Quantized consensus ADMM for multi agent distributed optimization
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+e O iterations, where e 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.
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)