介绍一种求解高维凸二次规划的可行方向法。
One method of solving the problem of sphere-constrained convex quadratic programming;
针对凸约束非凸二次规划问题，给出了一个分枝定界方法。
In this paper, a branch-and-bound method is proposed for non-convex quadratic programming problems with convex constrains.
然而，支持向量机的训练过程等价于求解一个约束凸二次规划。
However, the training procedure of support vector machines amounts to solving a constrained quadratic programming.
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。
Lower approximation algorithm and its solve of convex quadratic programming are also given in this article.
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。
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.
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。
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.
对于大规模的具有伪凸目标函数的二次规划问题，本文提出一种分解算法。
This paper proposes a decomposition algorithm for large scale quadratic programming with a pseudoconvex objective function.
SSVM模型的基本思想是将标准的支撑向量机模型转化成一个无约束二次凸规划模型进行求解。
The key idea of SSVM is to transform the standard model of SVM into an unconstraint quadric convex programming problem.
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.
然后根据两个双层规划的最优解和最优目标值之间的关系，提出一种简单有效的算法来解决非增值型凸二次双层规划间题。
And then a simple effective algorithm for the VQBP is proposed based on the relationship between the optimal solutions of the two value-type bilevel programming problems.
然后根据两个双层规划的最优解和最优目标值之间的关系，提出一种简单有效的算法来解决非增值型凸二次双层规划间题。
And then a simple effective algorithm for the VQBP is proposed based on the relationship between the optimal solutions of the two value-type bilevel programming problems.
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