关于拉格朗日中值定理的证明

一般高等数学教材上,大都是用罗尔定理证明拉朗日中值定理,直接给出一个辅助函数,把拉格朗日定理的证明归结为用罗尔定理,证明的关键是给出一个辅助函数.怎样构作这一辅助函数呢?我们来看:     

关于微分中值定理证明的教学

拉格朗日中值定理是微分学的理论基础 ,在介绍应用导数研究函数变化的性态之前 ,全面准确地理解中值定理的条件和结论及它的证明 ,对学好微分学起着至关重要的作用 .拉格朗日中值定理表述为 :如果函数 ｆ(ｘ)满足下列条件1 )在闭区间 [ａ ,ｂ]上连续 ,2 )在开区间 (ａ ,ｂ)内可     

利用微分学证明不等式的几种典型证法

通过对几道不等式证明题的分析,文中总结了利用微分学证明不等式的常用方法,有助于学生加深对微分学知识的理解,激发学生的学习兴趣,也有利于学生发散思维培养及提高解决问题的能力.     

罗尔定理证明一类存在性问题

提出罗尔定理证明一类存在性问题的方法,采用拉格朗日中值定理或柯西中值定理来证明这类问题往往需要构造精巧的辅助函数,我们还指出了这种方法的一般性.     

导数在不等式证明中的应用探究

不等式常见的证明方法有构造法、比较法、反证法等,但是,一些不等式利用这些方法证明比较困难,而利用导数证明不等式不但能精简证明流程,而且能确保证明结果的准确性.本文中主要分析了利用函数凹凸性、导数定义、拉格朗日中值定理证明不等式的详细方式,且给出了多种方式的适用范畴,结合实际情况整理了使用多种方式开展不等式证明的主要观点.     

积分中值定理的改进

目前高等数学教材所普遍采用的积分中值定理的证明方法,只能将积分中值点的范围限定在闭区间上．但利用拉格朗日中值定理证明积分中值定理,可以将积分中值点的范围缩小到开区间内．通过实例可以说明。改进后的积分中值定理能够解决一些用原来的积分中值定理无法解决的问题．     

不等式证明方法

本文结合典型例题对不等式的证明方法进行了归纳总结.     

关于拉格朗日中值定理与中间值的唯一性

拉格朗日中值定理是: 如果(i)函数f(x)在闭区间[a,b]连续,(ii)f(x)在开区间(a,b)可微,那么在(a,b)内至少存在一点ξ,使得     

浅谈不等式证明的几种特殊方法

不等式的证明在数学中是比较常见的题型 ,但有些不等式用常见的方法 (如比较法、分析法和综合法等 )很难证出来 ,或者根本证不出来 .这里介绍几种特殊的证法 ,解决一些不等式的证明问题 .1　数学归纳法数学归纳法是数学中解决证明题很重要的一种方法 ,在不等式证明中也不例外 ,对于与自然数有关的不等式都可以考虑这种方法 .例 1　证明 :|ｓｉｎｎｘ|≤ｎ|ｓｉｎｘ|对任何自然数都成立 .证　 1 )当ｎ =1时 ,不等式显然成立 ;2 )假设ｎ =ｋ时 ,不等式成立 ,即　 |ｓｉｎｋｘ|≤ｋ|ｓｉｎｘ|成立 .当ｎ =ｋ +1时 ,　 |ｓｉｎ(ｋ +1 )ｘ|=|ｓｉ…     

改进的L’Hospital法则的证明及其应用

对改进的L’Hospital法则提供了一个证明,所用的知识主要是函数极限的定义和柯西中值定理,而后又提供四个例子,作为改进的L’Hospital法则的应用.     

An iterative method for minimizing a convex nonsmooth function on a convex smooth surface

An iterative algorithm is proposed for the constrained minimization of a convex nonsmooth function on a set given as a convex smooth surface. The convergence of the algorithm in the sense of necessary conditions for a local minimum is proved.     

A modified combined relaxation method for non-linear convex variational inequalities

We consider a class of non-linear problems which is intermediate between equilibrium and variational inequality ones and has many applications. Unlike the usual variational inequality it involves two non-linear mappings, which need not be differentiable. We propose a class of iterative methods for this problem, which converge to a solution under weakened monotonicity type assumptions. This method is simpler essentially in comparison with those for the corresponding non-linear equilibrium problems.     

Multicomponent proof-theoretic method for proving interpolation properties

Proof-theoretic method has been successfully used almost from the inception of interpolation properties to provide efficient constructive proofs thereof. Until recently, the method was limited to sequent calculi (and their notational variants), despite the richness of generalizations of sequent structures developed in structural proof theory in the meantime. In this paper, we provide a systematic and uniform account of the recent extension of this proof-theoretic method to hypersequents, nested sequents, and labelled sequents for normal modal logic. The method is presented in terms and notation easily adaptable to other similar formalisms, and interpolant transformations are stated for typical rule types rather than for individual rules.     

On the degree theory for general mappings of monotone type

Degree theory has been developed as a tool for checking the solution existence of nonlinear equations. In his classic paper published in 1983, Browder developed a degree theory for mappings of monotone type f+T, where f is a mapping of class ₊(S) from a bounded open set Ω in a reflexive Banach space X into its dual X^∗, and T is a maximal monotone mapping from X into X^∗. This breakthrough paved the way for many applications of degree theoretic techniques to several large classes of nonlinear partial differential equations. In this paper we continue to develop the results of Browder on the degree theory for mappings of monotone type f+T. By enlarging the class of maximal monotone mappings and pseudo-monotone homotopies we obtain some new results of the degree theory for such mappings.     

Penalty function theory for general convex programming problems

A class of penalty functions for solving convex programming problems with general constraint sets is considered. Convergence theorems for penalty methods are established by utilizing the concept of infimal convergence of a sequence of functions. It is shown that most existing penalty functions are included in our class of penalty functions.     

Population monotonic solutions on convex games

The Dutta-Ray solution and the Shapley value are two well-known examples of population-monotonic solutions on the domain of convex games. We provide a new formula for the Dutta-Ray solution from which population-monotonicity immediately follows. Then we define a new family of population-monotonic solutions, which we refer to as “sequential Dutta-Ray solutions.” We also show that it is possible to construct several symmetric and population-monotonic solutions by using the solutions in this family. Received September 1998/Revised version: December 1999     

A method for minimizing the sum of a convex function and a continuously differentiable function

This paper presents a method for minimizing the sum of a possibly nonsmooth convex function and a continuously differentiable function. As in the convex case developed by the author, the algorithm is a descent method which generates successive search directions by solving quadratic programming subproblems. An inexact line search ensures global convergence of the method to stationary points.     

A minimization method for the sum of a convex function and a continuously differentiable function

This paper presents a method for finding the minimum for a class of nonconvex and nondifferentiable functions consisting of the sum of a convex function and a continuously differentiable function. The algorithm is a descent method which generates successive search directions by solving successive convex subproblems. The algorithm is shown to converge to a critical point.The authors wish to express their appreciation to the referees for their careful review and helpful comments.     

A symbolic finite-state approach for automated proving of theorems in combinatorial game theory

We develop a finite-state automata approach, implemented in a Maple package Toads and Frogs available from our websites, for conjecturing, and then rigorously proving, values for large families of positions in Richard Guy's combinatorial game ‘Toads and Frogs’. In particular, we prove a conjecture of Jeff Erickson.