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.
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.
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.
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