Simple solvers for large quadratic programming tasks

Authors:
Vojtěch Franc;Václav Hlaváč
Affiliations:
Center for Machine Perception, Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University;Center for Machine Perception, Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University
Venue:
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Year:
2005

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Abstract

This paper describes solvers for specific quadratic programming (QP) tasks. The QP tasks in question appear in numerous problems, e.g., classifier learning and probability density estimation. The QP task becomes challenging when large number of variables is to be optimized. This the case common in practice. We propose QP solvers which are simple to implement and still able to cope with problems having hundred thousands variables.