Annealing for Distributed Global Optimization

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

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

4 Scopus citations

Abstract

The paper proves convergence to global optima for a class of distributed algorithms for nonconvex optimization in network-based multi-agent settings. Agents are permitted to communicate over a time-varying undirected graph. Each agent is assumed to possess a local objective function (assumed to be smooth, but possibly nonconvex). The paper considers algorithms for optimizing the sum function. A distributed algorithm of the consensus + innovations type is proposed which relies on first-order information at the agent level. Under appropriate conditions on network connectivity and the cost objective, convergence to the set of global optima is achieved by an annealing-type approach, with decaying Gaussian noise independently added into each agent's update step. It is shown that the proposed algorithm converges in probability to the set of global minima of the sum function.

Original languageEnglish (US)
Title of host publication2019 IEEE 58th Conference on Decision and Control, CDC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3018-3025
Number of pages8
ISBN (Electronic)9781728113982
DOIs
StatePublished - Dec 2019
Event58th IEEE Conference on Decision and Control, CDC 2019 - Nice, France
Duration: Dec 11 2019Dec 13 2019

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2019-December
ISSN (Print)0743-1546

Conference

Conference58th IEEE Conference on Decision and Control, CDC 2019
CountryFrance
CityNice
Period12/11/1912/13/19

All Science Journal Classification (ASJC) codes

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

Keywords

  • Distributed optimization
  • multiagent systems
  • nonconvex optimization

Fingerprint Dive into the research topics of 'Annealing for Distributed Global Optimization'. Together they form a unique fingerprint.

  • Cite this

    Swenson, B., Kar, S., Poor, H. V., & Moura, J. M. F. (2019). Annealing for Distributed Global Optimization. In 2019 IEEE 58th Conference on Decision and Control, CDC 2019 (pp. 3018-3025). [9029708] (Proceedings of the IEEE Conference on Decision and Control; Vol. 2019-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC40024.2019.9029708