Multi-agent constrained optimization of a strongly convex function

Erfan Yazdandoost Hamedani, Necdet Serhat Aybat

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

1 Scopus citations

Abstract

We consider cooperative multi-agent consensus optimization problems over an undirected network of agents, where only local communications are allowed. The objective is to minimize the sum of agent-specific convex functions over agent-specific private conic constraint sets. We provide convergence rates in sub-optimality, infeasibility and consensus violation when the sum function is strongly convex; examine the effect of underlying network topology on the convergence rates of the proposed decentralized algorithm.

Original languageEnglish (US)
Title of host publication2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages558-562
Number of pages5
ISBN (Electronic)9781509059904
DOIs
StatePublished - Mar 7 2018
Event5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Montreal, Canada
Duration: Nov 14 2017Nov 16 2017

Publication series

Name2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
Volume2018-January

Other

Other5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017
CountryCanada
CityMontreal
Period11/14/1711/16/17

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Signal Processing

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    Hamedani, E. Y., & Aybat, N. S. (2018). Multi-agent constrained optimization of a strongly convex function. In 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings (pp. 558-562). (2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2017.8309021