Multi-agent constrained optimization of a strongly convex function over time-varying directed networks

Erfan Yazdandoost Hamedani; Necdet Serhat Aybat

doi:10.1109/ALLERTON.2017.8262781

Multi-agent constrained optimization of a strongly convex function over time-varying directed networks

Erfan Yazdandoost Hamedani, Necdet Serhat Aybat

Marcus Department of Industrial and Manufacturing Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

Abstract

We consider cooperative multi-agent consensus optimization problems over undirected and directed time-varying communication networks, where only local communications are allowed. The objective is to minimize the sum of agent-specific possibly non-smooth composite convex functions over agent-specific private conic constraint sets; hence, the optimal consensus decision should lie in the intersection of these private sets. Assuming the sum function is strongly convex, we provide convergence rates in sub-optimality, infeasibility and consensus violation; examine the effect of underlying network topology on the convergence rates of the proposed decentralized algorithm.

Original language	English (US)
Title of host publication	55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	518-525
Number of pages	8
ISBN (Electronic)	9781538632666
DOIs	https://doi.org/10.1109/ALLERTON.2017.8262781
State	Published - Jul 1 2017
Event	55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017 - Monticello, United States Duration: Oct 3 2017 → Oct 6 2017

Publication series

Name	55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017
Volume	2018-January

Other

Other	55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017
Country/Territory	United States
City	Monticello
Period	10/3/17 → 10/6/17

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Hardware and Architecture
Signal Processing
Energy Engineering and Power Technology
Control and Optimization

Access to Document

10.1109/ALLERTON.2017.8262781

Cite this

Hamedani, E. Y., & Aybat, N. S. (2017). Multi-agent constrained optimization of a strongly convex function over time-varying directed networks. In 55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017 (pp. 518-525). (55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ALLERTON.2017.8262781

Hamedani, Erfan Yazdandoost ; Aybat, Necdet Serhat. / Multi-agent constrained optimization of a strongly convex function over time-varying directed networks. 55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 518-525 (55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017).

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title = "Multi-agent constrained optimization of a strongly convex function over time-varying directed networks",

abstract = "We consider cooperative multi-agent consensus optimization problems over undirected and directed time-varying communication networks, where only local communications are allowed. The objective is to minimize the sum of agent-specific possibly non-smooth composite convex functions over agent-specific private conic constraint sets; hence, the optimal consensus decision should lie in the intersection of these private sets. Assuming the sum function is strongly convex, we provide convergence rates in sub-optimality, infeasibility and consensus violation; examine the effect of underlying network topology on the convergence rates of the proposed decentralized algorithm.",

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Hamedani, EY & Aybat, NS 2017, Multi-agent constrained optimization of a strongly convex function over time-varying directed networks. in 55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017. 55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 518-525, 55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017, Monticello, United States, 10/3/17. https://doi.org/10.1109/ALLERTON.2017.8262781

Multi-agent constrained optimization of a strongly convex function over time-varying directed networks. / Hamedani, Erfan Yazdandoost; Aybat, Necdet Serhat.
55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 518-525 (55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - We consider cooperative multi-agent consensus optimization problems over undirected and directed time-varying communication networks, where only local communications are allowed. The objective is to minimize the sum of agent-specific possibly non-smooth composite convex functions over agent-specific private conic constraint sets; hence, the optimal consensus decision should lie in the intersection of these private sets. Assuming the sum function is strongly convex, we provide convergence rates in sub-optimality, infeasibility and consensus violation; examine the effect of underlying network topology on the convergence rates of the proposed decentralized algorithm.

AB - We consider cooperative multi-agent consensus optimization problems over undirected and directed time-varying communication networks, where only local communications are allowed. The objective is to minimize the sum of agent-specific possibly non-smooth composite convex functions over agent-specific private conic constraint sets; hence, the optimal consensus decision should lie in the intersection of these private sets. Assuming the sum function is strongly convex, we provide convergence rates in sub-optimality, infeasibility and consensus violation; examine the effect of underlying network topology on the convergence rates of the proposed decentralized algorithm.

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Hamedani EY, Aybat NS. Multi-agent constrained optimization of a strongly convex function over time-varying directed networks. In 55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 518-525. (55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017). doi: 10.1109/ALLERTON.2017.8262781

Multi-agent constrained optimization of a strongly convex function over time-varying directed networks

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