A Global Optimization Algorithm for Concave Quadratic Programming Problems

1993 ◽  
Vol 3 (4) ◽  
pp. 826-842 ◽  
Author(s):  
Immanuel M. Bomze ◽  
Gabriele Danninger
2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Hongwei Jiao ◽  
Yongqiang Chen

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.


2017 ◽  
Vol 13 (3) ◽  
pp. 587-596
Author(s):  
S. Batbileg ◽  
N. Tungalag ◽  
A. Anikin ◽  
A. Gornov ◽  
E. Finkelstein

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