Distributed Global Optimization by Annealing

Brian Swenson, Soummya Kart, H. Vincent Poor, Jose M.F. Moura

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

Abstract

The paper considers distributed global minimization of a nonconvex function. We study a first-order consensus + innovations type algorithm that incorporates decaying additive Gaussian noise for annealing to converge to the set of global minima under certain technical assumptions. The paper presents simple methods for verifying that the required technical assumptions hold and illustrates it with a distributed target-localization application.

Original languageEnglish (US)
Title of host publication2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages181-185
Number of pages5
ISBN (Electronic)9781728155494
DOIs
StatePublished - Dec 2019
Event8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Le Gosier, Guadeloupe
Duration: Dec 15 2019Dec 18 2019

Publication series

Name2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings

Conference

Conference8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019
CountryGuadeloupe
CityLe Gosier
Period12/15/1912/18/19

All Science Journal Classification (ASJC) codes

  • Control and Optimization
  • Artificial Intelligence
  • Computer Networks and Communications

Keywords

  • consensus + innovations
  • Distributed optimization
  • multiagent systems
  • nonconvex optimization

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