Control approach to distributed optimization

Jing Wang, Nicola Elia

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

159 Scopus citations

Abstract

In this paper, we propose a novel computation model for solving the distributed optimization problem where the objective function is formed by the sum of convex functions available to individual agent. Our approach differentiates from the existing approach by local convex mixing and gradient searching in that we force the states of the model to the global optimal point by controlling the subgradient of the global optimal function. In this way, the model we proposed does not suffer from the limitation of diminishing step size in gradient searching and allows fast asymptotic convergence. The model also shows robustness to additive noise, which is a main curse for algorithms based on convex mixing or consensus.

Original languageEnglish (US)
Title of host publication2010 48th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2010
Pages557-561
Number of pages5
DOIs
StatePublished - Dec 1 2010
Externally publishedYes
Event48th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2010 - Monticello, IL, United States
Duration: Sep 29 2010Oct 1 2010

Publication series

Name2010 48th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2010

Other

Other48th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2010
CountryUnited States
CityMonticello, IL
Period9/29/1010/1/10

Keywords

  • Additive noise
  • Distributed optimization
  • Laplacian
  • Small gain theorem
  • Subgradients

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