Distributed random projection algorithm for convex optimization

Soomin Lee, Angelia Nedić

Research output: Contribution to journalArticlepeer-review

146 Scopus citations


Random projection algorithm is of interest for constrained optimization when the constraint set is not known in advance or the projection operation on the whole constraint set is computationally prohibitive. This paper presents a distributed random projection algorithm for constrained convex optimization problems that can be used by multiple agents connected over a time-varying network, where each agent has its own objective function and its own constrained set. We prove that the iterates of all agents converge to the same point in the optimal set almost surely. Experiments on distributed support vector machines demonstrate good performance of the algorithm.

Original languageEnglish (US)
Article number6461383
Pages (from-to)221-229
Number of pages9
JournalIEEE Journal on Selected Topics in Signal Processing
Issue number2
StatePublished - Apr 2013
Externally publishedYes


  • Asynchronous algorithms
  • Distributed convex optimization
  • Distributed multi-agent system
  • Random gossip network

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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