改进的两步高阶Euler-Chebyshev方法

给出非线性方程求根的Euler-Chebyshev方法的改进方法,证明了方法的收敛性,它们七次和九次收敛到单根.给出数值试验,且与牛顿法及其它较高阶的方程求根方法做了比较.结果表明方法具有很好的优越性,它丰富了非线性方程求根的方法,在理论上和应用上都有一定的价值.     

一类新的求解非线性方程的七阶方法

利用权函数法给出了一类求解非线性方程单根的七阶收敛的方法.每步迭代需要计算三个函数值和一个导数值,因此方法的效率指数为1.627.数值试验给出了该方法与牛顿法及同类方法的比较,显示了该方法的优越性.最后指出Kou等人给出的七阶方法是方法的特例.     

几类数列不等式的证明方法的改进

文[1]给出三种类型的数列不等式的证明方法,笔者读后深受启发,但发现其方法有很大的局限性.本文提出它们的改进建议,供大家参考.类型Ⅰa1+a2+a3+…+an相似文献   

二维分数阶Volterra积分方程的修正block-by-block方法

基于经典block-by-block方法的思想,构造了二维分数阶Volterra积分方程的一个修正block-by-block数值求解格式.该方法的优点在于只需求解u(x1,y),u(x2,y),u(x,y_1)和u(x,y_2),其他未知量均不需要耦合求解.数值算例表明该格式具有较好的逼近性.     

求解广义KdV方程的线性化局部Crank-Nicolson方法

针对广义KdV方程,构造了基于局部Crank-Nicolson方法的一种线性化差分格式,格式是一个可以显式求解的隐格式.数值试验表明,格式能够较好地求解广义KdV方程.     

求解第一类积分方程的正则化—小波方法及其数值试验

1 方法的描述 第一类(Fredholm)积分方程是指形如 (1.1)的积分方程,其中核k(x,y)和右端函数f(x)给定,u(x)是未知函数.许多物理、化学、力学和工程应用问题都能导致第一类积分方程.求解第一类积分方程的一个本质性困难是方程的不适定性,即解的存在性、唯一性和稳定性遭到破坏.常用的数值方法有奇异值分解(SVD)方法、Tikhonov正则化方法、投影方法、正则化-样条方法、再生核方法等.本文提出一种新的正则化-小波方法,在第一类积分方程有多个解时,可以求出具有最小范数的数值解;如果原积分方程有唯一解,则所得的数值解收敛于准确解.数值试验表明,该方法是可行的. 我们在L~2[a,b]中考虑第一类(Fredholm)积分方程,即假设方程(1.1)中积分算子K∈L~2([a,b]×[a,b])及右端f(x)∈L~2[a,b]给定.为保证数值求解算法的稳定性,我们先用正则化方法处理该方程,将不适定问题化为泛函极值问题来求解,然后利用多重正交样条小波基构造求解格式.由于我们给出了直接计算低阶的多重正交样条小波基函数的一般公式,使得解法可以在计算机迅速实现.     

一类四阶牛顿变形方法

给出非线性方程求根的一类四阶方法,也是牛顿法的变形方法.证明了方法收敛性,它们至少四次收敛到单根,线性收敛到重根.文末给出数值试验,且与牛顿法及其它牛顿变形法做了比较.结果表明方法具有很好的优越性,它丰富了非线性方程求根的方法,在理论上和应用上都有一定的价值.     

非线性RLW方程的特征数值方法

如所周知,在孤立子理论的研究及非线性波的物理问题中,RLW(Regularised LongWave)方程占据了相当重要的地位,在非常广泛的领域里得到了应用.因此,对该方程的研究已受到人们的很大重视,有许多专家、学者在这方面已做了不少工作.例如文献     

刚性动力学方程一种新的高精度单步隐式法的二叉树并行算法

本文面向ＳＩＭＤ（ＳｉｎｇｌｅＩｎｓｔｒｕｃｔｉｏｎＳｔｒｅａｍ，ＭｕｌｔｉｐｌｅＤａｔａＳｔｒｅａｍ）型机设计同步并行算法，对刚性动力学方程新算法提出了二层并行计算路径．同时，还讨论了并行计算的有关算法特性．算法分析表明，本文的算法是可行和高效的．     

一类分数阶Langevin方程block-by-block算法的数值分析

分数阶Langevin方程有重要的科学意义和工程应用价值,基于经典block-by-block算法,求解了一类含有Caputo导数的分数阶Langevin方程的数值解.Block-by-block算法通过引入二次Lagrange基函数插值,构造出逐块收敛的非线性方程组,通过在每一块耦合求得分数阶Langevin方程的数值解.在0<α<1条件下,应用随机Taylor展开证明block-by-block算法是3+α阶收敛的,数值试验表明在不同α和时间步长h取值下,block-by-block算法具有稳定性和收敛性,克服了现有方法求解分数阶Langevin方程速度慢精度低的缺点,表明block-by-block算法求解分数阶Langevin方程是高效的.     

On Convergence Domains of Newton's and Modified Newton Methods

ABSTRACT

We analyze convergence domains of Newton's and the modified Newton methods for solving operator equations in Banach spaces assuming first that the operator in question is ω-smooth in a ball centered at the starting point. It is shown that the gap between convergence domains of these two methods cannot be closed under ω-smoothness. Its exact size for Hölder smooth operators is computed. Then we proceed to investigate their convergence domains under regular smoothness. As our analysis reveals, both domains are the same and wider than their counterparts in the previous case.     

Convergence Analysis of Modified Hybrid Steepest-Descent Methods with Variable Parameters for Variational Inequalities

Assume that F is a nonlinear operator on a real Hilbert space H which is η-strongly monotone and κ-Lipschitzian on a nonempty closed convex subset C of H. Assume also that C is the intersection of the fixed-point sets of a finite number of nonexpansive mappings on H. We construct an iterative algorithm with variable parameters which generates a sequence {x _n } from an arbitrary initial point x ₀ ∊ H. The sequence {x _n } is shown to converge in norm to the unique solution u ^∗ of the variational inequality The authors thank the referees for helpful comments and suggestions His research was partially supported by the Teaching and Research Award Fund for Outstanding Young Teachers in Higher Education Institutions of MOE, China and the Dawn Program Foundation in Shanghai His research was partially supported by grant from the National Science Council of Taiwan His research was partially supported by grant from the National Science Council of Taiwan     

Global Convergence Properties of Nonlinear Conjugate Gradient Methods with Modified Secant Condition

Conjugate gradient methods are appealing for large scale nonlinear optimization problems. Recently, expecting the fast convergence of the methods, Dai and Liao (2001) used secant condition of quasi-Newton methods. In this paper, we make use of modified secant condition given by Zhang et al. (1999) and Zhang and Xu (2001) and propose a new conjugate gradient method following to Dai and Liao (2001). It is new features that this method takes both available gradient and function value information and achieves a high-order accuracy in approximating the second-order curvature of the objective function. The method is shown to be globally convergent under some assumptions. Numerical results are reported.     

Convergence of Conjugate Gradient Methods with a Closed-Form Stepsize Formula

Conjugate gradient methods are efficient methods for minimizing differentiable objective functions in large dimension spaces. However, converging line search strategies are usually not easy to choose, nor to implement. Sun and colleagues (Ann. Oper. Res. 103:161–173, 2001; J. Comput. Appl. Math. 146:37–45, 2002) introduced a simple stepsize formula. However, the associated convergence domain happens to be overrestrictive, since it precludes the optimal stepsize in the convex quadratic case. Here, we identify this stepsize formula with one iteration of the Weiszfeld algorithm in the scalar case. More generally, we propose to make use of a finite number of iterates of such an algorithm to compute the stepsize. In this framework, we establish a new convergence domain, that incorporates the optimal stepsize in the convex quadratic case. The authors thank the associate editor and the reviewer for helpful comments and suggestions. C. Labat is now in postdoctoral position, Johns Hopkins University, Baltimore, MD, United States.     

Some Convergence Properties of Descent Methods

In this paper, we discuss the convergence properties of a class of descent algorithms for minimizing a continuously differentiable function f on R ⁿ without assuming that the sequence { x _k } of iterates is bounded. Under mild conditions, we prove that the limit infimum of is zero and that false convergence does not occur when f is convex. Furthermore, we discuss the convergence rate of { } and { f(x _k )} when { x _k } is unbounded and { f(x _k )} is bounded.     

牛顿迭代法与几种改进格式的效率指数

研究牛顿迭代、牛顿弦截法以及它们的六种改进格式的计算效率,计算了它们的效率指数,得到牛顿迭代、改进牛顿法、弦截法和改进弦截法(即所谓牛顿迭代的P.C格式)、二次插值迭代格式、推广的牛顿迭代法、调和平均牛顿法和中点牛顿法的效率指数分别为0.347/n、0.3662/n、0.4812/n、0.4812/n、0.347/n、0.3662/n、0.3662/n、0.3662/n.我们的结果显示,利用抛物插值多项式推出的迭代格式和改进弦截法并没有真正提高迭代的计算效率.此外,我们还证明了改进弦截法与牛顿弦截法等价,并利用这一结论给出了改进弦截法收敛阶为2.618的一个简化证明.     

Convergence of Runge-Kutta Methods for Delay Differential Equations

This paper deals with the adaptation of Runge—Kutta methods to the numerical solution of nonstiff initial value problems for delay differential equations. We consider the interpolation procedure that was proposed in In 't Hout [8], and prove the new and positive result that for any given Runge—Kutta method its adaptation to delay differential equations by means of this interpolation procedure has an order of convergence equal to min {p,q}, where p denotes the order of consistency of the Runge—Kutta method and q is the number of support points of the interpolation procedure.This revised version was published online in October 2005 with corrections to the Cover Date.     

一类新的共轭梯度法的全局收敛性

本文提出了一类与HS方法相关的新的共轭梯度法.在强Wolfe线搜索的条件下,该方法能够保证搜索方向的充分下降性,并且在不需要假设目标函数为凸的情况下,证明了该方法的全局收敛性.同时,给出了这类新共轭梯度法的一种特殊形式,通过调整参数ρ,验证了它对给定测试函数的有效性.