An ADMM Solver for the MKL-L_{0/1}-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 MKL-L_{0/1}-SVM framework could be promising.