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.
This work was supported in part by the IST Programm of the European Community, under the PASCAL Network of Excellence, IST-2002-506778. This publication only reflects the authors views.
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List, N. (2004). Convergence of a Generalized Gradient Selection Approach for the Decomposition Method. In: Ben-David, S., Case, J., Maruoka, A. (eds) Algorithmic Learning Theory. ALT 2004. Lecture Notes in Computer Science(), vol 3244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30215-5_26
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DOI: https://doi.org/10.1007/978-3-540-30215-5_26
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