PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Gradient projection methods for quadratic programs and applications in training support vector machines
Thomas Serafini, Gaetano Zanghirati and Luca Zanni
Optimization Methods and Software Volume 20, Number 2-3, pp. 353-378, 2005. ISSN 1055-6788

Abstract

Gradient projection methods based on the Barzilai-Borwein spectral steplength choices are considered for quadratic programming problems with simple constraints. Well-known nonmonotone spectral projected gradient methods and variable projection methods are discussed. For both approaches the behavior of different combinations of the two spectral steplengths is investigated. A new adaptive steplength alternating rule is proposed that becomes the basis for a generalized version of the variable projection method (GVPM). Convergence results are given for the proposed approach and its effectiveness is shown by means of an extensive computational study on several test problems, including the special quadratic programs arising in training support vector machines. Finally, the GVPM behavior as inner QP solver in decomposition techniques for large-scale support vector machines is also evaluated.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3367
Deposited By:Gaetano Zanghirati
Deposited On:09 February 2008