摘要
本文提出了一种求解带二次约束和线性约束的二次规划的分支定界算法.在算法中,我们运用Lipschitz条件来确定目标函数和约束函数的在每个n矩形上的上下界,对于n矩形的分割,我们采用选择n矩形最长边的二分法,同时我们采用了一些矩形删除技术,在不大幅增加计算量的前提下,起到了加速算法收敛的效果.从理论上我们证明了算法的收敛性,同时数值实验表明该算法是有效的.
In this paper a branch and bound approach for nonconvex quadratic programming with quadratic constrained is introduced. In the proposed algorithm, we make use of the Lipschitz condition to determine lower bounds of functions over each rectangle. Further, convergence of the algorithm is proved. The implementation of the algorithms on several test problems is reported with satisfactory numerical results.
出处
《应用数学》
CSCD
北大核心
2006年第1期25-29,共5页
Mathematica Applicata
基金
SupportedbytheNationalNaturalScienceFoundation(10271073)
关键词
二次规划
二次约束
分支定界
最优化
Quadratic programming
Branch and bound algorithm
Lipschitz condition
