Coordinate Descent with Arbitrary Sampling I: Algorithms and Complexity

@article{Qu2016CoordinateDW,
  title={Coordinate Descent with Arbitrary Sampling I: Algorithms and Complexity},
  author={Zheng Qu and Peter Richt{\'a}rik},
  journal={Optimization Methods and Software},
  year={2016},
  volume={31},
  pages={829-857}
}
We study the problem of minimizing the sum of a smooth convex function and a convex blockseparable regularizer and propose a new randomized coordinate descent method, which we call ALPHA. Our method at every iteration updates a random subset of coordinates, following an arbitrary distribution. No coordinate descent methods capable to handle an arbitrary sampling have been studied in the literature before for this problem. ALPHA is a remarkably flexible algorithm: in special cases, it reduces to… CONTINUE READING
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