Distributed Proximal Algorithms for Multiagent Optimization with Coupled Inequality Constraints

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Article number9075384
Pages (from-to)1223-1230
Journal / PublicationIEEE Transactions on Automatic Control
Issue number3
Online published21 Apr 2020
Publication statusPublished - Mar 2021


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.

Research Area(s)

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