Quantisation Effects in Distributed Optimisation

Joseph A.G. Jonkman; Thomas Sherson; Richard Heusdens

doi:10.1109/ICASSP.2018.8461782

Quantisation Effects in Distributed Optimisation

Joseph A.G. Jonkman, Thomas Sherson, Richard Heusdens

Signal Processing Systems

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

6 Citations (Scopus)

Abstract

In this paper the effects of quantisation on distributed convex optimisation algorithms are explored via the lens of monotone operator theory. Specifically, by representing transmission quantisation via an additive noise model, we demonstrate how quantisation can be viewed as an instance of an inexact Krasnosel' skiľ-Mann scheme. In the case of two distributed solvers, the Alternating Direction Method of Multipliers and the Primal Dual Method of Multipliers, we further demonstrate how an adaptive quantisation scheme can be constructed to reduce transmission costs between nodes. Finally for the Gaussian channel capacity maximisation problem, we demonstrate convergence even in the presence of one-bit uniform quantisation based on the aforementioned adaptive quantisation scheme.

Original language	English
Title of host publication	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	3649-3653
Number of pages	5
ISBN (Electronic)	978-1-5386-4658-8
ISBN (Print)	978-1-5386-4659-5
DOIs	https://doi.org/10.1109/ICASSP.2018.8461782
Publication status	Published - 2018
Event	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018: Signal Processing and Artificial Intelligence: Changing the World - Calgary Telus Convention Center (CTCC), Calgary, Canada Duration: 15 Apr 2018 → 20 Apr 2018 https://2018.ieeeicassp.org

Conference

Conference	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Country/Territory	Canada
City	Calgary
Period	15/04/18 → 20/04/18
Internet address	https://2018.ieeeicassp.org

Keywords

ADMM
Distributed Convex Optimisation
Monotone Operator Theory
PDMM
Quantisation

Access to Document

10.1109/ICASSP.2018.8461782

Cite this

@inproceedings{1d042daabe364a8aa407c0eb3ee66cee,

title = "Quantisation Effects in Distributed Optimisation",

abstract = "In this paper the effects of quantisation on distributed convex optimisation algorithms are explored via the lens of monotone operator theory. Specifically, by representing transmission quantisation via an additive noise model, we demonstrate how quantisation can be viewed as an instance of an inexact Krasnosel' ski{\v I}-Mann scheme. In the case of two distributed solvers, the Alternating Direction Method of Multipliers and the Primal Dual Method of Multipliers, we further demonstrate how an adaptive quantisation scheme can be constructed to reduce transmission costs between nodes. Finally for the Gaussian channel capacity maximisation problem, we demonstrate convergence even in the presence of one-bit uniform quantisation based on the aforementioned adaptive quantisation scheme.",

keywords = "ADMM, Distributed Convex Optimisation, Monotone Operator Theory, PDMM, Quantisation",

author = "Jonkman, {Joseph A.G.} and Thomas Sherson and Richard Heusdens",

year = "2018",

doi = "10.1109/ICASSP.2018.8461782",

language = "English",

isbn = "978-1-5386-4659-5",

pages = "3649--3653",

booktitle = "2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings",

publisher = "IEEE",

address = "United States",

note = "2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 : Signal Processing and Artificial Intelligence: Changing the World ; Conference date: 15-04-2018 Through 20-04-2018",

url = "https://2018.ieeeicassp.org",

}

Jonkman, JAG, Sherson, T & Heusdens, R 2018, Quantisation Effects in Distributed Optimisation. in 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings., 8461782, IEEE, Piscataway, NJ, pp. 3649-3653, 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018, Calgary, Canada, 15/04/18. https://doi.org/10.1109/ICASSP.2018.8461782

Quantisation Effects in Distributed Optimisation. / Jonkman, Joseph A.G.; Sherson, Thomas; Heusdens, Richard.
2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Piscataway, NJ: IEEE, 2018. p. 3649-3653 8461782.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Quantisation Effects in Distributed Optimisation

AU - Jonkman, Joseph A.G.

AU - Sherson, Thomas

AU - Heusdens, Richard

PY - 2018

Y1 - 2018

N2 - In this paper the effects of quantisation on distributed convex optimisation algorithms are explored via the lens of monotone operator theory. Specifically, by representing transmission quantisation via an additive noise model, we demonstrate how quantisation can be viewed as an instance of an inexact Krasnosel' skiľ-Mann scheme. In the case of two distributed solvers, the Alternating Direction Method of Multipliers and the Primal Dual Method of Multipliers, we further demonstrate how an adaptive quantisation scheme can be constructed to reduce transmission costs between nodes. Finally for the Gaussian channel capacity maximisation problem, we demonstrate convergence even in the presence of one-bit uniform quantisation based on the aforementioned adaptive quantisation scheme.

AB - In this paper the effects of quantisation on distributed convex optimisation algorithms are explored via the lens of monotone operator theory. Specifically, by representing transmission quantisation via an additive noise model, we demonstrate how quantisation can be viewed as an instance of an inexact Krasnosel' skiľ-Mann scheme. In the case of two distributed solvers, the Alternating Direction Method of Multipliers and the Primal Dual Method of Multipliers, we further demonstrate how an adaptive quantisation scheme can be constructed to reduce transmission costs between nodes. Finally for the Gaussian channel capacity maximisation problem, we demonstrate convergence even in the presence of one-bit uniform quantisation based on the aforementioned adaptive quantisation scheme.

KW - ADMM

KW - Distributed Convex Optimisation

KW - Monotone Operator Theory

KW - PDMM

KW - Quantisation

UR - http://www.scopus.com/inward/record.url?scp=85054269948&partnerID=8YFLogxK

U2 - 10.1109/ICASSP.2018.8461782

DO - 10.1109/ICASSP.2018.8461782

M3 - Conference contribution

AN - SCOPUS:85054269948

SN - 978-1-5386-4659-5

SP - 3649

EP - 3653

BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings

PB - IEEE

CY - Piscataway, NJ

T2 - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018

Y2 - 15 April 2018 through 20 April 2018

ER -

Quantisation Effects in Distributed Optimisation

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this