PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Convergence of a generalized Gradient Selection Approach for the Decomposition Method
Niko List
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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:396
Deposited By:Nikolas List
Deposited On:19 December 2004