Convergence of a generalized Gradient Selection Approach for the
In: ALT 2004, 02-05 Oct 2004, Padova, Italy.
The decomposition method is currently one of the major methods for
solving the convex quadratic optimization problems being associated
with support vector machines. For a special case of such problems the
convergence of the decomposition method to an optimal solution has
been proven based on a working set selection via the gradient of the
objective function. In this paper we will show that a generalized
version of the gradient selection approach and its associated
decomposition algorithm can be used to solve a much broader class of convex
quadratic optimization problems.