Using an iterative linear solver in an interior‐point method for generating support vector machines

This paper concerns the generation of support vector machine classifiers for solving the pattern recognition problem in machine learning. A method is proposed based on interior‐point methods for convex quadratic programming. This interior‐point method uses a linear preconditioned conjugate gradient method with a novel preconditioner to compute each iteration from the previous. An implementation is developed by adapting the object‐oriented package OOQP to the problem structure. Numerical results are provided, and computational experience is discussed.