A new class of distributed optimization algorithms: Application to regression of distributed data

S. Sundhar Ram, A. Nedić, V. V. Veeravalli

Research output: Contribution to journalArticle

66 Scopus citations

Abstract

In a distributed optimization problem, the complete problem information is not available at a single location but is rather distributed among different agents in a multi-agent system. In the problems studied in the literature, each agent has an objective function and the network goal is to minimize the sum of the agents objective functions over a constraint set that is globally known. In this paper, we study a generalization of the above distributed optimization problem. In particular, the network objective is to minimize a function of the sum of the individual objective functions over the constraint set. The outer function and the constraint set are known to all the agents. We discuss an algorithm and prove its convergence, and then discuss extensions to more general and complex distributed optimization problems. We provide a motivation for our algorithms through the example of distributed regression of distributed data.

Original languageEnglish (US)
Pages (from-to)71-88
Number of pages18
JournalOptimization Methods and Software
Volume27
Issue number1
DOIs
StatePublished - Feb 1 2012
Externally publishedYes

Keywords

  • convex optimization
  • distributed optimization
  • distributed regression

ASJC Scopus subject areas

  • Software
  • Control and Optimization
  • Applied Mathematics

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