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The past years have witnessed many dedicated open-source projects that builtand maintain implementations of Support Vector Machines (SVM), parallelized forGPU, multi-core CPUs and distributed systems. Up to this point, no comparableeffort has been made to parallelize the Elastic Net, despite its popularity inmany high impact applications, including genetics, neuroscience and systemsbiology. The first contribution in this paper is of theoretical nature. Weestablish a tight link between two seemingly different algorithms and provethat Elastic Net regression can be reduced to SVM with squared hinge lossclassification. Our second contribution is to derive a practical algorithmbased on this reduction. The reduction enables us to utilize prior efforts inspeeding up and parallelizing SVMs to obtain a highly optimized and parallelsolver for the Elastic Net and Lasso. With a simple wrapper, consisting of only11 lines of MATLAB code, we obtain an Elastic Net implementation that naturallyutilizes GPU and multi-core CPUs. We demonstrate on twelve real world datasets, that our algorithm yields identical results as the popular (and highlyoptimized) glmnet implementation but is one or several orders of magnitudefaster.

A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing