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The convergence of the new algorithm based on Bayes′s theorem and stochastic transform process are analyzed.

基于Bayes定理和随机状态转移过程对新算法的收敛性进行分析。

This dissertation consists of three chapters, in which we discuss the convergence of NA bootstrap samples.

本文共三章,主要讨论了NA随机变量序列的bootstrap收敛性。

On the other hand, an iterative hard shrinkage algorithm is obtained by using the generalized conditional gradient method, as well as convergent theorems for Solutions and stopping criterion.

此外,利用广义条件梯度法得到了迭代硬阈值算法,并给出了解的收敛性定理和停止准则。

In terms of the minimum question of a positive definite quadratic function,a set of Newton directions in minimum points derived with an accurate single dimensional search are used to produce a set of conjugate direction ,and a proof of convergence is presented.

对正定二次函数极小问题,利用精确一维搜索所得极小点处的牛顿方向来生成一组共轭方向,并给出收敛性证明

In this dissertation, we presented the global convergence properties of nonlinear conjugate gradient methods without line search and with strong Wolfe conditions, Goldstein inexact line search.

本文给出了无约束最优化算法—非线性共轭梯度法在不需线搜索和用强Wolfe条件下以及用Goldstein非精确线搜索产生搜索步长情况下的全局收敛性证明。

Practical calculation shows that the proposed method can improve the convergence under large power angle conditions and also reduce the computing time by more than 80 percent.

实例计算表明,场路耦合端点量迭代法能改善大功角稳态工况下算法的收敛性,并使计算时间缩短了80%以上。

All these elements demonstrate favorable convergence behavior over the existing counterparts, free from spurious kinematic modes and do not exhibit locking phenomenon in thin plate/shell limit.

所有这些单元都显示了良好的收敛性;不含有多余机动模式;当趋于薄板/壳极限时,不存在"自锁"现象。

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。

The convergence theorem of the algorithm and its inexact version are both studied.

对这种方法及其不精确形式的收敛性分别进行了研究。

In Chaper 4,we extendthe simple Newton's method to sovle our problems,the convergence theorem is proved.

在第四章,我们将简单Newton方法加以推广,用于求解取值在闭凸锥中的非线性包含问题,建立了一般的收敛性定理。

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