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 q > 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.
|Original language||English (US)|
|Title of host publication||2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - May 18 2016|
|Event||41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China|
Duration: Mar 20 2016 → Mar 25 2016
|Name||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Other||41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016|
|Period||3/20/16 → 3/25/16|
Bibliographical notePublisher Copyright:
© 2016 IEEE.
- Multi-agent distributed optimization
- alternating direction method of multipliers (ADMM)
- linear convergence