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The method of convex quadratic programming relaxation has been used for the scheduling with rejection .

用凸二次规划松弛方法研究工件具有就绪时间,目标函数为工件总拒绝费用与接受工件的带权总完工时间之和的工件可拒绝排序问题,得到界为2的多项式时间近似算法。

Inequality constrained least-square problems are first changed to convex quadratic programming problems and then solved for the optimal solutions.

研究了不等式约束下的平差问题,即先将不等式约束的最小二乘问题转换成凸二次规划问题,然后求其最优解。

When the Hessian is positive definite, the QP subproblem is a strictly convex quadratic programming.

若QP子问题的Hessian阵正定,则它是一个严格凸二次规划问题。

Kun—Tucker condition is an effective way to deal with convex programming. So we based on this method to analysis the constraint model.

根据库恩——塔克条件解决凸规划问题的思想对模型进行分析,使得模糊需求报童问题更切实际。

Under some assumption conditions, we also developed trust region and affine scaling algorithm and potential reduction algorithm for convex programming problem with linear constraints.

然后在一定的假设条件下,对线性约束下的凸规划问题我们给出了相应的信赖域仿射尺度算法和势函数下降算法。

A parametric optimization method for tracking optimal solution's trajectory of linearly constrained convex separable programming was presented.

提出一种追踪线性约束下凸可分规划问题最优解轨迹的参数化方法。

Convex programming studies the case when the objective function is convex and the constraints, if any, form a convex set.

二次规划的目标函数可以有二次条件,而设置必须指定与线性等式和不等式。

And further more it gives a proof that this optimization problem can be seen as a convex programming in exponential neural manifold.

即将神经网络看作为统计模型,使用常用的统计推断技术,通过优化学习代价函数,对网络参数进行调整,从而达到学习的目的。

Along with the development of convex fuzzy set, people investigate the convexity of fuzzy mapping on convex sets and its application in fuzzy programming .

随着凸模糊集研究的深入与发展,人们讨论了模糊映射在凸集上的凸性及其在模糊规划中的应用。

Therefore, the research on convexity and generalized convexity is one of the most important aspects in mathematical programming.

因此,对凸函数和广义凸函数的研究是数学规划中最重要的内容之一。

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