A Decentralized Primal-Dual Method for Constrained Minimization of a Strongly Convex Function

Erfan Yazdandoost Hamedani, Necdet Serhat Aybat

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

We propose decentralized primal-dual methods for cooperative multiagent consensus optimization problems over both static and time-varying communication networks, where only local communications are allowed. The objective is to minimize the sum of agent-specific convex functions over conic constraint sets defined by agent-specific nonlinear functions; hence, the optimal consensus decision should lie in the intersection of these private sets. Under the strong convexity assumption, we provide convergence rates for suboptimality, infeasibility, and consensus violation in terms of the number of communications required; examine the effect of underlying network topology on the convergence rates.

Original languageEnglish (US)
Pages (from-to)5682-5697
Number of pages16
JournalIEEE Transactions on Automatic Control
Volume67
Issue number11
DOIs
StatePublished - Nov 1 2022

Keywords

  • Convergence analysis
  • constrained optimization
  • distributed algorithms
  • saddle point problem

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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