@InProceedings{8552941,
  author = {P. L. Combettes and J. Pesquet},
  booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},
  title = {Linear Convergence of Stochastic Block-Coordinate Fixed Point Algorithms},
  year = {2018},
  pages = {742-746},
  abstract = {Recent random block-coordinate fixed point algorithms are particularly well suited to large-scale optimization in signal and image processing. These algorithms feature random sweeping rules to select arbitrarily the blocks of variables that are activated over the course of the iterations and they allow for stochastic errors in the evaluation of the operators. The present paper provides new linear convergence results. These convergence rates are compared to those of standard deterministic algorithms both theoretically and experimentally in an image recovery problem.},
  keywords = {convergence of numerical methods;deterministic algorithms;image denoising;iterative methods;optimisation;stochastic processes;standard deterministic algorithms;stochastic block-coordinate fixed point algorithms;image processing;random sweeping rules;linear convergence;random block-coordinate fixed point algorithms;iterations;signal processing;image recovery problem;Convergence;Signal processing algorithms;Random variables;Signal processing;Standards;Europe;Clustering algorithms;Block-coordinate algorithm;fixed-point algorithm;linear convergence;stochastic algorithm},
  doi = {10.23919/EUSIPCO.2018.8552941},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570436777.pdf},
}

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