Shengyu Zhu, Mingyi Hong and Biao Chen. "Quantized consensus ADMM for multi-agent distributed optimization" Shanghai, China2016 IEEE International Conference on Acoustics, Speech and Signal Processing
Available at: http://works.bepress.com/mingyi_hong/30/