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arXiv · 1405.5300

Fast Distributed Coordinate Descent for Non-Strongly Convex Losses

Olivier Fercoq·Zheng Qu·Peter Richtárik·Martin Takáč
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Abstract

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.

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Olivier Fercoq, Zheng Qu, Peter Richtárik, Martin Takáč. 2014-07-27. Fast Distributed Coordinate Descent for Non-Strongly Convex Losses. https://arxiv.org/abs/1405.5300

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