Parallel decomposition approaches for training support vector machines

Abstract

This chapter discusses parallel decomposition techniques for solving the large quadratic programming (QP) problems arising in training support vector machines. A recent technique is improved by introducing an efficient solver for the inner QP sub-problems and a preprocessing step useful to hot start the decomposition strategy. The effectiveness of the proposed improvements is evaluated by solving large-scale benchmark problems on different parallel architectures. For a parallel solution of the QP sub-problem at each decomposition iteration, a generalized variable projection method is proposed based on an adaptive step-length selection. Since this solver has the same cost per iteration and a better convergence rate than the variable projection method used in, a remarkable time reduction is observed in the sub-problems solution. The computational experiments on well known large-scale test problems showed that the new decomposition approach, based on the above improvements, outperforms the technique, in both, in serial and parallel environments and can further achieve a better scalability.