Nikolas List Convergence of a Generalized Gradient Selection Approach
for the Decomposition Method Proceedings of the 15th International Conference on
Algorithmic Learning Theory, 339-348, 2004.
Abstract
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.
