本文作者的目标是应用二次优化理论以获得一般情况下的最优权系数，所得结果突破了前述的方差阵的限制。
In this paper, the authors aim to use quadratic optimization theory in obtaining generalized optimal weights, whereby, the restriction on the covariance matrix is just a mere special case.
采用二次饱和D—最优设计建立了精播条件下不同类型花生品种N肥用量和密度与产量的数学模型，探讨肥料与密度的产量效应及优化配置。
Mathematic models between yield and N fertilizer and plant density for two peanut variety types were set up by using quadratic saturation D-optimization design under single-seed planting.
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.
采用二次回归旋转正交组合实验设计方法，探讨了魔芋葡甘聚糖凝胶珠的硫酸酯化修饰的最优化工艺条件。
The optimal technical conditions for sulfated modification of konjac glucomannan gel beads were (investigated) by the quadratic regression rotatable orthogonal design.
本文研究求解约束最优化问题的序列二次规划算法(SQP算法)。
In this paper, we are concerned with the sequence quadratic programming (SQP) methods for solving constrained optimization problems.
该方法将模型修正问题转化为一个带二次约束的最优化问题。
The updating problems of structure models are turned into the optimization with a quadratic constraint.
该类算法的基本思想是通过求解一系列二次函数在信赖域中的极小值点逼近最优化问题的解。
The basic idea of these methods is to approximate the optimization problem by a sequence of quadratic minimization problems subject to some trust region.
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.
结合旅行商问题、二次指派问题以及网络路由问题等典型组合优化问题，概述了ACO在静态组合最优化和动态组合优化问题中的应用。
The applications of ACO to static and dynamic COPs, such as traveling salesman problem, quadratic assignment problem, network routing problem are reviewed.
对求解CTDM的部分线性化算法进行分析，提出了在进行步长优化时使用二次插值法得到模拟最优步长的方法。
By analyzing the partial linearization algorithm of CTDM, a quadratic interpolation method is proposed to obtain the approximated optimal step size.
根据最优化原理，按线性定常二次型最优跟踪器设计了针对任意载荷谱的最优控制器。
The optimal_Controller is designed for random load on the optimal theory and linear optimal tracker.
根据无约束最优化问题的梯度算法，提出了二次梯度算法，并证明了其收敛性。
In this paper, the gradient computational algorithm about unconditional extreme value is given.
根据无约束最优化问题的梯度算法，提出了二次梯度算法，并证明了其收敛性。
In this paper, the gradient computational algorithm about unconditional extreme value is given.
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