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2013 A Global Optimization Algorithm for Generalized Quadratic Programming
Hongwei Jiao, Yongqiang Chen
J. Appl. Math. 2013: 1-9 (2013). DOI: 10.1155/2013/215312

Abstract

We present a global optimization algorithm for solving generalized quadratic programming (GQP), that is, nonconvex quadratic programming with nonconvex quadratic constraints. By utilizing a new linearizing technique, the initial nonconvex programming problem (GQP) is reduced to a sequence of relaxation linear programming problems. To improve the computational efficiency of the algorithm, a range reduction technique is employed in the branch and bound procedure. The proposed algorithm is convergent to the global minimum of the (GQP) by means of the subsequent solutions of a series of relaxation linear programming problems. Finally, numerical results show the robustness and effectiveness of the proposed algorithm.

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Hongwei Jiao. Yongqiang Chen. "A Global Optimization Algorithm for Generalized Quadratic Programming." J. Appl. Math. 2013 1 - 9, 2013. https://doi.org/10.1155/2013/215312

Information

Published: 2013
First available in Project Euclid: 14 March 2014

zbMATH: 06950559
MathSciNet: MR3122109
Digital Object Identifier: 10.1155/2013/215312

Rights: Copyright © 2013 Hindawi

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