Distributed Proximal Algorithms for Multiagent Optimization with Coupled Inequality Constraints

Xiuxian Li, Gang Feng*, Lihua Xie

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

48 Citations (Scopus)

Abstract

This article aims to address distributed optimization problems over directed and time-varying networks, where the global objective function consists of a sum of locally accessible convex objective functions subject to a feasible set constraint and coupled inequality constraints whose information is only partially accessible to each agent. For this problem, a distributed proximal-based algorithm, called distributed proximal primal-dual algorithm, is proposed based on the celebrated centralized proximal point algorithm. It is shown that the proposed algorithm can lead to the global optimal solution with a general step size, which is diminishing and nonsummable, but not necessarily square summable, and the saddle-point running evaluation error vanishes proportionally to O(1/√k), where > 0 is the iteration number. Finally, a simulation example is presented to corroborate the effectiveness of the proposed algorithm.
Original languageEnglish
Article number9075384
Pages (from-to)1223-1230
JournalIEEE Transactions on Automatic Control
Volume66
Issue number3
Online published21 Apr 2020
DOIs
Publication statusPublished - Mar 2021

Research Keywords

  • Coupled inequality constraints
  • distributed optimization
  • multiagent networks
  • proximal point algorithm (PPA)

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