On stochastic subgradient mirror-descent algorithm with weighted averaging

Angelia Nedić, Soomin Lee

Research output: Contribution to journalArticle

34 Scopus citations

Abstract

This paper considers stochastic subgradient mirror-descent method for solving constrained convex minimization problems. In particular, a stochastic subgradient mirror-descent method with weighted iterate-averaging is investigated and its per-iterate convergence rate is analyzed. The novel part of the approach is in the choice of weights that are used to construct the averages. Through the use of these weighted averages, we show that the known optimal rates can be obtained with simpler algorithms than those currently existing in the literature. Specifically, by suitably choosing the stepsize values, one can obtain the rate of the order 1/√k for strongly convex functions, and the rate 1/√1+k for general convex functions (not necessarily differentiable). Furthermore, for the latter case, it is shown that a stochastic subgradient mirror-descent with iterate averaging converges (along a subsequence) to an optimal solution, almost surely, even with the stepsize of the form 1/√1+k, which was not previously known. The stepsize choices that achieve the best rates are those proposed by Tseng for acceleration of proximal gradient methods [P. Tseng, SIAM J. Optim., submitted].

Original languageEnglish (US)
Pages (from-to)84-107
Number of pages24
JournalSIAM Journal on Optimization
Volume24
Issue number1
DOIs
StatePublished - 2014
Externally publishedYes

Keywords

  • Convex optimization
  • Mirror-descent algorithm
  • Stochastic subgradient method
  • Weighted averaging

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science

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