On the Maximization of a Concave Quadratic Function with Box Constraints

A new method for maximizing a concave quadratic function with bounds on the variables is introduced. The new algorithm combines conjugate gradients with gradient projection techniques, as the algorithm of More and Toraldo [SIAM J. Optimization, 1 (1991), pp. 93-113] and other well-known methods do....

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Bibliographic Details
Published in:SIAM journal on optimization 1994-02, Vol.4 (1), p.177-192
Main Authors: Friedlander, Ana, Martínez, José Mario
Format: Article
Language:English
Subjects:
Algorithms
Applied mathematics
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Summary:A new method for maximizing a concave quadratic function with bounds on the variables is introduced. The new algorithm combines conjugate gradients with gradient projection techniques, as the algorithm of More and Toraldo [SIAM J. Optimization, 1 (1991), pp. 93-113] and other well-known methods do. A new strategy for the decision of leaving the current face is introduced that makes it possible to obtain finite convergence even for a singular Hessian and in the presence of dual degeneracy. Numerical experiments are presented.
ISSN:1052-6234
1095-7189
DOI:10.1137/0804010