  Frank-Wolfe algorithms for convex minimization have recently gained
considerable attention from the Optimization and Machine Learning communities,
as their properties make them a suitable choice in a variety of applications.
However, as each iteration requires to optimize a linear model, a clever
implementation is crucial to make such algorithms viable on large-scale
datasets. For this purpose, approximation strategies based on a random sampling
have been proposed by several researchers. In this work, we perform an
experimental study on the effectiveness of these techniques, analyze possible
alternatives and provide some guidelines based on our results.
