Parallel algorithm for support vector machines training and quadratic optimization problems

We consider an iterative algorithm, suitable for parallel implementation, to solve convex quadratic optimization problems with a single constraint and simple bounds on the variables. This approach can be used to effectively solve the quadratic optimization problem arising in training support vector...

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Bibliographic Details
Published in:Optimization methods & software 2005-04, Vol.20 (2-3), p.379-388
Main Author: de Leone, Renato
Format: Article
Language:English
Subjects:
Alternating direction methods
Matrix splitting methods
Quadratic programming
Support vector machines
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Summary:We consider an iterative algorithm, suitable for parallel implementation, to solve convex quadratic optimization problems with a single constraint and simple bounds on the variables. This approach can be used to effectively solve the quadratic optimization problem arising in training support vector machines. The proposed algorithm is a double iterative schema based on inexact matrix splitting and alternating direction method.
ISSN:1055-6788
1029-4937
DOI:10.1080/10556780512331318146