An ADMM Solver for the $\mathbf{MKL} -L_{0/1}-\mathbf{SVM}$

We formulate the Multiple Kernel Learning (abbreviated as MKL) problem for the support vector machine with the infamous (0, 1)-loss function. Some first-order optimality conditions are given and then exploited to develop a fast ADMM solver for the nonconvex and nonsmooth optimization problem. A simple numerical experiment on synthetic planar data shows that our
$\mathbf{MKL} -L_{0/1}-\mathbf{SVM}$
framework could be promising.