用二分法求解一元实系数多项式方程的全部实根

运用二分法,结合实系数多项式零点的界定理及Sturm定理,给出了一个求解一元实系数多项式方程全部实根的实用数值方法.     

Laplace积分的一种新求法

Laplace积分在复变函数、数学分析、Fourier分析中有重要的应用,其求解已有复变函数方法和实方法.在实方法中要用到∫∞0sinxxdx=π2,本文给出另外一种实方法,不需要利用这个积分.     

利用二分法思想巧解零点存在性问题

二分法可用于求方程的近似解,在处理一类函数零点存在性问题时,利用二分法也可使问题快速获解,达到事半功倍的效果.例1已知函数f(x)=ax~3+bx~2+(b-a)x(a,b是均不为零的常数),其导函数为f′(x),求证:函数y=f′(x)在(-1,0)内至少存在一个零点.     

复变函数中值定理的改进和推广

改进了复变函数柯西中值定理的表达形式,将实函数加权型中值定理推广到复变函数上,同时推广复变函数积分中值定理到一般光滑曲线上.     

函数零点的教学启示

2012年11月,我区青年教师教学评优课,几位教师都选择上了高一第一学期3.4函数的基本性质——函数的零点和二分法,部分教师的教学值得反思.一、教学片段教师甲问:"函数y=x-3的零点是什么?"一个学生回答:"这个函数的零点是(3,0)."教师说:"请你再考虑一下你的回答是否准确."这个学生有些茫然,这时教师让学生读一遍书中对函数零点的     

根据函数零点个数确定参数的范围

<正>函数的零点与参数取值范围问题在各类考试中频频出现.为方便同学们应对,我们共同来探讨:已知函数零点个数确定参数范围的求解方法.例1已知函数f(x)=■有3个不同的零点,则实数a的取值范围是.分析因f(x)有三个不同的零点,所以当x≤0时有一个零点,当x>0时有两个不同的零点,进而建立不等式组求解.     

函数的应用

1.本单元重、难点分析本单元的重点:利用"二分法"求方程的近似解,了解函数的零点与方程的根之间的联系;掌握函数零点(即方程的根)的存在性定理,学会结合函数的图象判断方程解的个数及解的范围;能够应用函数模型解决简单的实际问题.本单元的难点:利用"二分法"求方程的近似     

函数零点近似值的探求策略

新课改使学生接触到很多实际问题,而问题的解决往往求助于解方程,对于无公式且不能因式分解的方程,比如超越方程,学生感到束手无策.方程求解也即求函数零点,教材介绍了二分法.为了扩大学生的视野,帮助学生更好地解决实际问题,本文介绍几种零点近似值的探求方法.一、二分法例1求函数f(x)=lnx 2x-6在区间(2,3)内的零点(精确度为0.01).解:设函数f(x)在(2,3)内的零点为x0,用计算器计算得:f(2)<0,f(3)>0x0∈(2,3);f(2.5)<0,f(3)>0x0∈(2.5,3);f(2.5)<0,f(2.75)>0x0∈(2.5,2.75);f(2.5)<0,f(2.5625)>0x0∈(2.5,2.5625);f(2.53125)<0,f(2.5625)>0x0…     

函数零点问题中参数取值的求解

函数的零点主要涉及三个方面的问题：连续函数零点的存在性;连续函数零点个数的判定;求连续函数零点的近似解（二分法）．在以上三个问题的考查中,常常涉及到参数取值范围的求解,主要从问题的逆向方面进行考察．这类问题是目前新课标下高考的重点、难点、热点,如何引导学生解决这类问题？笔者认为应从两方面入手．     

函数的应用

1.本单元重、难点分析本单元的重点:利用"二分法"求方程的近似解,了解函数的零点与方程的根之间的联系;掌握函数零点的存在性定理,能够结合函数的图象判断方程解的个数及解的范围.     

On Computing Zeros of a Bivariate Bernstein Polynomial

1.IntroductionIn[6]and[4],theproblemoffindingtheintersectionofacubicB6zierpatchandaplanewasconsidered.[6]consideredarectangular,and[41atriangularpatch.SincetheBernsteinoperatorB.:f-Bn(f)preserveslinearfunctions,theproblemwassimplifiedtothecomputationofzerosofabivariateBernsteinpolynomialB.(f).BothpaPersproducedsimpleandefficientcomputationalalgorithms.Itisbaseduponthefollowingidea:determinethepointswhereinsidethesupportthetopologyofzerosofB.(f)changes.Thiswasdonebyrestrictingthebivariatepo…     

Weierstrass formula and zero-finding methods

Summary. Classical Weierstrass' formula [29] has been often the subject of investigation of many authors. In this paper we give some further applications of this formula for finding the zeros of polynomials and analytic functions. We are concerned with the problems of localization of polynomial zeros and the construction of iterative methods for the simultaneous approximation and inclusion of these zeros. Conditions for the safe convergence of Weierstrass' method, depending only on initial approximations, are given. In particular, we study polynomials with interval coefficients. Using an interval version of Weierstrass' method enclosures in the form of disks for the complex-valued set containing all zeros of a polynomial with varying coefficients are obtained. We also present Weierstrass-like algorithm for approximating, simultaneously, all zeros of a class of analytic functions in a given closed region. To demonstrate the proposed algorithms, three numerical examples are included. Received September 13, 1993     

An Algorithm for Finding All Isolated Zeros of Polynomial Systems

Finding all zeros of polynomial systems is very interesting and it is also useul for many applied science problems.In this paper,based on Wu‘s method,we give an algorithm to find all isolated zeros of polynomial systems (or polynomial equations).By solving Lorenz equations,it is shown that our algo-rithm is efficient and powerful.     

Pseudozeros of polynomials and pseudospectra of companion matrices

Summary. It is well known that the zeros of a polynomial are equal to the eigenvalues of the associated companion matrix . In this paper we take a geometric view of the conditioning of these two problems and of the stability of algorithms for polynomial zerofinding. The is the set of zeros of all polynomials obtained by coefficientwise perturbations of of size ; this is a subset of the complex plane considered earlier by Mosier, and is bounded by a certain generalized lemniscate. The is another subset of defined as the set of eigenvalues of matrices with ; it is bounded by a level curve of the resolvent of $A$. We find that if $A$ is first balanced in the usual EISPACK sense, then and are usually quite close to one another. It follows that the Matlab ROOTS algorithm of balancing the companion matrix, then computing its eigenvalues, is a stable algorithm for polynomial zerofinding. Experimental comparisons with the Jenkins-Traub (IMSL) and Madsen-Reid (Harwell) Fortran codes confirm that these three algorithms have roughly similar stability properties. Received June 15, 1993     

Approximating the zeros of analytic functions by the exclusion algorithm

We give a practical version of the exclusion algorithm for localizing the zeros of an analytic function and in particular of a polynomial in a compact of . We extend the real exclusion algorithm to a Jordan curve and give a method which excludes discs without any zero. The result of this algorithm is a set of discs arbitrarily small which contains the zeros of the analytic function.     

Point estimation and some applications to iterative methods

We consider one of the crucial problems in solving polynomial equations concerning the construction of such initial conditions which provide a safe convergence of simultaneous zero-finding methods. In the first part we deal with the localization of polynomial zeros using disks in the complex plane. These disks are used for the construction of initial inclusion disks which, under suitable conditions, provide the convergence of the Gargantini-Henrici interval method. They also play a key role in the convergence analysis of the fourth order Ehrlich-Aberth method with Newton's correction for the simultaneous approximation of all zeros of a polynomial. For this method we state the initial condition which enables the safe convergence. The initial condition is computationally verifiable since it depends only on initial approximations, which is of practical importance.     

On the convergence of Wang-Zheng's method

The choice of initial conditions ensuring safe convergence of the implemented iterative method is one of the most important problems in solving polynomial equations. These conditions should depend only on the coefficients of a given polynomial P and initial approximations to the zeros of P. In this paper we state initial conditions with the described properties for the Wang-Zheng method for the simultaneous approximation of all zeros of P. The safe convergence and the fourth-order convergence of this method are proved.     

Accurate computation of the zeros of the generalized Bessel polynomials

Summary. A general method for approximating polynomial solutions of second-order linear homogeneous differential equations with polynomial coefficients is applied to the case of the families of differential equations defining the generalized Bessel polynomials, and an algorithm is derived for simultaneously finding their zeros. Then a comparison with several alternative algorithms is carried out. It shows that the computational problem of approximating the zeros of the generalized Bessel polynomials is not an easy matter at all and that the only algorithm able to give an accurate solution seems to be the one presented in this paper. Received July 25, 1997 / Revised version received May 19, 1999 / Published online June 8, 2000     

Graeffe's, Chebyshev-like, and Cardinal's Processes for Splitting a Polynomial into Factors

Numerical splitting of a real or complex univariate polynomial into factors is the basic step of the divide-and-conquer algorithms for approximating complex polynomial zeros. Such algorithms are optimal (up to polylogarithmic factors) and are quite promising for practical computations. In this paper, we develop some new techniques, which enable us to improve numerical analysis, performance, and computational cost bounds of the known splitting algorithms. In particular, we study a Chebyshev-like modification of Graeffe's lifting iteration (which is a basic block of the splitting algorithms, as well as of several other known algorithms for approximating polynomial zeros), analyze its numerical performance, compare it with Graeffe's, prove some results on numerical stability of both lifting processes (that is, Graeffe's and Chebyshev-like), study their incorporation into polynomial root-finding algorithms, and propose some improvements of Cardinal's recent effective technique for numerical splitting of a polynomial into factors. Our improvement relies, in particular, on a modification of the matrix sign iteration, based on the analysis of some conformal mappings of the complex plane and of techniques of recursive lifting/recursive descending. The latter analysis reveals some otherwise hidden correlations among Graeffe's, Chebyshev-like, and Cardinal's iterative processes, and we exploit these correlations in order to arrive at our improvement of Cardinal's algorithm. Our work may also be of some independent interest for the study of applications of conformal maps of the complex plane to polynomial root-finding and of numerical properties of the fundamental techniques for polynomial root-finding such as Graeffe's and Chebyshev-like iterations.     

A modified fast Fourier transform for polynomial evaluation and the jenkins-Traub algorithm

Summary We present an algorithm to evaluate a polynomial at uniformly spaced points on a circle in the complex plane. As an application of this algorithm, a procedure is developed which gives a starting point for the Jenkins-Traub algorithm [5, 6] to compute the zeros of a polynomial.This work was supported by National Science Foundation grants DMS-8401758 and DMS-8520926 and Air Force Office of Scientific Research grant AFOSR-ISSA-860091