Mathematical Programming

, Volume 174, Issue 1–2, pp 433–451 | Cite as

Stochastic quasi-Fejér block-coordinate fixed point iterations with random sweeping II: mean-square and linear convergence

  • Patrick L. CombettesEmail author
  • Jean-Christophe Pesquet
Full Length Paper Series B


Combettes and Pesquet (SIAM J Optim 25:1221–1248, 2015) investigated the almost sure weak convergence of block-coordinate fixed point algorithms and discussed their applications to nonlinear analysis and optimization. This algorithmic framework features random sweeping rules to select arbitrarily the blocks of variables that are activated over the course of the iterations and it allows for stochastic errors in the evaluation of the operators. The present paper establishes results on the mean-square and linear convergence of the iterates. Applications to monotone operator splitting and proximal optimization algorithms are presented.


Block-coordinate algorithm Fixed-point algorithm Mean-square convergence Monotone operator splitting Linear convergence Stochastic algorithm 

Mathematics Subject Classification

47J25 46M10 65K10 90C15 


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature and Mathematical Optimization Society 2018

Authors and Affiliations

  • Patrick L. Combettes
    • 1
    Email author
  • Jean-Christophe Pesquet
    • 2
  1. 1.Department of MathematicsNorth Carolina State UniversityRaleighUSA
  2. 2.CentraleSupélec, Université Paris-Saclay, Center for Visual ComputingChâtenay-MalabryFrance

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