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1 results

  • 6 - Coordinate Descent

  • Stephen J. Wright, University of Wisconsin, Madison, Benjamin Recht, University of California, Berkeley
  • Book:
    Optimization for Data Analysis
    Published online:
    31 March 2022
    Print publication:
    21 April 2022, pp 100-117

  • Summary

    This chapter describes the coordinate descent approach, in which a single variable (or a block of variables) is updated at each iteration, usually based on partial derivative information for those variables, while the remainder are left unchanged. We describe two problems in machine learning for which this approach has potential advantages relative to the approaches described in previous chapters (which make use of the full gradient), and present convergence analyses for the randomized and cyclic versions of this approach. We show that convergence rates of block coordinate descent methods can be analyzed in a similar fashion to the basic single-component methods.