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global convergence相关的网络例句

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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]

Hence we first propose a globally optimized random search algorithm and prove its asymptotic global convergence; then to improve its convergence velocity and reduce implementation complexity, we propose a practical sub-optimal random search algorithm and analyze its implementation complexity.

为此本文首先给出了一种用于OFDM符号估计的全局最优随机搜索方法,并证明了其渐进全局收敛特性;同时为了改进全局最优的随机搜索算法的收敛速度和实现复杂度,提出了一种可实现的次优的随机搜索算法,并结合实现分析了所提的次优随机算法的计算复杂度。

FR conjugate gradient methods with perturbations are proposed. The global convergence property of the first method is proved under the condition of main directions' sufficient descent. Whereas, in the proof of the convergence for the other two methods, we only need main directions' descent. Importantly and quite interesting, boundedness conditions such as objective function being bounded below, boundedness of level set are not needed. Chapter 5 presents a version of Dai-Yuan conjugate gradient method with perturbations.

在主方向充分下降的条件下证明了第一个方法的全局收敛性,而后两个方法的收敛性是在主方向下降的条件下证明的,这些收敛性证明的一个共同特征就是不需要目标函数有下界或水平集有界等有界性条件,第5章采用Wolfe或Armijo步长规则提出了带扰动项的Dai-Yuanabbr。

Furthermore, the first-order optimality condition and its equivalent reformulations for generalized semi-infinite max-min programming with a non-compact set are presented using the lower-Hadamard directional derivative and subdifferential.2. Chapter 3 studies the gradient-type methods for unconstrained optimization problems. Section 1 proposes a new class of three-term memory gradient methods. The global convergence property of the method is established. Furthermore, in order to improve the convergence property of the method, a new class of memory gradient projection methods is presented with the property that the whole sequence of iterates converges to a solution to the problem under the conditions such as pseudo-convexity and continuous differentiability of objective function. In section 2, two new classes of methods, called gradient-type method with perturbations and hybrid projection method with perturbations, are proposed. In these methods, non-monotone line search technique is employed, which makes them easily executed in computer.

第3章研究了无约束优化问题的梯度型算法,第1节提出了一类新的三项记忆梯度算法,讨论了算法的全局收敛性,进一步提出了一类新的具有更好收敛性质的记忆梯度投影算法,并证明了该算法在函数伪凸的情况下具有整体收敛性,第2节在非单调步长搜索下提出了带扰动项的梯度型算法及其混合投影算法,这两类算法的一个重要特征就是步长采用线搜索确定而不象许多文献中那样要求步长趋于零,这样更容易在计算机上实现,在较弱的条件下证明了这些算法的全局收敛性,数值算例表明了算法的有效性。

The essence of EGA's global convergence and its behavior characteristics are analyzed. Two kinds of realization methods of EGA are studied and convergence analysis of them is carried out.

首次给出了最优保留及最优保留GA的规范化定义,分析了EGA全局收敛的本质及表现特征。

The paper proves that the search process of the non-crossover genetic algorithm is an ergodic homogeneous Markov Chain. The proof of global convergence of NCGA is presented is this paper. The upper bound of convergence speed of the NCGA is the same as the CGA. The possibly of retain the promising genes of NCGA is larger than CGA.

对于本人可以找到的32种典型的非线性优化测试函数,应用无交叉算子遗传算法,与基于经典遗传算法相比,在相同的进化代数的条件下,NCGA同样取得了略优的收敛速度,在相同CPU计算时间上NCGA也取得略优的结果; 3 提出了复合算子遗传算法。

In the algorithm,a kind of merit function and the schemes of nonmono-tone line search are used to have the algorithm be of the global convergence andsuperlinear convergence and have the algorithm be of a extended application range,especially at time of the feasible initial point is hard to be obtained.

该方法适用于任意的起始点。并且使用了一类效益函数,ε—积极约束策略及非单调线搜索,从而使算法保持了整体收敛性和局部超线性收敛性,并具有较广的适用范围,尤其在可行的起始点难于得到的情况下。

In this paper the convergence rate and the global convergence properties of the Gradient Method are studied.

本文研究了梯度法的收敛速度和全局收敛性。

The main characteristic of this method has good global convergence and fast local convergence.

该算法的特点是具有较好的全局收敛性和快速的局部收敛性。

Simulation tests show that there is much improvement in the speed of convergence and the probability of global convergence.

仿真实验证明,算法的收敛速度和全局收敛概率都有显著的提高。

The algorithm has good global convergence and convergence speed.

算法有着良好的全局收敛能力和收敛速度。

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