“…On the theoretical side, FW methods come with iteration complexity bounds that are independent of the number of variables in the problem, and sparsity guarantees that hold during the whole execution of the algorithm [3,22]. In addition, several variants of the basic procedure have been analyzed, which can improve the convergence rate and practical performance of the basic FW iteration [15,35,26,6]. From a practical point of view, they have emerged as efficient alternatives to traditional methods in several contexts, such as large-scale SVM classification [7,8,35,6] and nuclear norm-regularized matrix recovery [22,42].…”