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与 global convergence 相关的网络例句 [注:此内容来源于网络,仅供参考]

The paper contains four parts. In the first chapter, the application back ground and the main algorithms of the complementarity problems is introduced. In Chapter 2, some basic definitions and theories of complementarity problems are introduced. The 3rd chapter is the most important part of this paper, in which a modified smoothing Newton method is detailed; also the global convergence is established for the method.

全文共分为四章,各部分内容安排如下:第一章是绪论部分,介绍了互补问题的应用背景和近年来有关互补问题求解方法的研究成果;第二章是预备知识,介绍了与求解互补问题有关的一些定义以及相关的定理和推论;第三章是本文的重点,提出了求解互补问题的一种修正的光滑Newton算法,从理论上对算法的全局收敛性了证明;第四章是这种修正的光滑Newton法用于求解广义非线性互补问题中,同样证明了算法的全局收敛性。

We prove that the solution of a nonlinear complementarity problem is exactly the equilibrium point of differential equation system, and prove the asymptotical stability and global convergence.

在一定的条件下我们证明了非线性互补问题的解是该微分方程系统的平衡点,并且证明了该微分方程系统的稳定性和全局收敛性。

Based on the study on how to set the initial population and operational parameter s of operators, a conclusion can be drawn that distribution of the initial population and the selection of operational parameter s of operators directly concerns global convergence and searching efficiency of genetic algorithm.

通过对遗传算法初始种群与操作参数设定问题的研究,认为初始种群的分布状态与算子操作参数的选取直接关系遗传算法的全局收敛性与搜索效率,对初始种群与各操作参数进行合理设定是应用遗传算法进行寻优计算的重要问题。

The simulation results show that extensive mapping ability of neural network and rapid global convergence of genetic algorithm can be obtained by combining genetic algorithm and neural network.

仿真结果证明:遗传算法和神经网络的结合,可兼有神经网络广泛映射能力和遗传算法快速全局收剑等性能。

By 1971, a proof of global convergence for this simple algorithm existed in the optimization text [18], where the technique goes by the name method of local variations.

在1971年之前,一个这种简单算法的全局收敛的证明出现在优化的教科书[18]上,那里这种技术被称为局部变量方法。

With the idea of smoothing Newton method, we propose a new class of smoothing Newton methods for the nonlinear complementarity problem based on a class of special functions. In this paper, complementarity problem is converted into a series of smoothing nonlinear equations and a modified smoothing Newton algorithm is used to solve the equations. We use Newton direction and Gradient direction together in the algorithm which guarantees that our method is globally convergent. Also using another smoothing function, we reformulate the generalized nonlinear complementarity problems defined on a polyhedral cone as a system of smoothing equations and a smooth unconstrained optimization problem. Theoretical results that relate the stationary points of the merit function to the solution of the generalized nonlinear complementarity problems are presented, we use the modified smoothing Newton algorithm in generalized nonlinear complementarity problems, under mild hypothesis, a global convergence is proved.

本文一方面基于现有的各种光滑Newton法的思想和半光滑理论,利用著名的F-B互补函数的光滑形式,首先将互补问题的求解转化为求解一系列光滑的非线性方程组,然后给出了一种修正的光滑Newton法,该方法不仅放宽对函数F的要求,在Newton方程不可解时引入初始效益函数的最速下降方向,而且光滑因子的选择也比较简单可行,同时在适当的条件下,证明了其算法具有全局收敛性;另一方面,借助另一种F-B光滑函数,将多面体锥上的广义互补问题转化为一种光滑形式,讨论了优化问题的稳定点与广义非线性互补问题的解之间的理论关系,并将这种修正的光滑Newton法用于求解广义非线性互补问题中,在适当的条件下,该算法同样具有全局收敛性。

The modified particle swarm optimization is utilized to solve the proposed reactive power optimization model; and combining with non–stationary multi-stage assignment penalty function, the reactive power optimization problem is transferred into a problem to solve extreme-value without constraints, thus the calculation accuracy and global convergence ability of MPSO are effectively improved, and power grid company can achieve maximal economic benefit by means of the proposed algorithm.

应用改进粒子群优化算法求解该无功优化模型,并结合动态调整罚函数法将无功优化问题转化成无约束求极值问题,从而有效地提高了改进粒子群优化算法的全局收敛能力和计算精度,使电网公司取得了最大经济效益。

Then we can establish global convergence to a stationary point, that is, if {xk} is the sequence generated by the affine-scaling interior-point trust-region method, then every limit point of the sequence is a stationary point for the problem.

其次我们建立了收敛到一个稳定点的全局收敛结果,即若{x_k}是由仿射尺度内点信赖域法产生的序列,则序列的每个极限点都是问题的一个稳定点。

Polak's result is as strong as any of the contemporaneous global convergence results for either steepest descent or a globalized quasi-Newton method.

Polak的结果和同时期的任何最速下降或全局拟牛顿法的结果一样强。

The cost functions of the linear transversal and decision-feedback blind equalizers are proposed in this thesis, which are strictly convex function with respect to the complex tap-weight vector of the equalizer and the error probability. It is proved that their global convergence are guaranteed by using gradient algorithm.

本文提出了线性横向结构的盲均衡价值函数,DF结构的盲均衡价值函数,证明了这两种价值函数对均横器抽头矢量和系统误差概率,都是严格U函数;并且证明了用梯度算法,这两种价值函数保证收敛到全局极小点。

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