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Parallel Coordinate Descent for L1-Regularized Loss Minimization

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arxiv 1105.5379 v1 pith:IFAXY3GK submitted 2011-05-26 cs.LG cs.ITmath.IT

Joseph K. Bradley , Aapo Kyrola , Danny Bickson , Carlos Guestrin This is my paper · ORCID
classification cs.LGcs.ITmath.IT
keywords shotguncoordinatedescentl1-regularizedparallelalgorithmalgorithmsbehavior
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We propose Shotgun, a parallel coordinate descent algorithm for minimizing L1-regularized losses. Though coordinate descent seems inherently sequential, we prove convergence bounds for Shotgun which predict linear speedups, up to a problem-dependent limit. We present a comprehensive empirical study of Shotgun for Lasso and sparse logistic regression. Our theoretical predictions on the potential for parallelism closely match behavior on real data. Shotgun outperforms other published solvers on a range of large problems, proving to be one of the most scalable algorithms for L1.

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