牛顿迭代法优于预测式迭代法

从两个方面说明牛顿迭代法优于预测式迭代法：1．牛顿迭代法的收敛阶数高于预测式迭代法的收敛阶数。2·从算法复杂性出发，采用Ostrowski给出的“迭代过程有效性指标的概念，得到牛顿迭代法的有效性指标是2^1/3，预测式迭代法的有效性指标是3^1/3．     

一种求解非线性方程的修正牛顿迭代法

基于牛顿迭代法,提出了一种求解非线性方程的修正牛顿迭代法,并证明了该方法是3阶收敛的.最后,通过数值实验对比了常见的其他三种类型的迭代法,说明这类修正牛顿迭代法与传统的牛顿迭代法相比,具有更快的收敛速度,从而进一步证实了该方法的有效性.     

修正的三阶收敛的牛顿迭代法

给出了牛顿迭代法的两种修正形式,证明了它们是三阶收敛的,数值实验表明,与其它已知的三阶收敛的牛顿迭代法相比,修正的牛顿迭代法具有一定的优势.     

基于等距节点积分公式的牛顿迭代法及其收敛阶

利用等距节点的数值积分公式构造牛顿迭代法的变形格式.我们证明了利用4等分5个节点的Newton-Cotes公式构造的变形牛顿迭代法收敛阶为3,并进一步证明了对于最常用的3等分4节点、5等分6节点、6等分7节点、7等分8节点积分公式,所得到的变形牛顿迭代法收敛阶都是3.最后,本文猜想,利用任意等分的积分公式构造变形牛顿迭代法,所得的迭代格式收敛阶都是3.     

评寻求新迭代法的几篇文章

假定φ是求方程f(x)=0数值解的一个迭代法,根据φ由一组初始近似产生f(x)=0的近似解叙列{x_n},具收敛阶数为ρ。倘使,人们来了某种灵感,发现只要稍微增加一点点每步的计算量,就可使收敛阶数比ρ提高好多。于是就发表文章,一种新迭代法便在文献中出现了。     

关于Jain的隐式迭代法的注

本文利用嵌入法思想构造了一类求解非线性方程组的隐式迭代法，分析了方法的收敛阶，给出了具体的计算格式，最后的计算结果表明了方法的有效性．     

修正的三次收敛的牛顿迭代法

给出了牛顿迭代法的两种修正形式,证明了它们都是三阶收敛的,给出的相互比较的数值例子有力地说明了这一点.     

对牛顿迭代法的一个重要修改

对解非线性和超越方程ｆ（ｘ）＝０的牛顿迭代法作了重要的改进·利用动力系统的李雅普诺夫方法,构造了新的“牛顿类”方法·这些新的迭代方法保持了牛顿法的收敛速率和计算效能,摒弃了强加于ｆ（ｘ）的单调性要求ｆ′（ｘ）≠０·     

用正交投影迭代法求矩阵方程AXA~H=B的Hermite解

本文讨论了矩阵方程AXAH=B的Hermite解及其最佳逼近的正交投影迭代法,证明了算法的收敛性,得到收敛速率的估计式.通过数值试验也检验了算法的有效性.     

牛顿迭代法在高中数学中的应用北大核心

1引言高中数学中常用二分法来计算方程的近似解,计算过程简单,只要求函数连续即可,但该方法收敛速度慢,且不能求偶数重根,每一步计算的函数值只用上了他们的符号,计算的结果没有被充分的利用.有没有收敛更快的方法来求解方程的近似解呢?牛顿在《流数法》中给出了求高次代数方程近似解的数值解法:牛顿迭代法.     

解非线性不适定算子方程的一种正则化Newton迭代法

本文探讨一种求解非线性不适定算子方程的正则化Newton迭代法.本文讨论了这种迭代法在一般条件下的收敛性以及其他的一些性质.这种迭代法结合确定迭代次数的残差准则有局部收敛性.     

Radial basis collocation method and quasi‐Newton iteration for nonlinear elliptic problems

This work presents a radial basis collocation method combined with the quasi‐Newton iteration method for solving semilinear elliptic partial differential equations. The main result in this study is that there exists an exponential convergence rate in the radial basis collocation discretization and a superlinear convergence rate in the quasi‐Newton iteration of the nonlinear partial differential equations. In this work, the numerical error associated with the employed quadrature rule is considered. It is shown that the errors in Sobolev norms for linear elliptic partial differential equations using radial basis collocation method are bounded by the truncation error of the RBF. The combined errors due to radial basis approximation, quadrature rules, and quasi‐Newton and Newton iterations are also presented. This result can be extended to finite element or finite difference method combined with any iteration methods discussed in this work. The numerical example demonstrates a good agreement between numerical results and analytical predictions. The numerical results also show that although the convergence rate of order 1.62 of the quasi‐Newton iteration scheme is slightly slower than rate of order 2 in the Newton iteration scheme, the former is more stable and less sensitive to the initial guess. © 2007 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2008     

一种求多项式方程根的参数并行加速迭代法

推广了一种在无重根情况下,利用Newton类迭代法对同时求多项式零点的加速的迭代法.讨论了该方法的收敛性和收敛阶;最后给出数值算例表明:计算收敛阶和定理结论是一致的,且本算法具有较大的收敛范围.     

基于埃特金加速的改进Landweber迭代正则化方法

为克服Landweber迭代正则化方法在求解大规模不适定问题时收敛速度慢的不足,将埃特金加速技巧与不动点迭代相结合,构建了能快速收敛的改进Landweber迭代正则化方法.数值实验结果表明:改进的迭代正则化方法在稳定求解不适定问题时,能够快速地收敛至问题的最优解,较Landweber迭代正则化方法大大提高了收敛速度.     

An operator Newton method for the Stefan problem based on smoothing: A local perspective

The initial/Neumann boundary-value enthalpy formulation for the two-phase Stefan problem is regularized by smoothing. Known estimates predict a convergence rate of ^1/2, and this result is extended in this paper to include the case of a (nonzero) residual in the regularized problem. A modified Newton Kantorovich framework is established, whereby the exact solution of the regularized problem is replaced by one Newton iteration. It is shown that a consistent theory requires measure-theoretic hypotheses on the starting guess and the Newton iterate, otherwise residual decrease is not expected. The circle closes in one spatial dimension, where it is shown that the residual decrease of Newton's method correlates precisely with the ^1/2 convergence theory.     

A Neural Network-Based Policy Iteration Algorithm with Global $$H^2$$ -Superlinear Convergence for Stochastic Games on Domains

In this work, we propose a class of numerical schemes for solving semilinear Hamilton–Jacobi–Bellman–Isaacs (HJBI) boundary value problems which arise naturally from exit time problems of diffusion processes with controlled drift. We exploit policy iteration to reduce the semilinear problem into a sequence of linear Dirichlet problems, which are subsequently approximated by a multilayer feedforward neural network ansatz. We establish that the numerical solutions converge globally in the \(H^2\)-norm and further demonstrate that this convergence is superlinear, by interpreting the algorithm as an inexact Newton iteration for the HJBI equation. Moreover, we construct the optimal feedback controls from the numerical value functions and deduce convergence. The numerical schemes and convergence results are then extended to oblique derivative boundary conditions. Numerical experiments on the stochastic Zermelo navigation problem are presented to illustrate the theoretical results and to demonstrate the effectiveness of the method.

     

The use of approximate factorization in stiff ODE solvers

We consider implicit integration methods for the numerical solution of stiff initial-value problems. In applying such methods, the implicit relations are usually solved by Newton iteration. However, it often happens that in subintervals of the integration interval the problem is nonstiff or mildly stiff with respect to the stepsize. In these nonstiff subintervals, we do not need the (expensive) Newton iteration process. This motivated us to look for an iteration process that converges in mildly stiff situations and is less costly than Newton iteration. The process we have in mind uses modified Newton iteration as the outer iteration process and a linear solver for solving the linear Newton systems as an inner iteration process. This linear solver is based on an approximate factorization of the Newton system matrix by splitting this matrix into its lower and upper triangular part. The purpose of this paper is to combine fixed point iteration, approximate factorization iteration and Newton iteration into one iteration process for use in initial-value problems where the degree of stiffness is changing during the integration.     

The Modified Rayleigh Quotient Iteration

The Rayleigh Quotient Iteration (RQI) is a very popular method for computing eigenpairs of symmetric matrices. It is a special kind of inverse iteration method using the Rayleigh Quotient as shifts. Unfortunately, poor initial approximations may render RQI to slow convergence or even to divergence, In this paper we suggest two kinds of numbers each of which can be used instead of the Rayleigh Quotient as a shifts in the RQI. We call the iteration using the new shifts the Modified Rayleigh Quotient Iteration (MRQI). It has been proved that the MRQI always converges and its convergence rate is cubic.     

Iteration complexity analysis of block coordinate descent methods

In this paper, we provide a unified iteration complexity analysis for a family of general block coordinate descent methods, covering popular methods such as the block coordinate gradient descent and the block coordinate proximal gradient, under various different coordinate update rules. We unify these algorithms under the so-called block successive upper-bound minimization (BSUM) framework, and show that for a broad class of multi-block nonsmooth convex problems, all algorithms covered by the BSUM framework achieve a global sublinear iteration complexity of \(\mathcal{{O}}(1/r)\), where r is the iteration index. Moreover, for the case of block coordinate minimization where each block is minimized exactly, we establish the sublinear convergence rate of O(1/r) without per block strong convexity assumption.