20 Scopus citations

Abstract

Decentralized optimization algorithms have received much attention due to the recent advances in network information processing. However, conventional decentralized algorithms based on projected gradient descent are incapable of handling high-dimensional constrained problems, as the projection step becomes computationally prohibitive. To address this problem, this paper adopts a projection-free optimization approach, a.k.a. the Frank-Wolfe (FW) or conditional gradient algorithm. We first develop a decentralized FW (DeFW) algorithm from the classical FW algorithm. The convergence of the proposed algorithm is studied by viewing the decentralized algorithm as an inexact FW algorithm. Using a diminishing step size rule and letting t be the iteration number, we show that the DeFW algorithm's convergence rate is O(1/t) for convex objectives; is O(1/t2) for strongly convex objectives with the optimal solution in the interior of the constraint set; and is O(1√t) toward a stationary point for smooth but nonconvex objectives. We then show that a consensus-based DeFW algorithm meets the above guarantees with two communication rounds per iteration. We demonstrate the advantages of the proposed DeFW algorithm on low-complexity robust matrix completion and communication efficient sparse learning. Numerical results on synthetic and real data are presented to support our findings.

Original languageEnglish (US)
Article number7883821
Pages (from-to)5522-5537
Number of pages16
JournalIEEE Transactions on Automatic Control
Volume62
Issue number11
DOIs
StatePublished - Nov 1 2017

Keywords

  • Communication efficient algorithms
  • consensus algorithms
  • decentralized optimization
  • Frank-Wolfe (FW) algorithm
  • high-dimensional optimization
  • least absolute shrinkage and selection operator (LASSO)
  • matrix completion

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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