Abstract
In this paper, we present a new rectangle Branch and Bound approach for solving non convex quadratic programming problems in which we construct a new lower approximate convex quadratic function of the objective quadratic function over an n-rectangle \(S^{k}=\left[ a^{k},b^{k}\right] \) or \(S^{k}= \left[ L^{k},U^{k}\right] \). This quadratic function (the approximate one) is given to determine a lower bound of the global optimal value of the original problem (NQP) over each rectangle. In the other side, we apply a simple two-partition technique on rectangle, as well as, the tactics on reducing and deleting subrectangles are used to accelerate the convergence of the proposed algorithm. This proposed algorithm is proved to be convergent and shown to be effective with some examples.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Horst, R., Pardalos, P.M., Thoai, N.V.: Introduction to Global Optimization. Kluwer Academic Publishers, Dordrecht (1995)
Gao, Y., Xue, H., Shen, P.: A new rectangle branch and bound reduce approach for solving non convex quadratic programming problems. Appl. Math. Comput. 168, 1409–1418 (2005)
Honggang, X., Chengxian, X.: A branch and bound algorithm for solving a class of DC-programming. Appl. Math. Comput. 165, 291–302 (2005)
Pardalos, P.M.: Global optimization algorithms for linearly constrained indefinite quadratic problems. Comput. Math. Appl. Lic. 21(6–7), 87–97 (1991)
Jiao, H.: A branch and bound algorithm for globally solving a class of non convex programming problems. Nonlinear Anal. 70, 1113–1123 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Benacer, R., Gasmi, B. (2016). Branch and Bound Method to Resolve the Non-convex Quadratic Problems. In: Anastassiou, G., Duman, O. (eds) Intelligent Mathematics II: Applied Mathematics and Approximation Theory. Advances in Intelligent Systems and Computing, vol 441. Springer, Cham. https://doi.org/10.1007/978-3-319-30322-2_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-30322-2_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-30320-8
Online ISBN: 978-3-319-30322-2
eBook Packages: EngineeringEngineering (R0)