A parametric linearizing approach for quadratically inequality constrained quadratic programs

A parametric linearizing approach for quadratically inequality constrained quadratic programs AbstractIn this paper we propose a new parametric linearizing approach for globally solving quadratically inequality constrained quadratic programs. By utilizing this approach, we can derive the parametric linear programs relaxation problem of the investigated problem. To accelerate the computational speed of the proposed algorithm, an interval deleting rule is used to reduce the investigated box. The proposed algorithm is convergent to the global optima of the initial problem by subsequently partitioning the initial box and solving a sequence of parametric linear programs relaxation problems. Finally, compared with some existing algorithms, numerical results show higher computational efficiency of the proposed algorithm. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Open Mathematics de Gruyter

A parametric linearizing approach for quadratically inequality constrained quadratic programs

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Publisher
De Gruyter Open
Copyright
© 2018 Jiao and Chen
ISSN
2391-5455
eISSN
2391-5455
D.O.I.
10.1515/math-2018-0037
Publisher site
See Article on Publisher Site

Abstract

AbstractIn this paper we propose a new parametric linearizing approach for globally solving quadratically inequality constrained quadratic programs. By utilizing this approach, we can derive the parametric linear programs relaxation problem of the investigated problem. To accelerate the computational speed of the proposed algorithm, an interval deleting rule is used to reduce the investigated box. The proposed algorithm is convergent to the global optima of the initial problem by subsequently partitioning the initial box and solving a sequence of parametric linear programs relaxation problems. Finally, compared with some existing algorithms, numerical results show higher computational efficiency of the proposed algorithm.

Journal

Open Mathematicsde Gruyter

Published: Apr 20, 2018

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