Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization

Angelia Nedich, Alex Olshevsky, Michael G. Rabbat

Research output: Contribution to journalReview articlepeer-review

260 Scopus citations


In decentralized optimization, nodes cooperate to minimize an overall objective function that is the sum (or average) of per-node private objective functions. Algorithms interleave local computations with communication among all or a subset of the nodes. Motivated by a variety of applications..decentralized estimation in sensor networks, fitting models to massive data sets, and decentralized control of multirobot systems, to name a few..significant advances have been made toward the development of robust, practical algorithms with theoretical performance guarantees. This paper presents an overview of recent work in this area. In general, rates of convergence depend not only on the number of nodes involved and the desired level of accuracy, but also on the structure and nature of the network over which nodes communicate (e.g., whether links are directed or undirected, static or time varying). We survey the state-of-theart algorithms and their analyses tailored to these different scenarios, highlighting the role of the network topology.

Original languageEnglish (US)
Pages (from-to)953-976
Number of pages24
JournalProceedings of the IEEE
Issue number5
StatePublished - May 2018


  • Consensus algorithms
  • distributed averaging
  • distributed optimization
  • gossip algorithms
  • multiagent systems

ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering


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