含参无导数有记忆迭代方法与其动力系统的构造

借鉴含导数两步迭代格式转化成不含导数两步迭代格式的思想,提出了一种更通用的两步无导数迭代格式,通过权值保证了两步无导迭代格式达到最优阶;利用自加速参数和Newton(牛顿)插值多项式得到了两参和三参有记忆迭代格式,并与已有的两参和三参有记忆迭代格式进行比较;给出了几个格式的吸引域,比较了几个迭代格式的性能.     

具有参数平方收敛的线性插值迭代类

提出了一类具有参数平方收敛的求解非线性方程的线性插值迭代法,方法以Newton法和Steffensen法为其特例,并且给出了该类方法的最佳迭代参数.数值试验表明,选用最佳迭代参数或其近似值的新方法比Newton法和Steffensen方法更有效.     

对称不定问题的不精确Newton法

1.引 言 非线性方程组F（x）=0的数值求解,经典的算法是Newton迭代;xk 1=xk sk,k=0,1,2,…,（1.1）其中的sk满足F’（xk）sk=-F（xk）;k=0,1,2,….（1.2）这里x0为迭代的初始点,{xk}称为Newton迭代序列.当变量个数比较多时,每一步Newton迭代中计算Jacobi矩阵F’（xk）和求解线性方程组（1.2）的代价非常高;特别当xk远离方程组的解x＊时,高精度地求解线性方程组（1.2）     

求解热传导反问题的一种正则化Newton型迭代法

讨论热传导方程求解系数的一个反问题.把问题归结为一个非线性不适定的算子方程后,考虑该方程的Newton型迭代方法.对线性化后的Newton方程用隐式迭代法求解,关键的一步是引入了一种新的更合理的确定(内)迭代步数的后验准则.对新方法及对照的Tikhonov方法和Bakushiskii方法进行了数值实验,结果显示了新方法具有明显的优越性.     

Hammerstein型方程的Galerkin—Newton方法

1 引言 关于Hammerstein型方程的数值逼近方法,许多作者做了工作,例如[1]、[2]、[3]、[4]等,他们把无限维空间中的 Hammerstein型方程转化为有限维空间中的非线性 Hammer-stein型方程,在此基础上,[1]、[2]又用Newton型迭代方法对有限维空间中的非线性方程做了进一步地讨论.[5]中把Newton迭代方法与投影方法结合在一起,考虑了Hilbert空间中具有紧性的非线性算子的不动点问题的数值解法.本文把Galerkin有限维逼近方法与Newton迭代方法紧密结合,把无限维Banach空间中一类具有单调型算子的非线性Ham-merstein型方程的求解问题在迭代过程中化为有限维空间中的线性代数方程组求解.并证明了迭代序列超线性收敛于原方程的解,最后举例说明了这一方法的应用.     

基于函数值Pad逼近的[1/n]阶迭代算法

<正>1引言一般的,我们在求解非线性方程的根时,利用最多的是迭代法,其迭代效果也各不一样[1-4].通常,我们在构造非线性方程求根的迭代方法有Newton迭代算法、Halley迭代算法和割线法等,而Newton迭代格式构造简单且收敛速度较快,又被认为是求解一般非线性方程根的最常用方法.在:Newton迭代公式的推导过程中,利用最多的是泰勒展开式法、切线法、积分法[5].本文基于函数值Pad6逼近的行列式表示[6-7],构造出[1/0]、[1/1]、[1/2]阶Pade逼近     

奇异点处的拟Newton方法

1.引言 假定F是R~m→R~m的可微映射,x~*∈R~m是 F(x)=0 (1.1)的解. 如果在解点处Frechet导数是可逆的,只要F′(x)具有一定的性质,就可保证Newton迭代 x_(i+1)~N=x_i~N-F′(x_i~N)~(-1)F(x_i~N) i=0,1,… (1.2)产生的点列在||x_0-x~*||适当小时二阶收敛于x~*:     

基于L-S方法求解一类方程

基于Lyapunov-Schmidt方法求出给定方程的分岐方程,Newton迭代得到其在分岐点附近的近似非平凡解枝,得到了满意的结果.     

Newton迭代的区域估计与点估计

§1.引言、点估计 Sieve Smale在1986年国际数学家大会上介绍了他在连续复杂性理论方面的开创性研究.从报告摘要[1]及背景论文[2]来看,他着重介绍了解方程的整体代价,其基础是[3]关于Newton迭代的点估计的工作. 设f是从Banach空间E到同型空间F的解析映照.对于点z_0∈E,从z_0开始的Newton迭代是指     

BroWn-Broyden修正算法

1　引　　言求解非线性方程组F(x) =f1 (x1 ,… ,xn)廸n(x1 ,… ,xn)=0　　 F:D Rn→ Rn,(1.1)的 Brown方法 ,是将广义的 L U分解用于 Newton迭代过程 ,而形成的一类具有内外迭代形式的有效算法 .这类算法的特点是每步迭代的函数计算量仅仅为 Newton法的一半 ,而收敛速度则与 Newton法相同 .因此 ,按 Ostrowskii定义的效率指数去衡量 ,Brown方法为一效率较高的算法之一 ,是倍受推崇的 .本文 ,采用修正算法的思想 ,对 Brown方法作进一步改造 ,在不破坏原来的内外迭代形式下 ,使算法在每步迭代中的函数计值量由原来的 O(n2 )下降到 O(…     

求解方程的一类迭代公式

Using the forms of Newton iterative function, the iterative function of Newton's method to handle the problem of multiple roots and the Halley iterative function, we give a class of iterative formulae for solving equations in one variable in this paper and show that their convergence order is at least quadratic. At last we employ our methods to solve some non-linear equations and compare them with Newton's method and Halley's method. Numerical results show that our iteration schemes are convergent if we choose two suitable parametric functions λ(x) and μ(x). Therefore, our iteration schemes are feasible and effective.     

一类非线性矩阵方程对称解的双迭代算法

利用逆矩阵的Neumann级数形式,将在Schur插值问题中遇到的含未知矩阵二次项之逆的非线性矩阵方程转化为高次多项式矩阵方程,然后采用牛顿算法求高次多项式矩阵方程的对称解,并采用修正共轭梯度法求由牛顿算法每一步迭代计算导出的线性矩阵方程的对称解或者对称最小二乘解,建立求非线性矩阵方程的对称解的双迭代算法.双迭代算法仅要求非线性矩阵方程有对称解,不要求它的对称解唯一,也不对它的系数矩阵做附加限定.数值算例表明,双迭代算法是有效的.     

The Impact of Equal Weighting of Low- and High-Confidence Observations on Robust Linear Regression Computations

Equal weighting of low- and high-confidence observations occurs for Huber, Talwar, and Barya weighting functions when Newton's method is used to solve robust linear regression problems. This leads to easy updates and/or downdates of existing matrix factorizations or easy computation of coefficient matrices in linear systems from previous ones. Thus Newton's method based on these functions has been shown to be computationally cheap. In this paper we show that a combination of Newton's method and an iterative method is a promising approach for solving robust linear regression problems. We show that Newton's method based on the Talwar function is an active set method. Further we show that it is possible to obtain improved estimates of the solution vector by combining a line search method like Newton's method with an active set method.This revised version was published online in October 2005 with corrections to the Cover Date.     

一种新的改进Newton法(英文)

本文给出了求解非线性方程的一种新的改进方法.利用Newton法和Heron平均,将新改进方法与其它一些迭代法作比较.数值结果表明该方法具有一定的实用价值.     

Newton's iteration for non-linear equations in Markov chains

A large number of queueing systems may be modelled as infiniteMarkov chains for which the transition matrix has a repetitivestructure. In order to determine the stationary distributionfor these Markov chains, it is necessary to find a particularsolution of a non-linear matrix equation. Various iterative algorithms have been proposed to determinethe matrix of interest. We consider here one particular algorithmand transform it by Newton's method. We show that Newton's algorithmis well defined and converges quadratically in the domain ofinterest.     

解非线性方程的自动调节阻尼法

解非线性方程组的一般方法是将其线性化，形成各种形式的迭代程序进行数值近似计算·对于复杂强非线性问题，在迭代过程中往往不易收敛，甚至数值失稳而发散·不能满足工程要求·常规的牛顿法及改进的牛顿法均未彻底解决这一问题，因而使得复杂强非线性问题的数值模拟计算受到了限制·本文提出一种新的方法———自动调节阻尼法，是对带阻尼因子的牛顿法的进一步改进·引进阻尼因子向量，在迭代过程中，通过判断与调整，不断地自动调节阻尼因子向量，引用有效收敛系数与加速系数，改善对赋初值的要求，加速求解的迭代过程，保证了复杂强非线性方程求解的稳定性·采用这一新的方法，已成功地数值模拟了飞机中的一些复杂的传热问题，可进一步推广用于非线性流动、传热、结构动力响应等各种复杂强非线性的工程问题的数值模拟计算·     

Finite-difference methods of solving the nonlinear heat-conductivity problem

For the nonlinear heat-conductivity problem, implicit difference schemes are constructed with two methods of approximating a boundary condition. Newton's method and a simple iterative process are applied to solve the proposed difference schemes.Translated from Matematicheskie Metody i Fiziko-Mekhanicheskie Polya, No. 25, pp. 25–28, 1987.     

Generalized equations and the generalized Newton method

We give some convergence results on the generalized Newton method (referred to by some authors as Newton's method) and the chord method when applied to generalized equations. The main results of the paper extend the classical Kantorovich results on Newton's method to (nonsmooth) generalized equations. Our results also extend earlier results on nonsmooth equations due to Eaves, Robinson, Josephy, Pang and Chan. We also propose inner-iterative schemes for the computation of the generalized Newton iterates. These schemes generalize popular iterative methods (Richardson's method, Jacobi's method and the Gauss-Seidel method) for the solution of linear equations and linear complementarity problems and are shown to be convergent under natural generalizations of classical convergence criteria. Our results are applicable to equations involving single-valued functions and also to a class of generalized equations which includes variational inequalities, nonlinear complementarity problems and some nonsmooth convex minimization problems.     

One-step jackknife for M-estimators computed using Newton's method

To estimate the dispersion of an M-estimator computed using Newton's iterative method, the jackknife method usually requires to repeat the iterative process n times, where n is the sample size. To simplify the computation, one-step jackknife estimators, which require no iteration, are proposed in this paper. Asymptotic properties of the one-step jackknife estimators are obtained under some regularity conditions in the i.i.d. case and in a linear or nonlinear model. All the one-step jackknife estimators are shown to be asymptotically equivalent and they are also asymptotically equivalent to the original jackknife estimator. Hence one may use a dispersion estimator whose computation is the simplest. Finite sample properties of several one-step jackknife estimators are examined in a simulation study.The research was supported by Natural Sciences and Engineering Research Council of Canada.