直线与二次曲线相关的一个命题

命题 设直线l：f（x,y）=0与二次曲线g（x,y）=0交于不同的两点A（x1,y1）,B（x2,y2）,由{f（x,y）=0 g（x,y）=0,分别消去y,x得v（x）=0,v（y）=0（使u（x）,v（y）的二次项系数相等）,则以线段AB为走私的圆的方程为：u（x）＋v（y）=0.     

RESEARCH ANNOUNCEMENTS——Dynamical Behavior for the Three-dimensional Generalized Hasegawa-Mima Equations

We consider the following generalized three-dimensional （3-D） dissipative Hasegawa-Mima equations： △ut - ut ＋ {u, △u} ＋ knuy - vz ＋ α△（u - △u） ＋ f（x, y, z） = 0, （1） vt ＋ {u, v} ＋ uz ＋ γv - β△v = g（x, y, z） （2） with initial datum v｜t=0=u0（x,y,z）,v｜t=0=v0（x,y,z）,（x,y,z）∈Ω∈R^3 （3）.     

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．     

数形结合解复合函数的零点个数的常见解法

本文通过利用函数图像的方法研究复合函数y=g（f（x））的零点问题,即复合函数方程g（f（x））=0的根,令u=f（x）（内层方程）,这样g（f（x））=0就转化成g（u）=0.当外层方程g（u）=0容易求解时,可以先解方程g（u）=0,再解内层方程u=f（x）,这样方程的总个数即为复合函数y=g（f（x））的零点个数.     

二元全微分的原函数的求法

设du＝P（x，y）dx＋Q（x，y）dy，称P（x，y）dx＋Q（x，y）dy为函数u（x，y）的全微分，u（x，y）为P（x，y）dx＋Q（x，x）dx的一个原函数。若已知P（x，y）dx＋Q（x，y）dy为某一函数的全微分，如何求u（x，y）呢？今举例说明如下：例求全微分（x＋y）dx＋（x—y）dy的一个原函数。首先注意，在本题中P（x，y）一一函数的全微分，即存在原函数u（x，y），使有du（x，y）＝（x＋y）dx＋（x－y）dy．解法一，简单路径法可选取或为积分路径，即这里取则解法二，微分方程法由前式解得。（x，s）一专x’＋xv＋。s），其中。，）为y的一个…     

一道微分方程题目的多种解法

同济大学数学教研室编高等数学 (第四版 )下册 P40 7有一题目 :求方程的通解。学生普遍感到有些困难。下面给出几种解法。y′+x =x2 +y ( 1 )　　解　方法一　令 x2 +y-x=u,则 yx2 +y+x=u,y=u( x2 +y+x) ,两边对 x求导 ,得 dydx= ( x2 +y+x) dudx+u(2 x+dydx2 x2 +y+1 )。代入 ( 1 ) ,得 dudx+u2 ( u+x) =0 ,或udx +2 ( u +x) du =0 ( 2 )易见有积分因子 μ=u,引用之 ,解得 2 u3 +3 xu2 =c1。换回原变量 ,得 ( 1 )的通解为 ( x2 +y) 3 =x3 +32 xy+c.其中 c=c12 为任意常数。方法二　令 u=x2 +yx ,则 x2 +y =ux,两边对 x求导 ,得2 x+dydx2 x…     

ON DIRICHLET PROBLEMS FOR SECOND ORDER QUASILINEAR DEGENERATE ELLIPTIC EQUATIONS

The purpose of this paper is to study the existence of the classical solutions of some Dirichlet problems for quasilinear elliptic equations $$\[{a_{11}}(x,y,u)\frac{{{\partial ^2}u}}{{\partial {x^2}}} + 2{a_{12}}(x,y,u)\frac{{{\partial ^2}u}}{{\partial x\partial y}} + {a_{22}}(x,y,u)\frac{{{\partial ^2}u}}{{\partial {y^2}}} + f(x,y,u,\frac{{\partial u}}{{\partial x}},\frac{{\partial u}}{{\partial y}}) = 0\]$$ Where $\[{a_{ij}}(x,y,u)(i,j = 1,2)\]$ satisfy $$\[\lambda (x,y,u){\left| \xi \right|^2} \le \sum\limits_{i,j = 1}^2 {{a_{ij}}(x,y,u)} {\xi _i}{\xi _j} \le \Lambda (x,y,u){\left| \xi \right|^2}\]$$ for all $\[\xi \in {R^2}\]$ and $\[(x,y,u) \in \bar \Omega \times [0, + \infty ),i.e.\lambda (x,y,u),\Lambda (x,y,u)\]$ denote the minimum and maximum eigenvalues of the matrix $\[[{a_{ij}}(x,y,u)]\]$ respectively, moreover $$\[\lambda (x,y,0) = 0,\Lambda (x,u,0) = 0;\Lambda (x,y,u) \ge \lambda (x,y,u) > 0,(u > 0).\]$$ Some existence theorems under tire “ natural conditions imposed on $\[f(x,y,u,p,q)\]$ are obtained.     

二阶拟线性椭圆型方程的强非线性斜微商问题

本文利用某些算子在强弱拓扑意义下收敛的转换性质,两次运用Schauder不动点定理,建立了二阶拟线性椭圆型方程Lu≡α(x,y,u,u_∝,u_y)u_(∝x) 2b(x,y,u,u_∝,u_y)u_(∝y) c(x,y,u,u_x,u_y)u_(yy) d(x,y,u,u_x,u_y)=0,(x,y)∈G的强非线性斜微商问题αu_x-βu_y=f(x,y,u,u_x,u_y),α~2(x,y) β~2(x,y)≡1,(x,y)∈Г=аG解(或变态解)的存在性定理,并讨论了问题在负指标时的可解性条件。这里f关于u或u、u_0u_y具有指数大手1甚至整函数级增长的非线性,称之为强非线性。     

关于Campanato空间的一个注记

类似于Campanato空间理论，本文对具有以下连续模的函数类给出了一局部积分刻画：｜u（x）-u（y）｜≤C｜x—y｜^α（log｜x—y｜^-1）β，z，y∈Ω，其中C〉0，Ω为R^d中一正则区域，α∈（0，1]，β∈[0，1]．     

具有连续变量的非线性偏差分方程振动准则

本文研究具有连续变量的非线性变系数偏差分方程A(x+a,y) +Q(x,y) A(x,y+a) - R(x,y) A(x,y) +∑mi=1hi(x,y,A(x-σi,y-τi) ) =0其中 ,Q(x,y) ,R(x,y)∈ C(R+ × R+ - { 0 } ) ,hi(x,y,u)关于 u单调非减 ,且 hi(x,y,u) pi(x,y) u,(u>0 ) ;hi(x,y,u) pi(x,y) u,(u<0 )其中 ,pi(x,y)∈ C(R+ × R+ ,R+ - { 0 } ) ,i=1,2 ,… ,m,a,σi,τi∈ R+ ,得到了保证方程的所有解都具有振动生的若干充分条件     

A linear eigenvalue algorithm for the nonlinear eigenvalue problem

The Arnoldi method for standard eigenvalue problems possesses several attractive properties making it robust, reliable and efficient for many problems. The first result of this paper is a characterization of the solutions to an arbitrary (analytic) nonlinear eigenvalue problem (NEP) as the reciprocal eigenvalues of an infinite dimensional operator denoted ${\mathcal {B}}$ . We consider the Arnoldi method for the operator ${\mathcal {B}}$ and show that with a particular choice of starting function and a particular choice of scalar product, the structure of the operator can be exploited in a very effective way. The structure of the operator is such that when the Arnoldi method is started with a constant function, the iterates will be polynomials. For a large class of NEPs, we show that we can carry out the infinite dimensional Arnoldi algorithm for the operator ${\mathcal {B}}$ in arithmetic based on standard linear algebra operations on vectors and matrices of finite size. This is achieved by representing the polynomials by vector coefficients. The resulting algorithm is by construction such that it is completely equivalent to the standard Arnoldi method and also inherits many of its attractive properties, which are illustrated with examples.     

Continuity of the first eigenvalue for a family of degenerate eigenvalue problems

For each \({\alpha\in[0,2)}\) we consider the eigenvalue problem \({-{\rm div}(|x|^\alpha \nabla u)=\lambda u}\) in a bounded domain \({\Omega\subset \mathbb{R}^N}\) (\({N\geq 2}\)) with smooth boundary and \({0\in \Omega}\) subject to the homogeneous Dirichlet boundary condition. Denote by \({\lambda_1(\alpha)}\) the first eigenvalue of this problem. Using \({\Gamma}\)-convergence arguments we prove the continuity of the function \({\lambda_1}\) with respect to \({\alpha}\) on the interval \({[0,2)}\).     

On the condition numbers of a multiple eigenvalue of a generalized eigenvalue problem

For standard eigenvalue problems, closed-form expressions for the condition numbers of a multiple eigenvalue are known. In particular, they are uniformly 1 in the Hermitian case and generally take different values in the non-Hermitian case. We consider the generalized eigenvalue problem and identify the condition numbers. Our main result is that a multiple eigenvalue generally has multiple condition numbers, even in the Hermitian definite case. The condition numbers are characterized in terms of the singular values of the outer product of the corresponding left and right eigenvectors.     

Numerical enclosure for each eigenvalue in generalized eigenvalue problem

An algorithm for enclosing all eigenvalues in generalized eigenvalue problem Ax=λBx is proposed. This algorithm is applicable even if A∈C^n×n is not Hermitian and/or B∈C^n×n is not Hermitian positive definite, and supplies nerror bounds while the algorithm previously developed by the author supplies a single error bound. It is proved that the error bounds obtained by the proposed algorithm are equal or smaller than that by the previous algorithm. Computational cost for the proposed algorithm is similar to that for the previous algorithm. Numerical results show the property of the proposed algorithm.     

Quasilinearity below the 1st eigenvalue

This paper establishes the existence of two nontrivial weak solutions for a quasilinear Dirichlet problem below the first eigenvalue via the mountain pass theorem.