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Frank-Wolfe algorithms have recently regained the attention of the MachineLearning community. Their solid theoretical properties and sparsity guaranteesmake them a suitable choice for a wide range of problems in this field. Inaddition, several variants of the basic procedure exist that improve itstheoretical properties and practical performance. In this paper, we investigatethe application of some of these techniques to Machine Learning, focusing inparticular on a Parallel Tangent (PARTAN) variant of the FW algorithm that hasnot been previously suggested or studied for this type of problems. We provideexperiments both in a standard setting and using a stochastic speed-uptechnique, showing that the considered algorithms obtain promising results onseveral medium and large-scale benchmark datasets for SVM classification.

A PARTAN-Accelerated Frank-Wolfe Algorithm for Large-Scale SVM Classification