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global convergence相关的网络例句

查询词典 global convergence

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

During the iterations the trial step is truncated when necessary in order to keep the strict feasibility of the iteration points. It is shown that the LMTI approach and the STI approach have global convergence properties under some reasonable assumptions and will achieve local quadratic convergence rate under some even stronger conditions.

LMTI方法主要利用由有限内存BFGS公式的紧致形式获取的目标函数海色阵的近似矩阵构造信赖域问题模型并对其进行求解;而STI方法在构造的低维子空间上求信赖域子问题的解。

The simulation results show that this algorithm has better global convergence ability and more rapid convergence,and it is superior to Genetic Algorithm,Particle Swarm Optimization algorithm and QPSO algorithm.

仿真实例表明,该算法具有良好的全局收敛性能和快捷的收敛速度,调度效果优于遗传算法、粒子群优化算法和量子粒子群优化算法。

Experimental results show that the new algorithm can greatly improve the global convergence ability and enhance the rate of convergence.

对几个典型的测试函数进行仿真表明,该算法较标准蜂群算法提高了全局搜索能力和收敛速度,改善了优化性能。

The global convergence and the convergence speed of ANGA are more satisfier than SGA.

并进行了仿真实验,将由ANGA优化的负荷分配结果与经验负荷分配结果进行了比较。

By the way, we describe the modified quasi-Newton algorithm used the line search , and have given its global convergence and superlinear convergence.

接下来描述了采用线性搜索的修正拟Newton算法,并给出了它的全局收敛性与超线性收敛性。

Furthermore, the global convergence and superlinear convergence rate are proved under mild assumptions without the strict complementarity.

此外,在不需要严格互补的温和条件下,我们证明了算法的全局收敛性和超线性收敛性。

The algorithm is the combination of the method with global convergence and thatwith locally superlinear convergence.

其特点之一是该方法在每步迭代中只需要计算一个或三个具有相同系数矩阵的线性方程组。

Since a new generalized projection technique is contained in the auxiliary direction formula, under some mild assumptions without the strict complementarity, the global convergence and superlinear convergence of the algorithm can be obtained.

由于一种新的广义投影技术包含在辅助方向的计算公式中,因而在无严格互补等较温和的假设下便可得到算法的全局收敛性及超线性收敛性。

LC1 unconstrained optimization problem was discussed in the second chapter, giving a new trust region method and proving its global convergence and superlinear convergence under some mild conditions .

第二章讨论了LC~1无约束优化问题。给出了一个新的信赖域算法,并在一定的条件下证明了算法的全局收敛性和局部超线性收敛性。

To solve it, we turn it into nonsmooth equations, utilizing inexact theory we give an inexact generalized Newtons method and under some mild conditions we prove that it is global convergence and superlinear convergence .

首先将其约束问题的求解转化为非光滑方程组的求解,然后利用不完全求解理论给出了一个非精确的广义牛顿算法,在一定的条件下证明了算法的全局收敛性和局部超线性收敛性并给出了LC~1非线性约束问题的收敛性条件。

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