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datasets
experiments
numba_utils
objective_functions
pretty_plot
Poster.pdf
PresentationSlides.pdf
README.md
coordinate_descent.py
dataset_utils.py
example.py
experiments.json
main.py
selection_rules.py
unit_tests.py
update_rules.py

README.md

  • Paper: Coordinate descent converges faster with the Gauss-Southwell rule than random selection
  • Authors: Julie Nutini, Mark Schmidt, Issam H. Laradji, Michael Friedlander, Hoyt Koepke
  • Link: http://arxiv.org/abs/1506.00552

The corresponding paper (http://arxiv.org/abs/1506.00552) presents new analysis of the coordinate descent algorithm that uses the Gauss Southwell (GS) rule, which can be much faster than random selection; it shows that using exact coordinate optimization and/or lipschitz constants can improve the GS convergence rate. The paper also contains analysis on using fast approximation techniques for the GS rule and analysis for different proximal-gradient GS rules.

Using the code

The code serves two main purposes,

  • to generate the experimental results for the coordinate descent algorithms presented in the paper; including, but not limited to, random selection, non-uniform lipchitz sampling, and various gauss-southwell rules; and

  • to be accessible for users who would like to apply these algorithms on other datasets - the code can be used in a similar way as the algorithms hosted in scikit-learn repository.