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• W2508584906 abstract "This paper considers distributed average consensus using bounded quantizers with potentially unbounded input data. We develop a quantized consensus algorithm based on a distributed alternating direction methods of multipliers (ADMM) algorithm. It is shown that, within finite iterations, all the agent variables either converge to the same quantization point or cycle with a finite period. In the convergent case, we derive a consensus error bound which also applies to that of the unbounded rounding quantizer provided that the desired average lies within quantizer output range. Simulations show that the proposed algorithm almost always converge when the network becomes large and dense." @default.
• W2508584906 created "2016-09-16" @default.
• W2508584906 creator A5001095997 @default.
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• W2508584906 date "2016-07-01" @default.
• W2508584906 modified "2023-09-22" @default.
• W2508584906 title "Distributed average consensus with bounded quantization" @default.
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• W2508584906 doi "https://doi.org/10.1109/spawc.2016.7536852" @default.
• W2508584906 hasPublicationYear "2016" @default.
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