Journal of Applied Mathematics

A Global Optimization Algorithm for Generalized Quadratic Programming

Hongwei Jiao and Yongqiang Chen

Full-text: Open access

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.

Article information

Source
J. Appl. Math., Volume 2013 (2013), Article ID 215312, 9 pages.

Dates
First available in Project Euclid: 14 March 2014

Permanent link to this document
https://projecteuclid.org/euclid.jam/1394808167

Digital Object Identifier
doi:10.1155/2013/215312

Mathematical Reviews number (MathSciNet)
MR3122109

Zentralblatt MATH identifier
06950559

Citation

Jiao, Hongwei; Chen, Yongqiang. A Global Optimization Algorithm for Generalized Quadratic Programming. J. Appl. Math. 2013 (2013), Article ID 215312, 9 pages. doi:10.1155/2013/215312. https://projecteuclid.org/euclid.jam/1394808167


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