Distributed Stochastic Constrained Composite Optimization Over Time-Varying Network With a Class of Communication Noise

Zhan Yu*, Daniel W. C. Ho, Deming Yuan, Jie Liu

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

5 Citations (Scopus)

Abstract

This article is concerned with the distributed stochastic multiagent-constrained optimization problem over a time-varying network with a class of communication noise. This article considers the problem in composite optimization setting, which is more general in the literature of noisy network optimization. It is noteworthy that the mainstream existing methods for noisy network optimization are Euclidean projection based. Based on the Bregman projection-based mirror descent scheme, we present a non-Euclidean method and investigate their convergence behavior. This method is the distributed stochastic composite mirror descent type method (DSCMD-N), which provides a more general algorithm framework. Some new error bounds for DSCMD-N are obtained. To the best of our knowledge, this is the first work to analyze and derive convergence rates of optimization algorithm in noisy network optimization. We also show that an optimal rate of O(1/T) in nonsmooth convex optimization can be obtained for the proposed method under appropriate communication noise condition. Moveover, novel convergence results are comprehensively derived in expectation convergence, high probability convergence, and almost surely sense.

Original languageEnglish
Pages (from-to)3561-3573
Number of pages13
JournalIEEE Transactions on Cybernetics
Volume53
Issue number6
Early online date24 Nov 2021
DOIs
Publication statusPublished - Jun 2023

Scopus Subject Areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

User-Defined Keywords

  • Communication noise
  • composite optimization
  • distributed optimization
  • mirror descent
  • multiagent network

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