2011年高考江苏卷第18题的推广与探源

真题再现 如图1,在平面直角坐标系xOy中,M、N分别是椭圆x2/4+y2/2=1的顶点,过坐标原点的直线交椭圆于P、A两点,其中点P在第一象限.过P作x轴的垂线,垂足为C,连接AC,并延长交椭圆于点B.设直线PA的斜率为k.     

对上海市04年中考数学压轴题的探索

上海市 0 4年中考数学试卷最后一道压轴题如下 :数学课上 ,老师出示图 1和下面框中的条件 .如图 1 ,在直角坐标平面内 ,0为坐标原点 ,A点坐标(1 ,0 ) ,点B在x轴上且在点A的右侧 ,AB =OA ,过点A和B作x轴的垂线 ,分别交二次函数y=x2 的图象于点C和D .直线OC交BD于点M ,直线CD交y轴于点H .记点C、D的横坐标分别为xC、xD,点H的纵坐标为yH.图 1同学发现两个结论 :①S△CMD∶S梯形ABMC=2∶3②数值相等关系 :xC·xD=- yH.(1 )请你验证结论①和②成立 ;(2 )请你研究 :如果将上述框中的条件“A点坐标为 (1 ,0 )”改为“A点坐标为 (t,…     

用变换研究曲线间的位置关系

如果令u=mx,v=nx,则这个线性变换将x-O-y平面上的点P(x,y)变为u-O-v平面上的点Q(u,v),将x-O-y平面上的曲线P(x,y)=0变为u-O-v平面上的曲线Q(u,v)=0.这样的线性变换具有如下性质.1)如果P(x,y)=0为直线,那么Q(u,v)=0仍为直线;2)如果P(x,y)=0为曲线,那么Q(u,v)=0仍为曲线,并且变换     

一道中考数学试题的得与失

1 试题 在平面直角坐标系中,矩形ABCD的边AB=2,AD=1,且AB、AD分别在x轴、y轴的正半轴上,点A与坐标原点重合.     

分数外微分在三维空间中的应用

首先介绍了分数微积分和分数微分形式。讨论了在原点处对曲线坐标的分数外微分变换,并且获得了从三维卡氏坐标到球面坐标和柱面坐标的两个分数微分变换。特别地,当v=m=1时,这两个分数微分变换约化的结果与通过外微积分获得的结果是一致的。     

关于Hans Lewv方程

Hans Lewy 方程 u_x iu_y 2(xi-y)u_i=0处处有唯一特征方向,但不存在特征曲面.因为平面 t=0上原点处有一特征方向,因此对以其为支柱的 Cauchy 问题,Kowalewsky 定理不适用.本文证明了平面 t=0上解析函数均可表为直和:(x,y)=(z,xz)(z,zx),其中 z=x iy,而(ξ,η),(ξ,η)都是解析函数;方程有初始值 u|_t=0=(z,xz)的唯一解析解,没有取始值 u|_t=0=(x,zx)的解析解,但对后者有奇性解.所有解都有明显的表达式.所有结论都是在原点附近的.但对空间任一点有相应的结果.     

平面闭折线的k号心及其性质

本文拟用解析法,建立平面闭折线的k号心的概念,并研究它的性质. 定义1 在平面闭折线以A1A2…An所在平面内任取一点P,以P为原点建立平面直角坐标系,设顶点Ai的坐标为(xi,yi)(i=1,2,…,n),对非零实数k,     

图形在平面直角坐标系中的位似变换

<正>图形在平面直角坐标系中的位似变换及变换前后对应点的坐标有何规律?请看下面两个例题:例1如图1,在平面直角坐标系中,△ABC的顶点坐标分别为A(2,-2),B(1,1),C(6,2).以原点O为位似中心,在y轴右侧,画出△A_1B_1C_1,使△A_1B_1C_1与△ABC的位似比为2:1,并写出点C_1的坐标.     

坐标轴的平移 参数方程 极坐标

1.设O＇点在原坐标系xOy中的坐标为(a,b),以O＇为原点平移坐标轴,建立新坐标系X＇0＇y＇,平面内任一点M在原坐标系中的坐标为(x,y),在新坐标系中的坐标为(x＇,y＇),推导出x＇、y＇与x、 y之间的关系。 2.平移坐标轴,分别回答下列问题: (1)点M(a, b),当原点移至何处才能使它的新坐标为(2a,-b)? (2)原点移到0＇(a,b)后,点A的新坐标为(-a,-b),点A的原坐标是什么? (3)原点0＇(0,0)移到0(2,-1)后,原坐标系x＇0＇y＇变成新坐标系x0y、曲线方程为x~2/9+y~2/4=1.此曲线在原坐标系中的方程是什么? (4)曲线x~2+xy-2y~2+x+11y-12=0在原点移到(-1,2)点后,新方程是什么?曲线的形状是什么?     

对一道高考题的探究

北京2011年高考14题:曲线C是平面内与两个定点F(1-1,0)和F(21,0)的距离的积等于常数a(2a>1)的点的轨迹.给出下列三个结论:①曲线C过坐标原点;②曲线C关于坐标原点对称;③若点P在曲线C上,则     

解线方程组的一种新的方法

这是界于 Gauss 消去法与 Householder 法之间的一个方法,综合了它们的一些特点,并克服了一些缺点.在每一步,需像高斯法那样选主元,但却不必作行或列的交换.一般说来,变换阵 S 不是 Hermite,但是,像 Householder 法,它满足 S~(-1)=s,并保持想消去的向量模不变.计算量接近于 Gauss 法而比 Householder 法少.比两者均更能保存稀疏性.     

On the simultaneous tridiagonalization of two symmetric matrices

We discuss congruence transformations aimed at simultaneously reducing a pair of symmetric matrices to tridiagonal–tridiagonal form under the very mild assumption that the matrix pencil is regular. We outline the general principles and propose a unified framework for the problem. This allows us to gain new insights, leading to an economical approach that only uses Gauss transformations and orthogonal Householder transformations. Numerical experiments show that the approach is numerically robust and competitive.     

Further results on linear interval equations

As a continuation of my paper “New techniques for the analysis of linear interval equations” [Linear Algebra Appl. 58:273–325 (1984)], the interrelation between interval Gauss elimination and interval iteration is investigated. Main results are a new existence theorem for interval Gauss elimination (in the guise of a perturbation theorem), a convergence and comparison theorem for a general family of interval iteration schemes, and a new method for the calculation of the hull of the solution set of linear interval equations with inverse positive coefficient matrix.     

关于k-广义酉矩阵

本文研究了k-广义酉矩阵的性质及其与酉矩阵、辛矩阵、Householder矩阵之间的联系,取得了许多新的结果,推广了酉矩阵及Householder矩阵的相应结果,特别将正交矩阵的广义Cayley分解推广到了广义酉矩阵上;并将各类酉矩阵及辛矩阵统一了起来.     

Implicit difference approximation for the time fractional heat equation with the nonlocal condition

In this work, a method for solving inhomogeneous nonlocal fractional heat equations is proposed. The method is based on the modified Gauss elimination method. It is proved by using matrix stability approach that the method is unconditionally stable. Numerical results are provided to illustrate the accuracy and efficiency of the proposed method.     

A block Cholesky-LU-based QR factorization for rectangular matrices

The Householder method provides a stable algorithm to compute the full QR factorization of a general matrix. The standard version of the algorithm uses a sequence of orthogonal reflections to transform the matrix into upper triangular form column by column. In order to exploit (level 3 BLAS or structured matrix) computational advantages for block-partitioned algorithms, we develop a block algorithm for the QR factorization. It is based on a well-known block version of the Householder method which recursively divides a matrix columnwise into two smaller matrices. However, instead of continuing the recursion down to single matrix columns, we introduce a novel way to compute the QR factors in implicit Householder representation for a larger block of several matrix columns, that is, we start the recursion at a block level instead of a single column. Numerical experiments illustrate to what extent the novel approach trades some of the stability of Householder's method for the computational efficiency of block methods.     

An explicit solution for the cubic spline interpolation for functions of a single variable

An algorithm for computing the cubic spline interpolation coefficients without solving the matrix equation involved is presented in this paper. It requires only O(n) multiplication or division operations for computing the inverse of the matrix compared to O(n²) or larger number of operations in the Gauss elimination method.     

Symmetric Exchange of Variables and Stepwise Regression Methods

A step in Gauss-Jordan elimination may be seen as exchanging a dependent variable for an independent one. If at the same time one of them changes sign a symmetric system matrix remains symmetric with the pivot reversing sign [2]. This variant can efficiently be exploited inverting symmetric positive definite matrices but also for obtaining a most complete set of relevant statistical quantities in stepwise regression via normal equations proposed by Efroymson 1960 and improved by Breaux 1968. We compare this method with numerically more stable ones applying orthogonal transformations, in particular that of Eldéen 1972 using Householder transformations and that of Gragg-LeVeque-Trangenstein 1979 using modified Gram-Schmidt. (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Elimination with Noninvertible Pivots

A method for carrying out the Gauss elimination solution of linear systems is presented. The novelty arises from the fact that the pivot matrices are not required to be invertible, so that, for example, a scalar pivot may be zero, and a matrix pivot may be rectangular or singular. The need to execute the elimination algorithm in such circumstances arose in connection with a finite element solution of the Navier-Stokes equations in velocity-pressure variables. This application is briefly discussed, as is a method for the implementation of the algorithm.     

Equivalence between modified symplectic Gram-Schmidt and Householder SR algorithms

The SR factorization for a given matrix A is a QR-like factorization A=SR, where the matrix S is symplectic and R is J-upper triangular. This factorization is fundamental for some important structure-preserving methods in linear algebra and is usually implemented via the symplectic Gram-Schmidt algorithm (SGS). There exist two versions of SGS, the classical (CSGS) and the modified (MSGS). Both are equivalent in exact arithmetic, but have very different numerical behaviors. The MSGS is more stable. Recently, the symplectic Householder SR algorithm has been introduced, for computing efficiently the SR factorization. In this paper, we show two new and important results. The first is that the SR factorization of a matrix A via the MSGS is mathematically equivalent to the SR factorization via Householder SR algorithm of an embedded matrix. The later is obtained from A by adding two blocks of zeros in the top of the first half and in the top of the second half of the matrix A. The second result is that MSGS is also numerically equivalent to Householder SR algorithm applied to the mentioned embedded matrix.