One method of solving the problem of sphere-constrained convex quadratic programming;
介绍一种求解高维凸二次规划的可行方向法。
Lower approximation algorithm and its solve of convex quadratic programming are also given in this article.
混合优化控制算法，给出了求解最优控制器的上逼近算法及其凸二次规划求解方法。
In this paper, a branch-and-bound method is proposed for non-convex quadratic programming problems with convex constrains.
针对凸约束非凸二次规划问题，给出了一个分枝定界方法。
When used to solve the convex quadratic programming problems with super large scale of training samples(11000 training samples), the algorithm designed in this paper works better.
本文采用的方法在解决大规模训练问题（如11000个训练样本）时表现出的性能令人满意。
SVM transforms machine learning to solve an optimization problem, and to solve a convex quadratic programming problem by the optimization theory and method constructing algorithms.
它将机器学习问题转化为求解最优化问题，并应用最优化理论构造算法来解决凸二次规划问题。
Some numerical results for a large number of random convex quadratic programming problems show that the new algorithm is efficient and might be a polynomial-time algorithm under some conditions.
大量的关于随机的凸二次规划问题的数值实验结果表明它的计算效率是高的，在某些条件下可能是多项式时间算法。
Furthermore, it gives an optimum condition and a simple algorithm of the special interaction programming by changing it into a special convex quadratic bilevel programming.
并且通过把该交叉规划转化为特殊的凸二次双水平规划，给出这类交叉规划的最优性条件和求解算法。
Furthermore, it gives an optimum condition and a simple algorithm of the special interaction programming by changing it into a special convex quadratic bilevel programming.
并且通过把该交叉规划转化为特殊的凸二次双水平规划，给出这类交叉规划的最优性条件和求解算法。
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