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中文摘要：
对非凸二次规划（QP）问题提出新的确定性全局优化算法,该算法先对目标函数进行分解得到可分的等价问题,再根据相应函数的线性下估计建立原非凸二次规划的线性松弛规划,同时在分枝定界方法中使用区域删减准则来加速算法的收敛性.理论分析和数值计算表明提出的算法是收敛且有效的.
英文摘要：
A new deterministic approach is presented for globally solving nonconvex quadratic programming （QP）. This algorithm first decomposes the objective function of （QP） to obtain a seperable equivalent problem, then a relaxation linear programming of QP is given according to a linear under-estimator of the corresponding objective function. In addition,a region deleting or reducing rule is used to accelerate the convergence of the proposed branch-and-bound algorithm. The theoretical analysis and numerical computation show that the proposed method is convergent and efficient.
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