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superlinear convergence相关的网络例句

查询词典 superlinear convergence

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

This paper studies the superlinear convergence behavior of GMRES and GMRES, and supplies explain through experiment.

本文描绘了GMRES和GMRES的加速收敛现象,并通过实验给予解释。

And we show that the new trust region algorithm preserves the superlinear convergence of the traditional trust region algorithms under certain conditions.

在适当条件下,新的信赖域算法仍然具有超线性收敛性。

In the sixth chapter, we prove the local linear and Q-superlinear convergence of the modified TRCM methods.

在第六章我们证明了本文提出的锥模型信赖域方法的局部线性和超线性收敛性。

The superlinear convergence property of the algorithm is proved in this paper, and compared with the Tensor algorithm. It is proved that this method is more efficient for solving unconstrained optimization whose object function is of rank one defect.

文中证明了该算法是一个具有超线性收敛的算法,并且把修正的BFGS算法同Tensor方法进行了数值比较,证明了该算法对求解秩亏一的无约束优化问题更有效。

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。

We also introduce a control function, prove the global convergence, and prove the local superlinear convergence under some conditions. Then we propose another new algorithm by using an existed approximation functions, which has the same characters as the first algorithms does. Finally, the result of the numerical experiments indicates the efficiency of the first algorithm. The paper contains four parts.

本文还引入了新的控制函数,并证明了算法具有全局收敛性和在一定的条件下具有局部超线性收敛性;然后,在第一个算法的基础上,本文利用已有的光滑逼近函数提出了另外一个新的算法,通过适当参数选取,证明了新的算法具有与第一个算法同样良好的收敛性质;最后,通过数值计算说明了算法的高效性。

In Chapter 3, we propose two new quasi-Newton type trust region methods for solving unconstrained optimization problems. These algorithms combine two types of the preconvex part of modified Broydens family proposed in chapter 1 with trust region algorithm effectively, they have global convergence property and superlinear convergence rate.

第三章,给出了用来求解无约束最优化问题两种新的拟牛顿信赖域算法,这些算法将第一章给出的两种修改Broyden非凸族与信赖域算法有效结合起来,它们具有全局收敛性和超线性收敛速度。

Which possesses global convergence and locally superlinear convergence proven without strict complementarity at the Danskin point.

在不假设在Danskin点处满足严格互补条件的情况下证明了算法具有超线性收敛速度及全局收敛的性质。

And trust region type algorithm with memoryless technology not only satisfies new factorized quasi-Newton equation, but also saves much storage and algebraic operations than the original factorized quasi-Newton methods.we prove it posses the global convergence and the local superlinear convergence under some condition.

本文中利用无记忆技术产生的无记忆型信赖域算法不仅满足新分解牛顿方程,具有其所有的良好性质,而且比一般的分裂拟牛顿法节省存储空间,减少计算量。本文证明了在一定假设条件下该算法具有全局收敛性及局部超线性收敛性。

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