Quantized consensus ADMM for multi-agent distributed optimization

Shengyu Zhu, Mingyi Hong, Biao Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

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 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 languageEnglish (US)
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4134-4138
Number of pages5
Volume2016-May
ISBN (Electronic)9781479999880
DOIs
StatePublished - May 18 2016
Event41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China
Duration: Mar 20 2016Mar 25 2016

Other

Other41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
CountryChina
CityShanghai
Period3/20/163/25/16

Fingerprint

Topology
Communication
Parallel algorithms
Polynomials

Keywords

  • alternating direction method of multipliers (ADMM)
  • linear convergence
  • Multi-agent distributed optimization
  • quantization

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

Cite this

Zhu, S., Hong, M., & Chen, B. (2016). Quantized consensus ADMM for multi-agent distributed optimization. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings (Vol. 2016-May, pp. 4134-4138). [7472455] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2016.7472455

Quantized consensus ADMM for multi-agent distributed optimization. / Zhu, Shengyu; Hong, Mingyi; Chen, Biao.

2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings. Vol. 2016-May Institute of Electrical and Electronics Engineers Inc., 2016. p. 4134-4138 7472455.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zhu, S, Hong, M & Chen, B 2016, Quantized consensus ADMM for multi-agent distributed optimization. in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings. vol. 2016-May, 7472455, Institute of Electrical and Electronics Engineers Inc., pp. 4134-4138, 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016, Shanghai, China, 3/20/16. https://doi.org/10.1109/ICASSP.2016.7472455
Zhu S, Hong M, Chen B. Quantized consensus ADMM for multi-agent distributed optimization. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings. Vol. 2016-May. Institute of Electrical and Electronics Engineers Inc. 2016. p. 4134-4138. 7472455 https://doi.org/10.1109/ICASSP.2016.7472455
Zhu, Shengyu ; Hong, Mingyi ; Chen, Biao. / Quantized consensus ADMM for multi-agent distributed optimization. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings. Vol. 2016-May Institute of Electrical and Electronics Engineers Inc., 2016. pp. 4134-4138
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