PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A General Convergence Theorem for the Decomposition Method
Nikolas List and Hans Simon
Proceedings of COLT 2004 pp. 363-377, 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. Although there exist some versions of the method that are known to converge to an optimal solution, the general convergence properties of the method are not yet fully understood. In this paper, we present a variant of the decomposition method that basically converges for any convex quadratic optimization problem provided that the policy for working set selection satisfies three abstract conditions. We furthermore design a concrete policy that meets these requirements.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:58
Deposited By:Hans Simon
Deposited On:14 May 2004