Fast Distributed Coordinate Descent for Non-Strongly Convex Losses

Olivier Fercoq, Zheng Qu, Peter Richtárik, Martin Takáč

Research output: Working paper


We propose an efficient distributed randomized coordinate descent method for minimizing regularized non-strongly convex loss functions. The method attains the optimal $O(1/k^2)$ convergence rate, where $k$ is the iteration counter. The core of the work is the theoretical study of stepsize parameters. We have implemented the method on Archer - the largest supercomputer in the UK - and show that the method is capable of solving a (synthetic) LASSO optimization problem with 50 billion variables.
Original languageEnglish
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
Publication statusPublished - 21 May 2014


  • math.OC
  • cs.LG


Dive into the research topics of 'Fast Distributed Coordinate Descent for Non-Strongly Convex Losses'. Together they form a unique fingerprint.

Cite this