This work reports the theoretical and computational results concerning some recent gradient-based methods of Barzilai-Borwein type for Mathematical Programming and their parallel implementation for the solution of the training problem in large-scale support vector machines applications. Recently developed steplength updating rules are implemented as the core of a parallel decomposition technique in an existing object-oriented code. Good performances are obtained by the code tuning and optimization on well known challenging data sets, up to 64 processors. Moreover, a meaningful new formulation of the standard binary optimal separating hyperplane problem is given, which has interesting theoretical properties.

Parallel Gradient Methods for Some Classes of Large-Scale Nonlinear Programming Problems

ZANGHIRATI, Gaetano;
2008

Abstract

This work reports the theoretical and computational results concerning some recent gradient-based methods of Barzilai-Borwein type for Mathematical Programming and their parallel implementation for the solution of the training problem in large-scale support vector machines applications. Recently developed steplength updating rules are implemented as the core of a parallel decomposition technique in an existing object-oriented code. Good performances are obtained by the code tuning and optimization on well known challenging data sets, up to 64 processors. Moreover, a meaningful new formulation of the standard binary optimal separating hyperplane problem is given, which has interesting theoretical properties.
9788886037211
Parallel gradient methods; nonlinear programming; large-scale problems
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/534666
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