Improved Convergence Rates for Distributed Resource Allocation

Angelia Nedich, Alex Olshevsky, Wei Shi

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

1 Citation (Scopus)

Abstract

In this paper, we develop a class of decentralized algorithms for solving a convex resource allocation optimization problem over a connected network. By observing a connection between the resource allocation and the consensus optimization, we propose a novel class of algorithms for solving the resource allocation problem with improved convergence guarantees. Specifically, we introduce an algorithm for solving the resource allocation problem with an o(1/k) convergence rate when the agents' objective functions are generally convex and per agent local constraints are allowed; we then introduce a gradient-based algorithm for the case when per agent local constraints are absent and show that such scheme achieves geometric convergence with an improved scalability. We also provide a projection-gradient-based algorithm which can handle smooth objective and simple constraints more efficiently.

Original languageEnglish (US)
Title of host publication2018 IEEE Conference on Decision and Control, CDC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages172-177
Number of pages6
ISBN (Electronic)9781538613955
DOIs
StatePublished - Jan 18 2019
Event57th IEEE Conference on Decision and Control, CDC 2018 - Miami, United States
Duration: Dec 17 2018Dec 19 2018

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2018-December
ISSN (Print)0743-1546

Conference

Conference57th IEEE Conference on Decision and Control, CDC 2018
CountryUnited States
CityMiami
Period12/17/1812/19/18

Fingerprint

Resource Allocation
Resource allocation
Convergence Rate
Geometric Convergence
Gradient Projection
Decentralized
Scalability
Objective function
Gradient
Optimization Problem
Optimization
Class

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

Cite this

Nedich, A., Olshevsky, A., & Shi, W. (2019). Improved Convergence Rates for Distributed Resource Allocation. In 2018 IEEE Conference on Decision and Control, CDC 2018 (pp. 172-177). [8619322] (Proceedings of the IEEE Conference on Decision and Control; Vol. 2018-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2018.8619322

Improved Convergence Rates for Distributed Resource Allocation. / Nedich, Angelia; Olshevsky, Alex; Shi, Wei.

2018 IEEE Conference on Decision and Control, CDC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 172-177 8619322 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2018-December).

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

Nedich, A, Olshevsky, A & Shi, W 2019, Improved Convergence Rates for Distributed Resource Allocation. in 2018 IEEE Conference on Decision and Control, CDC 2018., 8619322, Proceedings of the IEEE Conference on Decision and Control, vol. 2018-December, Institute of Electrical and Electronics Engineers Inc., pp. 172-177, 57th IEEE Conference on Decision and Control, CDC 2018, Miami, United States, 12/17/18. https://doi.org/10.1109/CDC.2018.8619322
Nedich A, Olshevsky A, Shi W. Improved Convergence Rates for Distributed Resource Allocation. In 2018 IEEE Conference on Decision and Control, CDC 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 172-177. 8619322. (Proceedings of the IEEE Conference on Decision and Control). https://doi.org/10.1109/CDC.2018.8619322
Nedich, Angelia ; Olshevsky, Alex ; Shi, Wei. / Improved Convergence Rates for Distributed Resource Allocation. 2018 IEEE Conference on Decision and Control, CDC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 172-177 (Proceedings of the IEEE Conference on Decision and Control).
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