- 更多网络例句与等式约束相关的网络例句 [注：此内容来源于网络，仅供参考]
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In this paper, we study characterizations of admissible in the general linear model Y, Xβ,ε|ε～(0,σ~2∑. We demonstrate that an admissible linear estimator is as the conditional generalized ridge-type estimation in the no constraint, equality constraint, inequality constraint general linear model. We study the superiority of this conditional generalized ridge-type estimation, and prove that it is superior to the restricted best linear unbiased estimator in terms of mean squares. We also give the choice of the matrix K.
本文主要研究了一般线性模型Y，Xβ，ε|ε～(0，σ~2∑中参数估计的可容许性特征，得到了一般线性模型在无约束，有等式约束及有不等式约束下，可容许线性估计均具有条件广义岭估计的形式的结论，并且讨论了这一条件广义岭估计的优良性，证明了其在均方误差和均方误差矩阵意义下都优于约束最小二乘估计，给出了参数矩阵K的选取方法。
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This algorithm constructs a set of linear equations. As a result, the relation of the reconstructed design variables and the original design variables is derived, the variable number of optimum design is decreased from m + n + 2 to 4, and the equality constraint optimization problem is converted into reduced- dimension no equality constraint optimization problem.
该算法通过构造一组线性方程，得到了由重构设计变量到原设计变量的映射关系，使优化设计的变量由原来的m + n + 2个减少到4个，并将有等式约束优化问题转换成降维的无等式约束优化问题。
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Markowitz model of equality constraint protrusion analysis is constructed,including the generic restriction conditions.
文章在将一般化的约束条件纳入分析框架的基础上，建立起Markowitz等式约束凸规划分析模型，运用二次规划问题降维算法的原理，提出了一个求解Markowitz等式约束凸规划模型的快速算法。
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In chapter 2 we propose a linear equality constraint optimization question , the new algorithm is combined with the new conjugate gradient method(HS-DY conjugate gradient method)and Rosen"s gradient projection method , and has proven it"s convergence under the Wolfe line search.In chapter 3 we have combined a descent algorithm of constraint question with Rosen"s gradient projection, and proposed a linear equality constraint optimization question"s new algorithm, and proposed a combining algorithm about this algorithm, then we have proven their convergence under the Wolfe line search, and has performed the numerical experimentation.
在第三章中我们将无约束问题的一类下降算法与Rosen投影梯度法相结合，将其推广到线性等式约束最优化问题，提出了线性等式约束最优化问题的一类投影下降算法，并提出了基于这类算法的混合算法，在Wolfe线搜索下证明了这两类算法的收敛性，并通过数值试验验证了算法的有效性。
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The first one is an algorithm with terminal sliding mode equality constraint. The second is a dual-mode control scheme, which enlarge the terminal region. The sliding mode based MPC is implemented while system state is outside of terminal region, and sliding mode variable structure control designed off-line is used once system state arrives in the terminal region.
第一种方法为终端滑模等式约束非线性模型预测控制方法，首先离线设计切换函数使得滑动模态渐近稳定，然后在有限预测时域的基础上对切换函数附加一个终端等式约束；第二种方法为双模控制方案，在终端区外采用模型预测控制，使得系统状态在有限时域内到达终端区内；在终端区内部采用离线设计的滑模变结构控制，使得系统渐近稳定。
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In the term of mathematics,because equality constraintcondition can be considered as a special case of inequality constraint condition,Lagrange multipliers domain decomposition method is a special case of Lagrangemultiplier method of flexible multi-body system dynamics.
前者满足等式约束，后者满足不等式约束；从数学的角度上看，由于等式约束可以看作不等式约束的特例，因而Lagrange乘子区域分解法可以看作是多柔体系统动力学的Lagrange乘子法特例。
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By redefining multiplier associated with inequality constraint as a positive definite function of the originally-defined multiplier, it is no longer necessary to convert inequality constraints into equality constraints by slack variables in order to reuse the method dedicated to equality constraints for constructing Lagrange neural networks.
若重新定义与不等式约束相关的乘子为正定函数，则在构造Lagrange神经网络时，可直接使用处理等式约束的方法处理不等式约束，不需再用松驰变量将不等式约束转换为等式约束，减小了网络实现的复杂程度。
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Using the result for non-restricted model, we transform the restricted model to common model, and multi collectivity model to single collectivity model, thus, the necessary and sufficient conditions that nonhomogeneous linear estimators for Sβ are admissible in the class of nonhomogeneous linear estimators are obtained which filled the blank for admissibility for restricted linear model.
对线性等式约束的共同均值线性模型，利用无约束单总体模型的现有结果，通过适当变换，把等式约束模型向无约束转换，并把多总体转换为单总体，在矩阵损失下找到了均值参数β的条件可估函数Sβ的线性估计∑mAiyi+a在非齐次线性估计类中可容许的充要条件，填补了等式约束的共同均值线性模型可容许性方i=1面的空白。
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A proper k total colouring of a graph G is a colouring to its vertices and edges using k colours such that no two adjacent or incident elements of G may be assigned the same colour.
利用梯度投影与罚函数相结合的技巧，将带不等式和等式约束的优化问题化成一个无约束问题，提出了初始点可任意的求解不等式、等式约束优化问题的摄动梯度投影算法；参数δk取不同的数还可以得到一类梯度投影算法，从而得出了在搜索方向和步长不精确条件下的梯度投影法，保证了在实际应用中更容易实现；在较弱条件下，证明了该算法的全局收敛性。
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There exits several MDO algorithms. But they are in our opinion either nonefficient or complicated. So we devised an algorithm called Subspace Approximation Optimization . In the SAO algorithm, the whole system is decomposed into one system-level optimization and several disciplinary optimizations so that a large and complicated problem can be divided into several easy-solving sub-problems. The coupling relationships and the coordination among disciplines are presented by equality constraints and these equality constraints are assigned to relevant disciplines. The optimums of design variables in system level optimization are transferred to discipline level optimization. The optimums of design variables in discipline level correspond to the point that is the nearest to the optimums of design variables in system level. If the optimums of design variables in system level are out of feasible region of discipline 1eve1, linear constraints can be built in the system level optimization using the design variable optimums obtained by the discipline level optimization. The system level optimization would improve the design of the whole system with these linear constraints.
目前，国内外已经发展出了多种飞机多学科设计优化算法，本文的重点是针对协同优化算法的不足，提出了子空间近似优化算法(Subspace ApproximatingOptimization，SAO)，SAO算法中，整个系统的优化问题被分解成一个系统级优化和若干学科级优化，而各个学科之间的耦合与权衡关系则被当作等式约束，这些等式约束将被分配到各个学科级优化中去，系统级优化的任务是寻找整个系统的最优解，而学科级优化的目标函数是以系统级优化分配下来的设计点为圆心的超球半径的平方，因此，如果系统级优化分配下来的设计点在学科级优化可行域内，则学科级优化目标函数为0，反之，则学科级优化的最优点是系统级优化当前设计点距离可行域最近的点。
- 更多网络解释与等式约束相关的网络解释 [注：此内容来源于网络，仅供参考]
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equal confront：平等对抗
同等条件:Equal condition | 平等对抗:equal confront | 等式约束:Equal Constraint
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constrained optimization problems：约束最优化问题
约束最忧化:Constrained Optimization Problem | 约束最优化问题:constrained optimization problems | 等式约束问题:equality constrained optimization problem
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equality constraint：等式约束
equality 等式 | equality constraint 等式约束 | equalization 平衡化;同等化
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equality constraint：等式约束条件
equal-value map 等值图 | equality constraint 等式约束条件 | equality gate 同门
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Linear equality constraint：等式约束
言语制约:linguistic constraint | 等式约束:Linear equality constraint | 不等式约束:Inequality constraint
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nonlinear programming：非线性规划
非线性规划(Nonlinear Programming)是具有非线性约束条件或目标函数的数学规划. 非线性规划研究一个 n元实函数在一组等式或不等式的约束条件下的极值问题,且目标函数和约束条件至少有一个是未知量的非线性函数. 目标函数和约束条件都是线性函数的情形则属于线性规划.
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quadratic programming：二次规划
2 1[ ] 1 2 1 λλ w V r 1 42 这是一个二次规划(quadratic programming)问题所谓二次规划是指目标函数是二次的约束条件是 线性不等式(如非负约束)或者等式由于V 是正定的wTVw是凸函数而两个线性约束条件也确定了 一个凸集因此该问题一定有全局
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equality constrains：等式约束
司法公正:judicial equality | 等式约束:equality constrains | 权利平等:equality of rights
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linear programming problem：线性规划问题
linear equality constraints 线性等式约束 | linear programming problem 线性规划问题 | local solution 局部解