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Eufisky - The lost book

多重积分计算的一些题

(1)设fD:x2+y21上二阶连续可微,且Δf=2fx2+2fy2=x2+y2,


cool解:(Hansschwarzkopf)根据Gauss公式

\begin{align*}&\iint\limits_D\left(\frac{x}{\sqrt{x^2+y^2}}\frac{\partial f}{\partial x}+ \frac{y}{\sqrt{x^2+y^2}}\frac{\partial f}{\partial y}\right)\mathrm{d}x\mathrm{d}y \\&=\int_0^1\mathrm{d}r\int\limits_{x^2+y^2 = r^2}\left(\frac{x}{\sqrt{x^2+y^2}}\frac{\partial f}{\partial x}+ \frac{y}{\sqrt{x^2+y^2}}\frac{\partial f}{\partial y}\right)\mathrm{d}s\\ &=\int_0^1\mathrm{d}r\int\limits_{x^2+y^2 = r^2}\frac{\partial f}{\partial n}\mathrm{d}s=\int_0^1\mathrm{d}r\iint\limits_{x^2+y^2 \leqslant r^2}\Delta f\mathrm{d}x\mathrm{d}y\\&=\int_0^1\mathrm{d}r\iint\limits_{x^2+y^2 \leqslant r^2}(x^2+y^2)\mathrm{d}x\mathrm{d}y=\int_0^1\frac{\pi r^4}{2}\mathrm{d}r =\frac{\pi}{10} .\end{align*}
 
(2)设fD:x^2+y^2\leq1上二阶连续可微,且\Delta f=\frac{\partial^2f}{\partial x^2}+\frac{\partial^2f}{\partial y^2}=\exp{(-x^2-y^2)},

\iint\limits_D\left(x\frac{\partial f}{\partial x}+ y\frac{\partial f}{\partial y}\right)\mathrm{d}x\mathrm{d}y.


cool解:(Hansschwarzkopf)根据Gauss公式

\begin{align*}&\iint\limits_D\left(x\frac{\partial f}{\partial x}+ y\frac{\partial f}{\partial y}\right)\mathrm{d}x\mathrm{d}y\\ &=\int_0^1\mathrm{d}r\int\limits_{x^2+y^2 = r^2}\left(x\frac{\partial f}{\partial x}+y\frac{\partial f}{\partial y}\right)\mathrm{d}s\\ &=\int_0^1\mathrm{d}r\int\limits_{x^2+y^2 =r^2}r\frac{\partial f}{\partial n}\mathrm{d}s=\int_0^1\mathrm{d}r\iint\limits_{x^2+y^2 \leqslant r^2}r\Delta f\mathrm{d}x\mathrm{d}y\\ &=\int_0^1\mathrm{d}r\iint\limits_{x^2+y^2 \leqslant r^2}r\exp{(-x^2-y^2)}\mathrm{d}x\mathrm{d}y=\int_0^1\pi r(1-e^{-r^2})\mathrm{d}r =\frac{\pi }{2e} .\end{align*}

浙江大学2012年研究生入学考试高等代数试题参考解答

浙江大学2012年研究生入学考试高等代数试题参考解答
 
 
高等代数资源网
 
2013.8.2
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2. 试题

每题15分.

 

一.设En阶单位矩阵,

M=\begin{pmatrix}0&E\\-E&0\end{pmatrix},

矩阵A满足A^{T}MA=M.证明A的行列式等于1.

 

二.设An阶幂等矩阵,满足

 

(1)A=A_{1}+\cdots+A_{s};

 

(2)r(A)=r(A_{1})+\cdots+r(A_{s}).

 

证明:所有的A_{i}都相似于一个对角阵,A_{i}的特征值之和等于矩阵A_{i}的秩.

 

三.设\phin维欧氏空间的正交变换,证明:\phi最多可以表示为n+1个镜面反射的复合.

 

四.设An阶复矩阵,证明存在常数项等于0的多项式g(\lambda),h(\lambda)使得g(A)是可以对角化的矩阵,h(A)是幂零矩阵,且A=g(A)+h(A).

 

五.设A=\begin{pmatrix}3&2&-2\\k&-1&-k\\4&2&-3\end{pmatrix}.(i)当k为何值时,存在矩阵P使得P^{-1}AP为对角矩阵?并求出这样的矩阵P和对角矩阵.(ii)求k=2时矩阵A的Jordan标准形.

 

六.令二次型f(x_{1},\cdots,x_{n})=\sum_{i=1}^{m}(a_{i1}x_{1}+\cdots+a_{in}x_{n})^{2}.

 

(i)求此二次型的方阵.

 

(ii)当a_{ij}均为实数时,给出此二次型为正定的条件.

 

七.设VW是数域K上的线性空间,Hom_{K}(V,W)表示VW的所有线性映射组成的线性空间.证明:对f,g\in Hom_{K}(V,W),Imf\cap Img=\{0\},f,gHom_{K}(V,W)中是线性无关的.

 

八.令线性空间V=Imf\oplus W,其中W是线性变换f的不变子空间.

 

(i)证明W\subseteq Kerf;

 

(ii)证明若V是有限维线性空间,则W=Kerf;

 

(iii)举例说明,当V是无限维的,可能有W\subseteq Ker f,W\neq Kerf.

 

九.设A=\begin{pmatrix}1&0&-1&2&1\\-1&1&3&-1&0\\-2&1&4&-1&3\\3&-1&-5&1&-6\end{pmatrix}.

 

(i)求5\times 5阶秩为2的矩阵M,使得AM=0;

 

(ii)假如B是满足AB=05\times5阶矩阵,证明:秩\mathrm{rank\,}(B)\leq2.

 

十.令T是有限维线性空间V的线性变换,设WVT-不变子空间.那么T|_{W}的最小多项式整除T的最小多项式.

 

 

 

3. 参考解答

一.设En阶单位矩阵,

M=\begin{pmatrix}0&E\\-E&0\end{pmatrix},

矩阵A满足A^{T}MA=M.证明A的行列式等于1.

 

\textbf{证明:}(法1)将A分块为

A=\begin{pmatrix}A_{1}&A_{2}\\A_{3}&A_{4}\end{pmatrix}

A^{T}MA=M

\begin{aligned}-A_{2}A_{1}^{T}+A_{1}A_{2}^{T}&=&0\\-A_{2}A_{3}^{T}+A_{1}A_{4}^{T}&=&I\\\end{aligned}

 

A_{1}可逆,由A的分块可得|A|=|A_{1}||A_{4}-A_{3}A_{1}^{-1}A_{2}|由上面第一式可得A_{2}=A_{1}A_{2}^{T}(A_{1}^{-1})^{T}

代入第二式可得

A_{4}-A_{3}A_{1}^{-1}A_{2}=(A_{1}^{-1})^{T}

从而可得|A|=1.

 

A_{1}不可逆时,考虑矩阵A_{1}+tE,则存在无穷多t的值使得A_{1}+tE可逆(这是因为|A_{1}+tE|是关于t的一个多项式,只能有有限个根.),由前面的证明有

1=\begin{vmatrix}A_{1}+tE&A_{2}\\A_{3}&A_{4}\end{vmatrix}.

此式两边是关于t的多项式,且有无穷多t的值使得等式成立,从而等式恒成立.令t=0可得.

 

(法2)博士数学论坛(\url{www.math.org.cn})的mxcandy提供.

 

A^{T}MA=M

M(\lambda E-A)=\lambda M-MA=\lambda A^{T}MA-MA=(\lambda A^{T}-E)MA.

两边取行列式,由|M|=1\neq0

|\lambda E-A|=|A||\lambda A^{T}-E|.

注意到A2n阶矩阵,以及矩阵的转置行列式不变有

|\lambda A^{T}-E|=|E-\lambda A^{T}|=|(E-\lambda A^{T})^{T}|=|E-\lambda A|.

于是

|\lambda E-A|=|A||\lambda A^{T}-E|=|A||E-\lambda A|.

A的特征多项式|\lambda E-A|=f(\lambda),则由上式有

f(\lambda)=|\lambda E-A|=|A|\lambda^{2n}f(\dfrac{1}{\lambda}).\eqno(*)

考虑\lambda=1,2n次多项式f(\lambda)有分解式

f(\lambda)=(\lambda-1)^{m}g(\lambda),g(1)\neq0,0\leq m\leq 2n.

已知A^{T}MA=M,两边取行列式,可得|A|^{2}=1,从而A可逆,故

MA=(A^{T})^{-1}M,

由平滑性或者归纳法可得,对任意自然数k

M(E-A)^{k}=(E-(A^{T})^{-1})^{k}M,

从而

M(E-A)^{2k}=M(E-A)^{k}(E-A)^{k}=(E-(A^{T})^{-1})^{k}M(E-A)^{k}=-(A^{T})^{-k}(E-A)^{k}M(E-A)^{k}.

M^{T}=-M知,(E-A)^{k}M(E-A)^{k}反对称,注意到|M|=1,从而

\mathrm{rank\,}((E-A)^{2k})=\mathrm{rank\,}((E-A)^{k}M(E-A)^{k}).

由于反对称矩阵的秩为偶数,从而\mathrm{rank\,}((E-A)^{2k})为偶数.特别的,任取2k\geq m,则特征值\lambda=1的代数重数m为偶数,即

m=2n-\mathrm{rank\,}((E-A)^{2k})\triangleq 2p,2k\geq m,0\leq p\leq n.

f(\lambda)=(\lambda-1)^{2p}g(\lambda)代入到(*)式,得

(\lambda-1)^{2p}g(\lambda)=|A|\lambda^{2n}(\dfrac{1}{\lambda}-1)^{2p}g(\dfrac{1}{\lambda}),

g(\lambda)=|A|\lambda^{2n-2k}g(\dfrac{1}{\lambda}),

\lambda=1,并注意到g(1)\neq0,可得|A|=1.

 

注1:满足题目条件的矩阵A称为辛矩阵.

 

注2:由上述证明知:辛矩阵的特征多项式自反,特征值互倒成对,\lambda=\pm1代数重数为偶数.

 

(法3)许以超,线性代数与矩阵论(第二版).高等教育出版社.P329.

 

(法4)许以超,线性代数与矩阵论(第二版).高等教育出版社.P405.

 

(法5)高等代数中的一些问题.博士数学论坛(\url{www.math.org.cn})xida.P7.

二.设An阶幂等矩阵,满足

 

(1)A=A_{1}+\cdots+A_{s};

 

(2)r(A)=r(A_{1})+\cdots+r(A_{s}).

 

证明:所有的A_{i}都相似于一个对角阵,A_{i}的特征值之和等于矩阵A_{i}的秩.

 

\textbf{证明:}只需证明A_{i}是幂等矩阵.利用n阶矩阵C是幂等矩阵的充要条件为r(C)+r(C-E)=n,只需证明r(A_{i}-E)=n-r(A_{i}).利用矩阵秩的不等式

|r(A)-r(B)|\leq r(A\pm B)\leq r(A)+r(B)

以及题目条件有

\begin{aligned}n-r(A_{i})\leq r(A_{i}-E)&=r(A-E-(A_{1}+\cdots+A_{i-1}+A_{i+1}+\cdots+A_{s}))\\&\leq r(A-E)+r(A_{1}+\cdots+A_{i-1}+A_{i+1}+\cdots+A_{s})\\&\leq r(A-E)+r(A_{1})+\cdots+r(A_{i-1})+r(A_{i+1})+\cdots+r(A_{s})\\&=n-r(A)+r(A)-r(A_{i})\\&=n-r(A_{i}).\end{aligned}

从而r(A_{i}-E)=n-r(A_{i}).

 

三.设\phin维欧氏空间的正交变换,证明:\phi最多可以表示为n+1个镜面反射的复合.

 

\textbf{证明:}(法1)设\alphan维欧氏空间V中的单位向量,定义线性变换

\sigma(\beta)=\beta-(\beta,\alpha)\alpha,\forall\beta\in V,

\sigmaV的正交变换,称为镜面反射(镜像变换).计算可得\sigma^{2}=I(恒等变换).

 

\alpha_{1},\alpha_{2}n维欧氏空间V中的两个长度相等的不同向量,则存在镜面反射\sigma使得\sigma(\alpha_{1})=\alpha_{2}.

实际上,令\alpha=\dfrac{\alpha_{1}-\alpha_{2}}{|\alpha_{1}-\alpha_{2}|},定义\sigma(\beta)=\beta-(\beta,\alpha)\alpha,\forall\beta\in V即可.

 

下面证明原问题.对空间的维数n用数学归纳法.

 

n=1时,设e_{1}V的单位向量,则V=L(e_{1}).由于\phi(e_{1})\in V,故存在实数\lambda使得\phi(e_{1})=\lambda e_{1},\phi是正交变换可得

1=(e_{1},e_{1})=(\phi(e_{1}),\phi(e_{1}))=\lambda^{2}(e_{1},e_{1})=\lambda^{2},

因此\lambda=\pm1.

\tau(\alpha)=\alpha-2(\alpha,e_{1})e_{1},\forall\alpha\in V,

\tau是镜面反射,且当\lambda=1时,对\forall\alpha\in V,\alpha=ke_{1},k\in R

\phi(\alpha)=k\phi(e_{1})=ke_{1},\forall\alpha=ke_{1}=\alpha,\in V,

\phi是恒等变换.而

\tau^{2}(\alpha)=k\tau^{2}(e_{1})=k\tau(-e_{1})=ke_{1}=\alpha,

\tau^{2}也是恒等变换,从而\phi=\tau^{2}.而当\lambda=-1时,显然\phi=\tau.

 

假设结论对n-1维欧氏空间成立,对n维欧氏空间V的一正交变换\phi,\phi=I,则对V的任一镜面反射\sigma\phi=I=\sigma^{2}.\phi\neq I,则存在V的单位向量e使得\phi(e)=\eta\neq e,由于|\eta|=|\phi(e)|,从而存在V的镜面反射\tau使得\tau(\eta)=e.

于是\tau(\phi(e))=e.

W=L(e),由于\tau\phi仍为正交变换,故W^{\bot}\tau\phin-1维不变子空间,且\tau\phi|_{W^{\bot}}为正交变换.由归纳假设,在W^{\bot}中存在单位向量\alpha_{1},\alpha_{2},\cdots,\alpha_{k},它们分别决定W^{\bot}的镜面反射\sigma_{1},\sigma_{2},\cdots,\sigma_{k}使得

\tau\phi|_{W^{\bot}}=\sigma_{1}\sigma_{2}\cdots\sigma_{k},

现将\sigma_{i}的定义扩大到V,即补充定义\sigma_{i}(e)=e.\sigma_{i}即为\alpha_{i}决定的V的镜面反射.这是因为\forall\alpha\in V,\alpha=\beta_{1}+\beta_{2},\beta_{1}=ke\in W=L(e),\beta_{2}\in W^{\bot},注意到(\beta_{1},\alpha_{i})=0,

 

\begin{aligned}\sigma_{i}(\alpha)&=\sigma_{i}(\beta_{1})+\sigma(\beta_{2})\\&=\beta_{1}+\beta_{2}-2(\beta_{2},\alpha_{i})\alpha_{i}\\&=\alpha-2(\alpha,\alpha_{i})\alpha_{i}.\end{aligned}

现在显然有\sigma_{1}\sigma_{2}\cdots\sigma_{k}(\beta_{1})=\beta_{1},这是因为\tau\phi(e)=e,\tau\phi(\beta_{1})=\beta_{1}.从而

 

\begin{aligned}\tau\phi(\alpha)&=\tau\phi(\beta_{1})+\tau\phi(\beta_{2})\\&=\beta_{1}+\sigma_{1}\sigma_{2}\cdots\sigma_{k}(\beta_{2})\\&\sigma_{1}\sigma_{2}\cdots\sigma_{k}(\beta_{1})+\sigma_{1}\sigma_{2}\cdots\sigma_{k}(\beta_{2})\\&=\sigma_{1}\sigma_{2}\cdots\sigma_{k}(\alpha)\end{aligned}

 

从而\tau\phi=\sigma_{1}\sigma_{2}\cdots\sigma_{k}.注意到\tau^{2}=I,

\phi=\tau\sigma_{1}\sigma_{2}\cdots\sigma_{k}.

 

(法2)n阶矩阵M=E-2\alpha\alpha^{T},其中\alphan维实列向量,且\alpha^{T}\alpha=1.则矩阵M是正交矩阵,称为镜像矩阵.容易验证M^{2}=E.即单位矩阵是两个镜像矩阵之积.

 

\alpha,\beta是两个不同的n维实列向量,且|\alpha|=|\beta|,则存在实镜像矩阵M使得M\alpha=\beta.实际上,令\alpha=\dfrac{\alpha-\beta}{|\alpha-\beta|},M=E-2\alpha\alpha^{T}即可.

 

可以证明欧氏空间中的线性变换\phi是镜面反射的充要条件是\phi在一组标准正交基下的矩阵为镜像矩阵.

 

这样要证明原问题,只需证明任意n阶实正交矩阵A可以分解不超过n+1个镜像矩阵之积即可.

 

对矩阵的阶数n用数学归纳法.

n=1时,结论显然成立.

 

假设结论对n-1阶矩阵成立,将n阶正交矩阵A按列分块为

A=(\alpha_{1},\alpha_{2},\cdots,\alpha_{n}),

|\alpha_{1}|=1,从而存在镜像矩阵M_{1}使得M_{1}\alpha_{1}=(1,0,\cdots,0)^{T},注意到M_{1}A还是正交矩阵,必有

 

 

M_{1}A=M_{1}(\alpha_{1},\alpha_{2},\cdots,\alpha_{n})=(M_{1}\alpha_{1},M_{1}\alpha_{2},\cdots,M_{1}\alpha_{n})=\begin{pmatrix}1&0&\cdots&0\\0& &  &\\\vdots&&Q_{1}&\\0&&&\end{pmatrix}

 

 

容易验证Q_{1}也是正交矩阵,从而由归纳假设,存在n-1阶镜像矩阵M_{2},\cdots,M_{k}使得Q_{1}=M_{2}\cdots M_{k},

于是

A=M_{1}^{-1}\begin{pmatrix}1&0&\cdots&0\\0& &  &\\\vdots&&Q_{1}&\\0&&&\end{pmatrix}=M_{1}^{-1}\begin{pmatrix}1&\\&M_{2}\cdots M_{k}\end{pmatrix}=M_{1}^{-1}\begin{pmatrix}1&\\&M_{2}\end{pmatrix}\cdots\begin{pmatrix}1&\\&M_{k}\end{pmatrix}.

 

易知\begin{pmatrix}1&\\&M_{i}\end{pmatrix}都是镜像矩阵.

 

 

 

四.设An阶复矩阵,证明存在常数项等于0的多项式g(\lambda),h(\lambda)使得g(A)是可以对角化的矩阵,h(A)是幂零矩阵,且A=g(A)+h(A).

 

\textbf{证明:}等我看看能否找到一个好的方法.

 

五.设A=\begin{pmatrix}3&2&-2\\k&-1&-k\\4&2&-3\end{pmatrix}.(i)当k为何值时,存在矩阵P使得P^{-1}AP为对角矩阵?并求出这样的矩阵P和对角矩阵.(ii)求k=2时矩阵A的Jordan标准形.

 

\textbf{证明:}由于

|A-\lambda E|=\begin{vmatrix}3-\lambda&2&-2\\k&-1-\lambda&-k\\4&2&-3-\lambda\end{vmatrix}=-(\lambda+1)^{2}(\lambda-1),

A的特征值为\lambda_{1}=-1(\mbox{二重}),\lambda_{2}=1.

 

(i)存在矩阵P使得P^{-1}AP为对角矩阵的充要条件是特征值的代数重数等于几何重数,即r(A-\lambda_{1}E)=1,

A-\lambda_{1}E=\begin{pmatrix}4&2&-2\\k&0&-k\\4&2&-2\end{pmatrix},

 

从而k=0.

P可以是

P=\begin{pmatrix}1&1&1\\-2&0&0\\0&2&1\\\end{pmatrix},此时P^{-1}AP=diag(-1,-1,1).

 

(2)k=2

 

\lambda E-A=\begin{pmatrix}\lambda-3&-2&2\\-2&\lambda+1&2\\-4&-2&\lambda+3\end{pmatrix}\rightarrow\begin{pmatrix}1&&\\&1&\\&&(\lambda+1)^{2}(\lambda-1)\end{pmatrix},

 

所以A的Jordan标准形为\begin{pmatrix}-1&1&\\&-1&\\&&1\end{pmatrix}.

 

六.令二次型f(x_{1},\cdots,x_{n})=\sum_{i=1}^{m}(a_{i1}x_{1}+\cdots+a_{in}x_{n})^{2}.

 

(i)求此二次型的方阵.

 

(ii)当a_{ij}均为实数时,给出此二次型为正定的条件.

 

\textbf{证明:}(i)由于

(a_{i1}x_{1}+\cdots+a_{in}x_{n})^{2}=(x_{1},\cdots,x_{n})\begin{pmatrix}a_{i1}\\\vdots\\a_{in}\end{pmatrix}\begin{pmatrix}a_{i1}&\cdots&a_{in}\end{pmatrix}\begin{pmatrix}x_{1}\\\vdots\\x_{n}\end{pmatrix},

 

\begin{align*}f(x_{1},\cdots,x_{n})&=\sum_{i=1}^{m}(a_{i1}x_{1}+\cdots+a_{in}x_{n})^{2}\\&=\sum_{i=1}^{n}(x_{1},\cdots,x_{n})\begin{pmatrix}a_{i1}\\\vdots\\a_{in}\end{pmatrix}\begin{pmatrix}a_{i1}&\cdots&a_{in}\end{pmatrix}\begin{pmatrix}x_{1}\\\vdots\\x_{n}\end{pmatrix}\\&=(x_{1},\cdots,x_{n})\begin{pmatrix}\sum_{i=1}^{n}a_{i1}^{2}&\cdots&\sum_{i=1}^{n}a_{i1}a_{in}\\\vdots&\vdots&\vdots\\\sum_{i=1}^{n}a_{in}a_{i1}&\cdots&\sum_{i=1}^{n}a_{in}^{2}\end{pmatrix}\begin{pmatrix}x_{1}\\\vdots\\x_{n}\end{pmatrix}\end{align*}.

 

若记A=(a_{ij})_{n\times n},f(x_{1},\cdots,x_{n})=(x_{1},\cdots,x_{n})(A^{T}A)\begin{pmatrix}x_{1}\\\vdots\\x_{n}\end{pmatrix}.

 

故所求矩阵为A^{T}A.

 

(2)当a_{ij}为实数时,A^{T}A是半正定的,故f(x_{1},\cdots,x_{n})=(x_{1},\cdots,x_{n})(A^{T}A)\begin{pmatrix}x_{1}\\\vdots\\x_{n}\end{pmatrix}

正定的充要条件是r(A^{T}A)=n.r(A^{T}A)=r(A),故原二次型正定的充要条件是r(A)=n.

 

七.设VW是数域K上的线性空间,Hom_{K}(V,W)表示VW的所有线性映射组成的线性空间.证明:对f,g\in Hom_{K}(V,W),Imf\cap Img=\{0\},f,gHom_{K}(V,W)中是线性无关的.

 

\textbf{证明:}注:这里应该假设f\neq0,g\neq0.否则题目无意义.

反证法.假设f=kg,k\in K,由于f\neq0,故存在\alpha\in V,使得0\neq f(\alpha)\in Im f\subset W,此时

o\neq \dfrac{1}{k}f(\alpha)=g(\alpha)\in Img,

注意到ImgW的字空间,从而f(\alpha)\in Img,这样0\neq f(\alpha)\in Imf\cap Img.这与条件矛盾.

 

八.令线性空间V=Imf\oplus W,其中W是线性变换f的不变子空间.

 

(i)证明W\subseteq Kerf;

 

(ii)证明若V是有限维线性空间,则W=Kerf;

 

(iii)举例说明,当V是无限维的,可能有W\subseteq Ker f,W\neq Kerf.

 

\textbf{证明:}(i)\forall\alpha\in W,则由条件有

f(\alpha)\in Imf\cap W,

注意到V=Imf\oplus W,从而Imf\cap W=\{0\},f(\alpha)=0.\alpha\in Kerf.这就证明了W\subseteq Kerf.

 

(2)由(i),要证明W=Kerf,只需证明dim W=dim Kerf.而由V=Imf\oplus W以及维数公式dimV=dim Imf+dim Kerf

dim W=dimV-dim Imf=dim Kerf.

从而结论成立.

 

(3)例:V=P[x]是数域P上关于x的一元多项式的全体,则V是无限维线性空间,f(p(x))=p'(x)V上的求导线性变换,则

此时Imf=V,Kerf=P,W=\{0\}.

 

九.设A=\begin{pmatrix}1&0&-1&2&1\\-1&1&3&-1&0\\-2&1&4&-1&3\\3&-1&-5&1&-6\end{pmatrix}.

 

(i)求5\times 5阶秩为2的矩阵M,使得AM=0;

 

(ii)假如B是满足AB=05\times5阶矩阵,证明:秩\mathrm{rank\,}(B)\leq2.

 

\textbf{证明:}将M按列分块为

M=(m_{1},m_{2},m_{3},m_{4},m_{5}),

0=AM=A(m_{1},m_{2},m_{3},m_{4},m_{5})=(Am_{1},Am_{2},Am_{3},Am_{4},Am_{5}),

Am_{i}=0,i=1,2,3,4,5.此即m_{i}是线性方程组Ax=0的解.

 

(i)求解Ax=0可得其一个基础解系为

\alpha_{1}=(-1,2,1,0)^{T},\alpha_{2}=(3,1,0,-2,0)^{T}.

故可取

M=(\alpha_{1},\alpha_{2},\alpha_{1},\alpha_{1},\alpha_{1}).

 

(ii)注意到B的列向量是方程组Ax=0的解,而方程组的任一解皆可由其基础解系线性表示,故B的列向量可由\alpha_{1},\alpha_{2}线性表示,故r(B)\leq2.

十.令T是有限维线性空间V的线性变换,设WVT-不变子空间.那么T|_{W}的最小多项式整除T的最小多项式.

 

\textbf{证明:}易知W是平凡子空间,即W=\{0\}\mbox{或}W=V时,结论成立.

 

下面假设0<dimW=r<dimV=n,W的一组基\alpha_{1},\cdots,\alpha_{r},将其扩充为V的一组基\alpha_{1},\cdots,\alpha_{r},\alpha_{r+1},\cdots,\alpha_{n},WT的不变子空间,则可知T在上述基下的矩阵为

T(\alpha_{1},\cdots,\alpha_{r},\alpha_{r+1},\cdots,\alpha_{n})=(\alpha_{1},\cdots,\alpha_{r},\alpha_{r+1},\cdots,\alpha_{n})\begin{pmatrix}A_{r\times r}&B\\0&C_{(n-r)\times(n-r)}\end{pmatrix}.

T|_{W},T的最小多项式分别为m_{T}(x),m(x),

0=m(\begin{pmatrix}A_{r\times r}&B\\0&C_{(n-r)\times(n-r)}\end{pmatrix})=\begin{pmatrix}m(A_{r\times r})&*\\0&m(C_{(n-r)\times(n-r)})\end{pmatrix},

从而m(A)=0,m(x)T|_{W}的零化多项式,从而m_{T}(x)|m(x).

转载自:http://www.52gd.org/?p=414

北京大学数学科学学院2015年直博生摸底考试试题解答

这份试题本来已经写好答案了,但因为电脑的事,里面文件都没了。下面重新给出解答:


1.(90分) 设y=f(x)\mathbb{R}上的C^\infty函数,对任意整数k\geq0,记M_k=\sup_{x\in\mathbb{R}}|f^{(k)}(x)|.设mn为两整数, 0\leq m<n,试分别就下列情况,给出你的结论和证明.
(1)如果M_mM_n均有界,那么对哪些整数k, M_k有界?对哪些整数k, M_k可以无界?
(2)如果\lim_{x\to+\infty}|f^{(m)}(x)|存在有限极限,而M_n有界,则对哪些自然数k,极限\lim_{x\to+\infty}|f^{(k)}(x)|也存在极限?
(3)如果\lim_{x\to+\infty}|f^{(m)}(x)|\lim_{x\to+\infty}|f^{(n)}(x)|都存在有限极限,则对哪些自然数k,极限\lim_{x\to+\infty}|f^{(k)}(x)|也存在极限?
 

2.(30分) 判断级数\sum\nolimits_{n = 2}^{ + \infty } {\frac{{{{\left( { - 1} \right)}^n}}}{{\sqrt n  + {{\left( { - 1} \right)}^{\left[ {\sqrt n } \right]}}}}}的敛散性,其中[x]表示x的取整.

enlightened证:\begin{align*}\sum\limits_{n = 2}^{ + \infty } {\frac{{{{\left( { - 1} \right)}^n}}}{{\sqrt n  + {{\left( { - 1} \right)}^{\left[ {\sqrt n } \right]}}}}}  &= \sum\limits_{n = 2}^{ + \infty } {\frac{{{{\left( { - 1} \right)}^n}\left( {\sqrt n  - {{\left( { - 1} \right)}^{\left[ {\sqrt n } \right]}}} \right)}}{{n - 1}}} \\&= \sum\limits_{n = 2}^{ + \infty } {\frac{{{{\left( { - 1} \right)}^n}\sqrt n }}{{n - 1}}}  - \sum\limits_{n = 2}^{ + \infty } {\frac{{{{\left( { - 1} \right)}^{n + \left[ {\sqrt n } \right]}}}}{{n - 1}}}.\end{align*}

由Leibniz判别法知,级数\sum\limits_{n = 2}^{ + \infty } {\frac{{{{\left( { - 1} \right)}^n}\sqrt n }}{{n - 1}}}  = \sum\limits_{n = 2}^{ + \infty } {\frac{{{{\left( { - 1} \right)}^n}}}{{\sqrt n  - \frac{1}{{\sqrt n }}}}} 收敛.
k \le \sqrt n  < k + 1,即{k^2} \le n < {\left( {k + 1} \right)^2}时, {\left[ {\sqrt n } \right]}=k,则
\begin{align*}&\sum\limits_{n = 2}^{ + \infty } {\frac{{{{\left( { - 1} \right)}^{n + \left[ {\sqrt n } \right]}}}}{{n - 1}}}  =  - \frac{1}{2} + \sum\limits_{k = 2}^{ + \infty } {\sum\limits_{n = {k^2}}^{{k^2} + 2k} {\frac{{{{\left( { - 1} \right)}^{n + k}}}}{{n - 1}}} }  =  - \frac{1}{2} + \sum\limits_{k = 2}^{ + \infty } {{{\left( { - 1} \right)}^k}\sum\limits_{n = {k^2}}^{{k^2} + 2k} {\frac{{{{\left( { - 1} \right)}^n}}}{{n - 1}}} } \\&=  - \frac{1}{2} + \sum\limits_{k = 2}^{ + \infty } {{{\left( { - 1} \right)}^k}\sum\limits_{n = {k^2}}^{{k^2} + 2k} {{{\left( { - 1} \right)}^{{k^2}}}\left[ {\left( {\frac{1}{{{k^2} - 1}} + \frac{1}{{{k^2} + 1}} +  \cdots  + \frac{1}{{{k^2} + 2k - 1}}} \right)} \right.} } \\&\left. { - \left( {\frac{1}{{{k^2}}} + \frac{1}{{{k^2} + 2}} +  \cdots  + \frac{1}{{{k^2} + 2k - 2}}} \right)} \right] =  - \frac{1}{2} + \sum\limits_{k = 2}^{ + \infty } {\left[ {\left( {\frac{1}{{{k^2} - 1}} + \frac{1}{{{k^2} + 1}} +  \cdots  + \frac{1}{{{k^2} + 2k - 1}}} \right)} \right.} \\&\left. { - \left( {\frac{1}{{{k^2}}} + \frac{1}{{{k^2} + 2}} +  \cdots  + \frac{1}{{{k^2} + 2k - 2}}} \right)} \right] \le  - \frac{1}{2} + \sum\limits_{k = 2}^{ + \infty } {\left[ {\left( {\frac{{k + 1}}{{{k^2} - 1}} - \frac{k}{{{k^2} + 2k - 2}}} \right)} \right.} \\&\le  - \frac{1}{2} + \sum\limits_{k = 2}^{ + \infty } {\left[ {\left( {\frac{1}{{k - 1}} - \frac{1}{{k + 2}}} \right)} \right.}  =  - \frac{1}{2} + 1 + \frac{1}{2} + \frac{1}{3} = \frac{4}{3}\end{align*}
\begin{align*}{a_n} &= \left( {\frac{1}{{{k^2} - 1}} + \frac{1}{{{k^2} + 1}} +  \cdots  + \frac{1}{{{k^2} + 2k - 1}}} \right) - \left( {\frac{1}{{{k^2}}} + \frac{1}{{{k^2} + 2}} +  \cdots  + \frac{1}{{{k^2} + 2k - 2}}} \right)\\&= \left( {\frac{1}{{{k^2} - 1}} - \frac{1}{{{k^2}}}} \right) + \left( {\frac{1}{{{k^2} + 1}} - \frac{1}{{{k^2} + 2}}} \right) +  \cdots  + \left( {\frac{1}{{{k^2} + 2k - 3}} - \frac{1}{{{k^2} + 2k - 2}}} \right) + \frac{1}{{{k^2} + 2k - 1}}\\&\ge \frac{1}{{{k^2} + 2k - 1}} > 0.\end{align*}
\sum\limits_{n = 2}^{ + \infty } {\frac{{{{\left( { - 1} \right)}^{n + \left[ {\sqrt n } \right]}}}}{{n - 1}}} 收敛,从而数列\sum\nolimits_{n = 2}^{ + \infty } {\frac{{{{\left( { - 1} \right)}^n}}}{{\sqrt n  + {{\left( { - 1} \right)}^{\left[ {\sqrt n } \right]}}}}}亦收敛.laugh

3.(30分) 证明\int_0^1 {\int_0^1 {\frac{1}{{{{\left( {xy} \right)}^{xy}}}}dxdy} }  = \int_0^1 {\frac{1}{{{x^x}}}dx}  = \sum\limits_{n = 1}^{ + \infty } {\frac{1}{{{n^n}}}} .

enlightened证:令u = xy,v = x,则x=v,y=\frac uv.由0\leq x,y\leq 1可知0\leq u\leq v,0\leq v\leq 1,

\begin{align*}\left| {\frac{{\partial \left( {x,y} \right)}}{{\partial \left( {u,v} \right)}}} \right| = \left| {\begin{array}{*{20}{c}}0&1\\{\frac{1}{v}}&{ - \frac{u}{{{v^2}}}}\end{array}} \right| = - \frac{1}{v}\,,\end{align*}
那么有
\begin{align*}&\int_0^1 {\int_0^1 {\frac{1}{{{{\left( {xy} \right)}^{xy}}}}dxdy} } = \int_0^1 {dv} \int_0^v {\frac{1}{{{u^u}v}}du} \\&= \int_0^1 {du} \int_u^1 {\frac{1}{{{u^u}v}}dv} = \int_0^1 {\frac{{ - \ln u}}{{{u^u}}}du} \\&= \int_0^1 {\frac{{ - \ln u - 1}}{{{u^u}}}du} + \int_0^1 {\frac{1}{{{u^u}}}du} \\&= \left[ {\frac{1}{{{u^u}}}} \right]_0^1 + \int_0^1 {\frac{1}{{{u^u}}}du} = \int_0^1 {\frac{1}{{{x^x}}}dx}.\end{align*}
\begin{align*}&\int_0^1 {\frac{1}{{{x^x}}}dx} = \int_0^1 {{e^{ - x\ln x}}dx} = \int_0^1 {{e^{ - x\ln x}}dx} \\&= \int_0^1 {\sum\limits_{n = 0}^{+\infty} {\frac{{{{\left( { - x\ln x} \right)}^n}}}{{n!}}} dx} = \sum\limits_{n = 0}^{+\infty} {\frac{1}{{n!}}\int_0^1 {{{\left( { - x\ln x} \right)}^n}dx} }.\end{align*}
t=-(n+1)\ln x,有
\begin{align*}\int_0^1 {{{\left( { - x\ln x} \right)}^n}dx} &= \frac{1}{{{{\left( {n + 1} \right)}^{n + 1}}}}\int_0^{ + +\infty } {{t^n}{e^{ - t}}dt} \\&= \frac{{\Gamma \left( {n + 1} \right)}}{{{{\left( {n + 1} \right)}^{n + 1}}}} = \frac{{n!}}{{{{\left( {n + 1} \right)}^{n + 1}}}}.\end{align*}
因此有
\begin{align*}\int_0^1 {\frac{1}{{{x^x}}}dx} = \sum\limits_{n = 0}^{ + \infty } {\frac{1}{{n!}}\int_0^1 {{{\left( { - x\ln x} \right)}^n}dx} } = \sum\limits_{n = 0}^{ + \infty } {\frac{1}{{{{\left( {n + 1} \right)}^{n + 1}}}}} =\sum\limits_{n = 1}^{ + \infty } {\frac{1}{{{n^n}}}} .\end{align*}laugh

4.(25分) 设A是一个n阶方阵,且n\geq3.A^\astA的伴随矩阵(即A的代数余子式所组成的矩阵).试证明,若{(A^\ast)}^\ast\neq O (零矩阵),则A可逆,且此时{(A^\ast)}^\astA的一个纯量倍.

enlightened证:由于A可逆时,A^\ast必可逆,从而{(A^\ast)}^\ast亦可逆;当A不可逆时,A^\ast的秩不大于1,从而{(A^\ast)}^\ast必为零矩阵.

 

由此可知,当{(A^\ast)}^\ast\neq O 时,A可逆.再由A{A^ * } = \left| A \right|{I_n} \Rightarrow \left| {{A^ * }} \right| = {\left| A \right|^{n - 1}},{\left( {{A^ * }} \right)^{ - 1}} = \frac{1}{{\left| A \right|}}A{A^ * }{\left( {{A^ * }} \right)^*} = \left| {{A^ * }} \right|{I_n}可知

{\left( {{A^ * }} \right)^*} = \left| {{A^ * }} \right|{\left( {{A^ * }} \right)^{ - 1}} = {\left| A \right|^{n - 1}} \cdot \frac{1}{{\left| A \right|}}A = {\left| A \right|^{n - 2}}A.由于{\left| A \right|^{n - 2}}是个定值,我们得知{(A^\ast)}^\astA的一个纯量倍.laugh


5.(25分) 设A是一个3阶实方阵,考虑A所定义的线性变换\mathbb{R}^3\to\mathbb{R}^3,\alpha\to A\alpha ( \alpha是列向量).试证明:若AA'=A'A (其中A'是指A的转置矩阵),则上述线性变换必有一个2维不变子空间.

 


6.(25分) 设AB是复数域\mathbb{C}上的两个n阶方阵,并且An个特征值1,2,\cdots,n, B也有n个特征值\sqrt{p_1},\cdots,\sqrt{p_n},其中p_1,\cdots,p_n是前n个素数(比如p_1=2,p_2=3等).试证明: M_n(\mathbb{C})上的线性变换X\to AXB是可以对角化的.

 

7.(25分) 设A = \left( {\begin{array}{*{20}{c}}{ - 2}&1&3\\{ - 2}&1&2\\{ - 1}&1&2\end{array}} \right),

试找出两个没有常数项的多项式f(x)\varphi(x),使得下列三个条件同时成立:
1). f(A)可对角化.    2). \varphi (A)是幂零矩阵.    3). A=f(A)+\varphi (A).
enlightened解:f\left( \lambda \right) = \left| {\lambda {I_n} - A} \right| = \left( {\lambda + 1} \right){\left( {\lambda - 1} \right)^2}.

由Cayley---Hamilton定理可知,f\left( A \right) = \left( {A + 1} \right){\left( {A - 1} \right)^2} = {A^3} - {A^2} - A + {I_n} = 0.

我们有

A = {A^3} + \left( {{A^2} - {A^4}} \right).f\left( x \right) = {x^3},\varphi \left( x \right) = {x^2} - {x^4}即可.laugh


8.几何部分共5道小题,每小题10分。

(1)三维欧氏空间中取定直角坐标系。有一直线l过点(1,0,0)且方向向量为(0,1,1)lz轴旋转生成一个二次曲面S。试写出此二次曲面的代数方程(形如f(x,y,z)=0).
(2)设有一固定平面\Sigma,具有以下性质:上述直线l在绕z轴旋转过程中总是与\Sigma相交。考虑与\Sigma平行的平面族\Sigma_t,t\in\mathbb{R},\Sigma_0=\Sigma。试证明\Sigma_t\cap S总是椭圆.
(3)试证明t值变化过程中,上述各椭圆的中心总落在一条过原点的空间定直线L上.
(4)固定L上任一点p,试证明:由p向曲面S作的各条切线的切点都落在一条椭圆\Gamma_p上,且椭圆\Gamma_p所在平面是\Sigma_t之一.
(5)S把它在空间的补集分成内外两个连通分支,其中外部区域不包含原点。取上一小题所述椭圆\Gamma所在平面落在S外部的一点\hat p。试证明:从\hat pS所作的各条切线之切点落在一条双曲线\hat \Gamma上,且\hat \Gamma所在平面过p点.
enlightened证:(1)记A(1,0,0),设直线上有一点B(x,y,z),则\overrightarrow {AB} = \left( {x - 1,y,z} \right)=t(0,1,1),则B(1,t,t).对于给定z=t,其绕z轴旋转形成的图形为{x^2} + {y^2} = {t^2} + 1 = {z^2} + 1,故该二次曲面方程为{x^2} + {y^2} - {z^2} - 1 = 0.

(2)设固定平面\Sigma的方程为Ax+By+Cz-a_t=0

 

  • A^2+B^2=0A=B=0时,方程退化成

z = \frac{{{a_t}}}{C},\left\{ \begin{array}{l}{x^2} + {y^2} - {z^2} = 1\\z = \frac{{{a_t}}}{C}\end{array} \right. \Rightarrow \frac{{{x^2}}}{{\frac{{a_t^2}}{{{C^2}}} + 1}} + \frac{{{y^2}}}{{\frac{{a_t^2}}{{{C^2}}} + 1}} = 1,

可知此时\Sigma_t\cap S为圆,当然可以看成是椭圆.

  • A^2+B^2\neq0时,作坐标系旋转

\left\{ \begin{array}{l}{x_1} = \frac{B}{{\sqrt {{A^2} + {B^2}} }}x - \frac{A}{{\sqrt {{A^2} + {B^2}} }}y\\{y_1} = - \frac{{AC}}{{\sqrt {\left( {{A^2} + {B^2}} \right)\left( {{A^2} + {B^2} + {C^2}} \right)} }}x - \frac{{BC}}{{\sqrt {\left( {{A^2} + {B^2}} \right)\left( {{A^2} + {B^2} + {C^2}} \right)} }}y + \frac{{{A^2} + {B^2}}}{{\sqrt {\left( {{A^2} + {B^2}} \right)\left( {{A^2} + {B^2} + {C^2}} \right)} }}z\\{z_1} = \frac{A}{{\sqrt {{A^2} + {B^2} + {C^2}} }}x + \frac{B}{{\sqrt {{A^2} + {B^2} + {C^2}} }}y + \frac{C}{{\sqrt {{A^2} + {B^2} + {C^2}} }}z\end{array} \right.

\left\{ \begin{array}{l}x = \frac{B}{{\sqrt {{A^2} + {B^2}} }}{x_1} - \frac{{AC}}{{\sqrt {\left( {{A^2} + {B^2}} \right)\left( {{A^2} + {B^2} + {C^2}} \right)} }}{y_1} + \frac{A}{{\sqrt {{A^2} + {B^2} + {C^2}} }}{z_1}\\y = - \frac{A}{{\sqrt {{A^2} + {B^2}} }}{x_1} - \frac{{BC}}{{\sqrt {\left( {{A^2} + {B^2}} \right)\left( {{A^2} + {B^2} + {C^2}} \right)} }}{y_1} + \frac{B}{{\sqrt {{A^2} + {B^2} + {C^2}} }}{z_1}\\z = \frac{{\sqrt {{A^2} + {B^2}} }}{{\sqrt {{A^2} + {B^2} + {C^2}} }}{y_1} + \frac{C}{{\sqrt {{A^2} + {B^2} + {C^2}} }}{z_1}\end{array} \right.,

该平面方程化为{z_1} = \frac{{{a_t}}}{{\sqrt {{A^2} + {B^2} + {C^2}} }}, S化为

x_1^2 + \frac{{{C^2} - {A^2} - {B^2}}}{{{A^2} + {B^2} + {C^2}}}y_1^2 - \frac{{{C^2} - {A^2} - {B^2}}}{{{A^2} + {B^2} + {C^2}}}z_1^2 - \frac{{4C\sqrt {{A^2} + {B^2}} }}{{{A^2} + {B^2} + {C^2}}} - 1 = 0,

则截面方程为

x_1^2 + \frac{{{C^2} - {A^2} - {B^2}}}{{{A^2} + {B^2} + {C^2}}}y_1^2 - \frac{{{C^2} - {A^2} - {B^2}}}{{{{\left( {{A^2} + {B^2} + {C^2}} \right)}^2}}}a_t^2 - \frac{{4C\sqrt {{A^2} + {B^2}} }}{{{A^2} + {B^2} + {C^2}}} - 1 = 0.显然\Sigma_t\cap S为椭圆.

 

综上可知, \Sigma_t\cap S总是椭圆.

(3)由(2)可知,在x_1y_1z_1坐标系中,椭圆的中心为

\left( {0,0,\frac{{{a_t}}}{{\sqrt {{A^2} + {B^2} + {C^2}} }}} \right),即中心在z_1轴上,\left\{ \begin{array}{l}{x_1} = \frac{B}{{\sqrt {{A^2} + {B^2}} }}x - \frac{A}{{\sqrt {{A^2} + {B^2}} }}y = 0\\{y_1} = - \frac{{AC}}{{\sqrt {\left( {{A^2} + {B^2}} \right)\left( {{A^2} + {B^2} + {C^2}} \right)} }}x - \frac{{BC}}{{\sqrt {\left( {{A^2} + {B^2}} \right)\left( {{A^2} + {B^2} + {C^2}} \right)} }}y + \frac{{{A^2} + {B^2}}}{{\sqrt {\left( {{A^2} + {B^2}} \right)\left( {{A^2} + {B^2} + {C^2}} \right)} }}z = 0\end{array} \right.,从而在xyz坐标系中,椭圆中心落在过原点的定直线

L: \left\{ \begin{array}{l}Bx - Ay = 0\\- ACx - BCy + \left( {{A^2} + {B^2}} \right)z = 0\end{array} \right.

上.

(4)设S: x_1^2 + \frac{{{C^2} - {A^2} - {B^2}}}{{{A^2} + {B^2} + {C^2}}}y_1^2 - \frac{{{C^2} - {A^2} - {B^2}}}{{{A^2} + {B^2} + {C^2}}}z_1^2 - \frac{{4C\sqrt {{A^2} + {B^2}} }}{{{A^2} + {B^2} + {C^2}}} - 1 = 0,上一点P\left( {{x_{1,0}},{y_{1,0}},{z_{1,0}}} \right),则曲面SP点处的切面为

\left( {{x_1} - {x_{1,0}}} \right) \cdot \left( {2{x_{1,0}}} \right) + \left( {{y_1} - {y_{1,0}}} \right) \cdot \left( {2\frac{{{C^2} - {A^2} - {B^2}}}{{{A^2} + {B^2} + {C^2}}}{y_{1,0}}} \right) + \left( {{z_1} - {z_{1,0}}} \right) \cdot \left( { - 2\frac{{{C^2} - {A^2} - {B^2}}}{{{A^2} + {B^2} + {C^2}}}{z_{1,0}}} \right) = 0,记坐标系xyz中,直线L上任一点px_1,y_1,z_1中的坐标为p_1(0,0,m),则p_1满足切面方程,即\left( {0 - {x_{1,0}}} \right) \cdot \left( {2{x_{1,0}}} \right) + \left( {0 - {y_{1,0}}} \right) \cdot \left( {2\frac{{{C^2} - {A^2} - {B^2}}}{{{A^2} + {B^2} + {C^2}}}{y_{1,0}}} \right) + \left( {m - {z_{1,0}}} \right) \cdot \left( { - 2\frac{{{C^2} - {A^2} - {B^2}}}{{{A^2} + {B^2} + {C^2}}}{z_{1,0}}} \right) = 0, - 2m\frac{{{C^2} - {A^2} - {B^2}}}{{{A^2} + {B^2} + {C^2}}}{z_{1,0}} - \frac{{8C\sqrt {{A^2} + {B^2}} }}{{{A^2} + {B^2} + {C^2}}} - 2 = 0 \Rightarrow {z_{1,0}} = \frac{{\left( {{A^2} + {B^2} + {C^2}} \right) + 4C\sqrt {{A^2} + {B^2}} }}{{m\left( {{A^2} + {B^2} - {C^2}} \right)}},故此时的Pz_1坐标为定值, 在x_1,y_1,z_1P形成的轨迹为椭圆,且对应在x,y,z\Sigma_t\cap S的一个椭圆.

(5)设x_1y_1z_1坐标系中, \Gamma_p: \left\{ \begin{array}{l}{z_1} = \frac{{\left( {{A^2} + {B^2} + {C^2}} \right) + 4C\sqrt {{A^2} + {B^2}} }}{{m\left( {{A^2} + {B^2} - {C^2}} \right)}}\\x_1^2 + \frac{{{C^2} - {A^2} - {B^2}}}{{{A^2} + {B^2} + {C^2}}}y_1^2 - \frac{{{C^2} - {A^2} - {B^2}}}{{{A^2} + {B^2} + {C^2}}}z_1^2 - \frac{{4C\sqrt {{A^2} + {B^2}} }}{{{A^2} + {B^2} + {C^2}}} - 1 = 0\end{array} \right.所在平面落在S外部的一点\hat p_1的坐标为\left( {{x_{1,1}},{y_{1,1}},{z_{1,1}}} \right), \hat p_1满足(4)中P点处的切面方程,即\left( {{x_{1,1}} - {x_{1,0}}} \right) \cdot \left( {2{x_{1,0}}} \right) + \left( {{y_{1,1}} - {y_{1,0}}} \right) \cdot \left( {2\frac{{{C^2} - {A^2} - {B^2}}}{{{A^2} + {B^2} + {C^2}}}{y_{1,0}}} \right) + \left( {{z_{1,1}} - {z_{1,0}}} \right) \cdot \left( { - 2\frac{{{C^2} - {A^2} - {B^2}}}{{{A^2} + {B^2} + {C^2}}}{z_{1,0}}} \right) = 0,

 

则有

2{x_{1,1}}{x_{1,0}} + 2\frac{{{C^2} - {A^2} - {B^2}}}{{{A^2} + {B^2} + {C^2}}}{y_{1,1}}{y_{1,0}} - \frac{{8C\sqrt {{A^2} + {B^2}} }}{{{A^2} + {B^2} + {C^2}}} - 2 + \frac{{2\left( {{A^2} + {B^2} + {C^2}} \right) + 8C\sqrt {{A^2} + {B^2}} }}{{m\left( {{A^2} + {B^2} - {C^2}} \right)}}{z_{1,0}} = 0.显然其经过p_1.进一步地,经过与之前类似的坐标轴旋转,我们知道P的轨迹落在一条双曲线上.


PS:这份试卷是考完后的第一天根据好友同学提供的资料进行整理的,感谢他们的辛劳,同时也祝贺他们在昨天下午清华的初试中获得成功。

 

一个很好的积分题

背景是这个:

 

 

 

 

 

 

 

 

 

 

 

 

 

 

然后郝XX跟我说了下他的求法,还是挺有意思的!


\int_0^{2\pi } {{e^{\cos x}}\cos \left( {\sin x} \right)\cos nxdx} .


由Euler公式,我们有

\begin{align*}&{e^{y\cos x}}\cos \left( {y\sin x} \right) + i{e^{y\cos x}}\sin \left( {y\sin x} \right) = {e^{y\cos x}} \cdot {e^{iy\sin x}}\\=& {e^{y\cos x + iy\sin x}} = {e^{y\left( {\cos x + i\sin x} \right)}} = \sum\limits_{n = 0}^{ + \infty } {\frac{{{y^n}}}{{n!}}{{\left( {\cos x + i\sin x} \right)}^n}} \\=& \sum\limits_{n = 0}^{ + \infty } {\frac{{{y^n}}}{{n!}}{{\left( {\cos x + i\sin x} \right)}^n}} = \sum\limits_{n = 0}^{ + \infty } {\frac{{{y^n}}}{{n!}}\left( {\cos nx + i\sin nx} \right)} \\= &\sum\limits_{n = 0}^{ + \infty } {\frac{{{y^n}\cos nx}}{{n!}}} + i\sum\limits_{n = 0}^{ + \infty } {\frac{{{y^n}\sin nx}}{{n!}}} .\end{align*}

因此有

{e^{y\cos x}}\cos \left( {y\sin x} \right) = \sum\limits_{n = 0}^{ + \infty } {\frac{{{y^n}\cos nx}}{{n!}}} ,{e^{y\cos x}}\sin \left( {y\sin x} \right) = \sum\limits_{n = 0}^{ + \infty } {\frac{{{y^n}\sin nx}}{{n!}}} .

在第一个式子中令y=1,我们有

{e^{\cos x}}\cos \left( {\sin x} \right) = \sum\limits_{n = 0}^{ + \infty } {\frac{{\cos nx}}{{n!}}} .

因此我们有

\begin{align*}&\int_0^{2\pi } {{e^{\cos x}}\cos \left( {\sin x} \right)\cos nxdx} = \int_0^{2\pi } {\left( {\cos nx \cdot \sum\limits_{n = 0}^{ + \infty } {\frac{{\cos nx}}{{n!}}} } \right)dx} \\=& \int_0^{2\pi } {\left( {\cos nx \cdot \frac{{\cos nx}}{{n!}}} \right)dx} = \left\{ \begin{array}{l}\frac{\pi }{{n!}},n \ge 1\\2\pi ,n = 0\end{array} \right..\end{align*}


而对于无穷乘积\prod\limits_{n = 3}^\infty  {\cos \frac{\pi }{{n!}}}  \approx 0.858314.也就是管理员一分钟都不能禁他!!!

事实上,我们有

\begin{align*}&\int_0^\pi  {{e^{p\cos x}}\cos \left( {p\sin x} \right)\cos qxdx}  = \int_0^\pi  {{e^{p\cos x}}\cos qx{\mathop{\rm Re}\nolimits} \left( {{e^{ip\sin x}}} \right)dx} \\= &\int_0^\pi  {\cos qx{\mathop{\rm Re}\nolimits} \left( {{e^{ip\sin x + p\cos x}}} \right)dx}  = \int_0^\pi  {\cos qx{\mathop{\rm Re}\nolimits} \left( {{e^{p{e^{ix}}}}} \right)dx} \\= &\int_0^\pi  {\cos qx{\mathop{\rm Re}\nolimits} \left( {\sum\limits_{k = 0}^\infty  {\frac{{{p^k}{e^{ikx}}}}{{k!}}} } \right)dx}  = \sum\limits_{k = 0}^\infty  {\frac{{{p^k}}}{{k!}}\int_0^\pi  {\cos qx\cos kxdx} } \\= &\frac{1}{2}\sum\limits_{k = 0}^\infty  {\frac{{{p^k}}}{{k!}}\left[ {\frac{{\sin \left( {k - q} \right)x}}{{k - q}} - \frac{{\sin \left( {k + q} \right)x}}{{k + q}}} \right]} _0^\pi  = \frac{\pi }{2}\frac{{{p^q}}}{{q!}}.\end{align*}

 

华东师范大学数学系精品课程主页

华东师范大学数学系精品课程主页http://math.ecnu.edu.cn/jpkc/

 

几个逼格稍高的积分级数题

先是证明两个积分成立:

\begin{align}\int_0^{ + \infty } {\frac{{\sin nx}}{{x + \frac{1}{{x + \frac{2}{{x + \frac{3}{{x +  \cdots }}}}}}}}dx}  &= \frac{{\sqrt {\frac{\pi }{2}} }}{{n + \frac{1}{{n + \frac{2}{{n + \frac{3}{{n +  \cdots }}}}}}}}\\\int_0^{ + \infty } {\frac{{\sin \frac{{n\pi x}}{2}}}{{x + \frac{{{1^2}}}{{x + \frac{{{2^2}}}{{x + \frac{{{3^2}}}{{x +  \cdots }}}}}}}}dx}  &= \frac{1}{{n + \frac{{{1^2}}}{{n + \frac{{{2^2}}}{{n + \frac{{{3^2}}}{{n +  \cdots }}}}}}}}.\end{align}

接着是两个级数题,判断它们是否成立:

\begin{align}\sum\limits_{n = 0}^{ + \infty } {\left[ {\left( {1 + \frac{1}{3} + \frac{1}{5} +  \cdots  + \frac{1}{{2n + 1}}} \right) \cdot \frac{1}{{{5^n}\left( {2n + 1} \right)}}} \right]}  &= \frac{{{\pi ^2}}}{{4\sqrt 5 }} - \frac{{\sqrt 5 }}{{24}}{\left( {\ln \left( {2 + \sqrt 5 } \right)} \right)^2}\\\sum\limits_{n = 0}^{ + \infty } {\left[ {\left( {1 + \frac{1}{3} + \frac{1}{5} +  \cdots  + \frac{1}{{2n + 1}}} \right) \cdot \frac{1}{{{9^n}\left( {2n + 1} \right)}}} \right]}  &= \frac{{{\pi ^2}}}{8} - \frac{3}{8}{\left( {\ln 2} \right)^2}\end{align}

2015年丘赛分析组个人赛试题

  1. Let f_n\in L^2(R) be a sequence of measurable functions over the line, f_n\rightarrow f almost everywhere. Let ||f_n||_{L^2}\rightarrow||f||_{L^2}, prove that ||f_n-f||_{L^2}\rightarrow 0.

    enlightenedProof.(Weingarten) L^2(\mathbf R) is a Hilbert space, and \|f_n\|_{L^2}\to\|f\|_{L^2} as n\to\infty , so we only need to prove f_n\in f weakly in L^2(\mathbf R), that is, for each g\in L^2(\mathbf R), there holds

    \lim_{n\to\infty}\int_{\mathbf R}f_ng=\int_{\mathbf R} fg.

    For each \epsilon>0, there exists R>0, such that \int_{|x|>R}|g|^2<\epsilon^2,

    and by the absolute continuity of integration of g, there exists a positive \delta, such that: for any Lebesgue measurable subset E of \mathbf R with m(E)<\delta, there holds \int_E|g|^2<\epsilon^2.

    By the Egoroff's thoerem, there exists a subset E_\delta of (-R,R) with m((-R,R)\setminus E_\delta)<\delta, such that the convergence \lim\limits_{n\to\infty}f_n=f is uniform on the E_\delta, so there exists N\in\mathbf Z_+, such that (\forall n>N,x\in E_\delta),(|f_n-f|<\epsilon/\sqrt{2R}).

    Assume M=\|g\|_{L^2}+\|f\|_{L^2}+\sup\limits_n\|f_n\|_{L^2}, hence \forall n>N, we have the following estimations

    \begin{align*}\int_{\mathbf R}|f_n-f|\cdot|g|&=\left(\int_{E_\delta}+\int_{(-R,R)\setminus E_\delta}+\int_{|x|>R}\right)|f_n-f|\cdot|g|\\&\leq\sqrt{\int_{E_\delta}|f_n-f|^2\cdot\int_{E_\delta}|g|^2}+\sqrt{\int_{(-R,R)\setminus E_\delta}|f_n-f|^2\cdot\int_{(-R,R)\setminus E_\delta}|g|^2}\\&+\sqrt{\int_{|x|>R}|f_n-f|^2\cdot\int_{|x|>R}|g|^2}\\&\leq M\epsilon+2\sqrt{2}M\epsilon.\end{align*}

    the proof is finished.


     

  2. Let f be a continuous function on [a,b], define M_n=\int ^b_a f(x)x^n{\rm d}x. Suppose that M_n=0 for all n, show that f(x)=0 for all x.
  3. Determine all entire functions f that satisfying the inequality |f(z)|\leq|z|^2|{\rm Im}(z)|^2 for z sufficlently large.
  4. Describe all holomorphic functions over the unit disk D=\{z||z|\leq 1\} which maps the boundary of the disk into the boundary of the disk.
  5. Let T:H_1\rightarrow H_2, Q:H_2\rightarrow H_1 be bounded linear operators of Hilbert spaces H_1,\ H_2. Let QT={\rm Id}-S_1,TQ={\rm Id}-S_2 where S_1 and S_2 are compact operators. Prove {\rm Ker}T=\{v\in H_1,Tv=0\},{\rm Coker}T=H_2/\overline{{\rm Im}T}, where {\rm Im}T=\{Tv\in H_2,v\in H_1\} are finite dimensional and {\rm Im}(T) is closed in H_2.
  6. Let H_1 be the Sobolev space on the unit interval [0,1], i.e. the Hilbert space consisting of functions f\in L^2([0,1]) such that ||f||_1^2=\sum^\infty_{n=-\infty}(1+n^2)|\hat f(n)|^2<\infty; where \hat f(n)=\frac 1 {2\pi}\int_0^1f(x)e^{-2\pi inx}{\rm d}x are Fourier coefficients of f. Show that there exists constant C>0 such that ||f||_{L^\infty}\leq C||f||_1 for all f\in H_1, where ||\cdot||_{L^\infty} stands for the usual supremum norm. (Hint: Use Fourier series.)

中科院李文威老师主页

通过一份李文威老师在国科大的讲义认识了他,里面有他的一些讲义。有兴趣的同学可去观摩:http://www.wwli.url.tw/index.php/zh-CN/teachingitem-zh-cn

北大本科06数分期中试题(李伟固命题)

前些年在百度文库(http://wenku.baidu.com/link?url=Y_HDeeYMcyEGuUJWt3fNqC7N08AEqMVfleNVGcv7hC2t9EVO0-MFFHWuqnLYyiDJ4H7ATgg1fOQGN1Lta2RW4Z4pOrm6aZ468WkrTqNqZzG)找到一份李伟固命制的06年北大期中考试题,当时感觉难度稍大,正好结识了Veer大神,便把这份题发给他。事实证明,V神秒得还是很顺利!

 

1.给定实数\lambda_i(1\leq i\leq n),满足\sum\limits_{i = 1}^n {\lambda _i^k} > 0\left( {k = 1,2,3, \cdots } \right).令f\left( x \right) = \prod\limits_{i = 1}^n {\frac{1}{{1 - {\lambda _i}x}}}.证明: f^{(k)}(0)>0,k=1,2,3,\cdots.

enlightened证明:令g\left( x \right) = \ln f\left( x \right) = - \ln \left( {1 - {\lambda _1}x} \right)-\ln \left( {1 - {\lambda _2}x} \right) -\cdots -\ln \left( {1 - {\lambda _n}x} \right)\left( {x \in U\left( {0;\delta } \right)\text{使得对}\forall {\lambda _i},\text{有}1 - {\lambda _i}x > 0} \right),则

g\left( x \right) = \left( {\sum\limits_{i = 1}^n {{\lambda _i}} } \right)x + \left( {\frac{1}{2}\sum\limits_{i = 1}^n {\lambda _i^2} } \right){x^2} + \cdots + \left( {\frac{1}{k}\sum\limits_{i = 1}^n {\lambda _i^k} } \right){x^k} + \cdots 由函数幂级数展开的唯一性可知{g^{\left( k \right)}}\left( 0 \right) = \frac{1}{k}\sum\limits_{i = 1}^n {\lambda _i^k} > 0\left( {x \in U\left( {0;\delta } \right)} \right).

 

另一方面f\left( x \right) = {e^{g\left( x \right)}}\left( {x \in U\left( {0;\delta } \right)} \right).首先注意到对任意可导函数F(x),有{\left( {{e^{F\left( x \right)}}} \right)^\prime } = F'\left( x \right){e^{F\left( x \right)}}.其次注意到对可导函数组F_1,F_2,\cdots,F_s,有{\left( {{F_1}{F_2}{F_3} \cdots {F_s}} \right)^\prime } = {{F'}_1}{F_2}{F_3} \cdots {F_s} + {F_1}{F_2}^\prime {F_3} \cdots {F_s} + \cdots + {F_1}{F_2}{F_3} \cdots {F_s}^\prime,从而归纳可证

{f^{\left( k \right)}}\left( x \right) = {\left( {{e^{g\left( x \right)}}} \right)^{\left( k \right)}} = \left( {\sum\limits_{j \in {N_ + },{k_i} \in {N_ + }} {{g^{\left( {{k_1}} \right)}}\left( x \right){g^{\left( {{k_2}} \right)}}\left( x \right) \cdots {g^{\left( {{k_j}} \right)}}\left( x \right)} } \right){e^{g\left( x \right)}}.{g^{\left( k \right)}}\left( 0 \right) > 0,k=1,2,3,\cdotsg(0)=0,所以{f^{\left( k \right)}}\left( 0 \right) > 0,k=1,2,3,\cdots.cool

补充:可知f\left( x \right) = \prod\limits_{i = 1}^n {\frac{1}{{1 - {\lambda _i}x}}} = \left( {1 + {\lambda _1}x + \lambda _1^2{x^2} + \cdots } \right)\left( {1 + {\lambda _2}x + \lambda _2^2{x^2} + \cdots } \right) \cdots \left( {1 + {\lambda _n}x + \lambda _n^2{x^2} + \cdots } \right).由幂级数的乘积公式归纳可得

{f^{\left( k \right)}}\left( 0 \right) = \sum\limits_{\substack{{k_1} + {k_2} + \cdots + {k_n} = k\\ \left( {{k_1},{k_2}, \cdots ,{k_n}} \right)}} {\lambda _1^{{k_1}}\lambda _2^{{k_2}} \cdots \lambda _n^{{k_n}}} \left( \text{其中}{{k_i} \in N} \right).若能通过\sum\limits_{i = 1}^n {\lambda _i^k} > 0\left( {k = 1,2,3, \cdots } \right)得出\sum\limits_{\substack{{k_1} + {k_2} + \cdots + {k_n} = k\\ \left( {{k_1},{k_2}, \cdots ,{k_n}} \right)}} {\lambda _1^{{k_1}}\lambda _2^{{k_2}} \cdots \lambda _n^{{k_n}}}>0即可得到证明.

 

 

2.令D = \left\{ {u = \left( {x,y} \right) \in {\mathbb{R}^2}\left| {\left\| u \right\| = \sqrt {{x^2} + {y^2}} \le \frac{1}{2}} \right.} \right\}. f(u)=f(x,y)是全平面上的连续可微函数满足\left\| {\nabla f\left( {0,0} \right)} \right\| = 1,\left\| {\nabla f\left( u \right) - \nabla f\left( v \right)} \right\| \le \left\| {u - v} \right\|.那么对于任意的u,v\in D,证明函数f|_DD中唯一点处达到其最大值.

enlightened证明:对u\in D,有\left\| {\nabla f\left( u \right) - \nabla f\left( {0,0} \right)} \right\| \le \left\| {u - \left( {0,0} \right)} \right\|,即

1 - \left\| {\nabla f\left( u \right)} \right\| = \left\| {\nabla f\left( {0,0} \right)} \right\| - \left\| {\nabla f\left( u \right)} \right\| \le \left\| {\nabla f\left( u \right) - \nabla f\left( {0,0} \right)} \right\| \le \left\| u \right\|,亦即1 - \left\| u \right\| \le \left\| {\nabla f\left( u \right)} \right\|,则\nabla f\left( u \right) \ne \left( {0,0} \right),所以f的最大值只可能在边界上取得.

 

易知f在其边界上的函数可设为g\left( t \right) = f\left( {\frac{1}{2}\cos \theta ,\frac{1}{2}\sin \theta } \right),g'\left( \theta \right) = \frac{1}{2}\left( { - \sin \theta {f_x} + \cos \theta {f_y}} \right),\theta \in \left[ {0,2\pi } \right).现假设f在其边界上有两点取得最大值,不妨设为\theta=\theta_1\theta=\theta_2,记{u_1} = \left( {\frac{1}{2}\cos {\theta _1},\frac{1}{2}\sin {\theta _1}} \right),{u_2} = \left( {\frac{1}{2}\cos {\theta _2},\frac{1}{2}\sin {\theta _2}} \right),则由g'\left( {{\theta _1}} \right) = g'\left( {{\theta _2}} \right) = 0可得- \sin {\theta _1}{f_x}\left( {{u_1}} \right) + \cos {\theta _1}{f_y}\left( {{u_1}} \right) = 0,即\nabla f\left( {{u_1}} \right)\left( { - \sin {\theta _1},\cos {\theta _1}} \right)垂直即可.设\nabla f\left( {{u_1}} \right) = {a_1}\left( {\cos {\theta _1},\sin {\theta _1}} \right),由于fu_1处取得最大值,则fu_1点沿方向(\cos\theta_1,\sin \theta_1)的方向导数需大于等于0,所以a_1\geq 0.

 

另一方面由\left\| {\nabla f\left( {0,0} \right)} \right\| - \left\| {\nabla f\left( {{u_1}} \right)} \right\| \le \left\| {\nabla f\left( {0,0} \right) - \nabla f\left( {{u_1}} \right)} \right\|当且仅当{\nabla f\left( {{0,0}} \right)}{\nabla f\left( {{u_1}} \right)}异向取等可知

\left\| {\nabla f\left( {0,0} \right)} \right\| - \left\| {{u_1}} \right\| \le \left\| {\nabla f\left( {{u_1}} \right)} \right\| = \left| {{a_1}} \right|,

a_1\geq \frac12.同理\nabla f\left( {{u_2}} \right) = {a_2}\left( {\cos {\theta _2},\sin {\theta _2}} \right),a_2\geq \frac12.由于不等式取等需要与{\nabla f\left( {0,0} \right)}异向,故a_1\geq\frac12,a_2\geq\frac12中有一个是严格的.不妨设为a_1>\frac12,再设\overrightarrow {{r_1}} = \left( {\cos {\theta _1},\sin {\theta _1}} \right),\overrightarrow {{r_2}} = \left( {\cos {\theta _2},\sin {\theta _2}} \right),则

 

\begin{align*}&{\left\| {\nabla f\left( {{u_1}} \right) - \nabla f\left( {{u_2}} \right)} \right\|^2} - {\left\| {{u_1} - {u_2}} \right\|^2}\\= &{\left\| {{a_1}\overrightarrow {{r_1}} - {a_2}\overrightarrow {{r_2}} } \right\|^2} - {\left\| {\frac{1}{2}\overrightarrow {{r_1}} - \frac{1}{2}\overrightarrow {{r_2}} } \right\|^2} = a_1^2 + a_2^2 - 2{a_1}{a_2}\overrightarrow {{r_1}} \overrightarrow {{r_2}} - \left( {\frac{1}{4} - \frac{1}{2}\overrightarrow {{r_1}} \overrightarrow {{r_2}} + \frac{1}{4}} \right)\\= &a_1^2 + a_2^2 - \frac{1}{2} - \left( {2{a_1}{a_2} - \frac{1}{2}} \right)\overrightarrow {{r_1}} \overrightarrow {{r_2}} \left( \text{由于}{\overrightarrow {{r_1}}\text{与} \overrightarrow {{r_2}} \text{不同向},\text{所以}\overrightarrow {{r_1}} \overrightarrow {{r_2}} < 1,\text{且}2{a_1}{a_2} - \frac{1}{2} > 0} \right)\\> &a_1^2 + a_2^2 - \frac{1}{2} - \left( {2{a_1}{a_2} - \frac{1}{2}} \right) = {\left( {{a_1} - {a_2}} \right)^2} \ge 0,\end{align*}

 

因此{\left\| {\nabla f\left( {{u_1}} \right) - \nabla f\left( {{u_2}} \right)} \right\|^2} - {\left\| {{u_1} - {u_2}} \right\|^2} > 0,即\left\| {\nabla f\left( {{u_1}} \right) - \nabla f\left( {{u_2}} \right)} \right\| > \left\| {{u_1} - {u_2}} \right\|,矛盾,从而f在边界上只有一点取得最大值,即函数f|_DD中唯一点处达到其最大值.cool

 

3.讨论级数\sum\limits_{n = 1}^{ + \infty } {{{\left( { - 1} \right)}^{n - 1}}\frac{{\sin \left( {\ln n} \right)}}{{{n^\alpha }}}},\alpha\in \mathbb{R}的收敛性.

 

enlightened解:(1)当\alpha\leq0时,我们可知\mathop {\lim }\limits_{n \to \infty } {\left( { - 1} \right)^{n - 1}}\frac{{\sin \left( {\ln n} \right)}}{{{n^\alpha }}}发散,从而级数\sum\limits_{n = 1}^{ + \infty } {{{\left( { - 1} \right)}^{n - 1}}\frac{{\sin \left( {\ln n} \right)}}{{{n^\alpha }}}}发散.

 

(2)当\alpha>0时,由于\mathop {\lim }\limits_{n \to \infty } {\left( { - 1} \right)^{n - 1}}\frac{{\sin \left( {\ln n} \right)}}{{{n^\alpha }}} = 0,我们讨论其前2n项和数列的收敛性即可,也就是级数\sum\limits_{n = 1}^{ + \infty } {\left[ {\frac{{\sin \left( {\ln \left( {2k - 1} \right)} \right)}}{{{{\left( {2k - 1} \right)}^\alpha }}} - \frac{{\sin \left( {\ln \left( {2k} \right)} \right)}}{{{{\left( {2k} \right)}^\alpha }}}} \right]}的收敛性.

 

\begin{align*}&\left| {\frac{{\sin \left( {\ln \left( {2k - 1} \right)} \right)}}{{{{\left( {2k - 1} \right)}^\alpha }}} - \frac{{\sin \left( {\ln \left( {2k} \right)} \right)}}{{{{\left( {2k} \right)}^\alpha }}}} \right|\\= &\left| {\sin \left( {\ln \left( {2k - 1} \right)} \right)\left( {\frac{1}{{{{\left( {2k - 1} \right)}^\alpha }}} - \frac{1}{{{{\left( {2k} \right)}^\alpha }}}} \right) + \frac{{\sin \left( {\ln \left( {2k - 1} \right)} \right) - \sin \left( {\ln \left( {2k} \right)} \right)}}{{{{\left( {2k} \right)}^\alpha }}}} \right|\\\le &\left| {\sin \left( {\ln \left( {2k - 1} \right)} \right)} \right|\left[ {\frac{1}{{{{\left( {2k - 1} \right)}^\alpha }}} - \frac{1}{{{{\left( {2k} \right)}^\alpha }}}} \right] + \left| {\frac{{2\cos \left[ {\frac{{\ln \left( {2k - 1} \right) + \ln \left( {2k} \right)}}{2}} \right]\sin \left[ {\frac{{\ln \left( {2k - 1} \right) - \ln \left( {2k} \right)}}{2}} \right]}}{{{{\left( {2k} \right)}^\alpha }}}} \right|\\\le &\left[ {\frac{1}{{{{\left( {2k - 1} \right)}^\alpha }}} - \frac{1}{{{{\left( {2k} \right)}^\alpha }}}} \right] + \left| {\ln \left( {1 - \frac{1}{{2k}}} \right)} \right|\frac{1}{{{{\left( {2k} \right)}^\alpha}}}.\end{align*}

已知\left| {\ln \left( {1 - \frac{1}{{2k}}} \right)} \right|\frac{1}{{{{\left( {2k} \right)}^\alpha }}} \sim \frac{1}{{{{\left( {2k} \right)}^{\alpha + 1}}}},从而\sum\limits_{k = 1}^{ + \infty } {\left| {\ln \left( {1 - \frac{1}{{2k}}} \right)} \right|\frac{1}{{{{\left( {2k} \right)}^\alpha }}}}收敛.又\sum\limits_{k = 1}^{ + \infty } {\left[ {\frac{1}{{{{\left( {2k - 1} \right)}^\alpha }}} - \frac{1}{{{{\left( {2k} \right)}^\alpha }}}} \right]}是收敛的,故\sum\limits_{n = 1}^{ + \infty } {\left| {\frac{{\sin \left( {\ln \left( {2k - 1} \right)} \right)}}{{{{\left( {2k - 1} \right)}^\alpha }}} - \frac{{\sin \left( {\ln \left( {2k} \right)} \right)}}{{{{\left( {2k} \right)}^\alpha }}}} \right|}收敛,因此\sum\limits_{n = 1}^{ + \infty } {\left[ {\frac{{\sin \left( {\ln \left( {2k - 1} \right)} \right)}}{{{{\left( {2k - 1} \right)}^\alpha }}} - \frac{{\sin \left( {\ln \left( {2k} \right)} \right)}}{{{{\left( {2k} \right)}^\alpha }}}} \right]}亦收敛.

综上所述, 当\alpha\leq 0时,级数发散;当\alpha>0时级数收敛.cool

 

3.求\mathop {\inf }\limits_{n \ge 1} \left\{ {\mathop {\min }\limits_{x \in \left[ {0,\frac{\pi }{2}} \right]} \sum\limits_{k = 1}^n {\frac{{\cos kx}}{k}} } \right\}.

enlightened解:首先, \mathop {\min }\limits_{x \in \left[ {0,\frac{\pi }{2}} \right]} \sum\limits_{k = 1}^1 {\frac{{\cos kx}}{k}} = \mathop {\min }\limits_{x \in \left[ {0,\frac{\pi }{2}} \right]} \cos x = 0,\mathop {\min }\limits_{x \in \left[ {0,\frac{\pi }{2}} \right]} \sum\limits_{k = 1}^2 {\frac{{\cos kx}}{k}} = \mathop {\min }\limits_{x \in \left[ {0,\frac{\pi }{2}} \right]} \left( {\cos x + \frac{{\cos 2x}}{2}} \right) = - \frac{1}{2}.

 

现记{f_n}\left( x \right) = \sum\limits_{k = 1}^n {\frac{{\cos kx}}{k}},则\mathop {\min }\limits_{x \in \left[ {0,\frac{\pi }{2}} \right]} {f_1}\left( x \right) = 0 \ge - \frac{1}{2},\mathop {\min }\limits_{x \in \left[ {0,\frac{\pi }{2}} \right]} {f_1}\left( x \right) = - \frac{1}{2} \ge - \frac{1}{2}.

 

现假设n=k-1(k\geq2,k\in\mathbb{N}_+)时,有\mathop {\min }\limits_{x \in \left[ {0,\frac{\pi }{2}} \right]} {f_n}\left( x \right) = \mathop {\min }\limits_{x \in \left[ {0,\frac{\pi }{2}} \right]} {f_{k - 1}}\left( x \right) \ge - \frac{1}{2}成立;

 

n=k时,注意到{f_n}^\prime \left( x \right) = \sum\limits_{k = 1}^n {\left( { - \sin kx} \right)} = \sum\limits_{i = 1}^k {\left( { - \sin ix} \right)} = \sum\limits_{i = 1}^k {\frac{{\cos \left( {i + \frac{1}{2}} \right)x - \cos \left( {i - \frac{1}{2}} \right)x}}{{2\sin \frac{x}{2}}}} ,

{f_k}^\prime \left( x \right) = \frac{{\cos \left( {k + \frac{1}{2}} \right)x - \cos \frac{x}{2}}}{{2\sin \frac{x}{2}}} = - \frac{{\sin \frac{{k + 1}}{2}x\sin \frac{k}{2}x}}{{\sin \frac{x}{2}}},x \in \left[ {0,\frac{\pi }{2}} \right].

f_k(x)的连续性可知\min f_k(x)的点只可能在端点或稳定点,从而令f'_k(x)=0,则x = \frac{{2j\pi }}{{k + 1}},\frac{{2j\pi }}{k},j \in \mathbb{Z}\frac{{2j\pi }}{{k + 1}},\frac{{2j\pi }}{k} \in \left( {0,\frac{\pi }{2}} \right),而

{f_k}\left( {\frac{{2j\pi }}{{k + 1}}} \right) = \sum\limits_{i = 1}^n {\frac{{\cos i\left( {\frac{{2j\pi }}{{k + 1}}} \right)}}{i}} = {f_{k - 1}}\left( {\frac{{2j\pi }}{{k + 1}}} \right) + \frac{{\cos \frac{{2kj\pi }}{{k + 1}}}}{k} = {f_{k - 1}}\left( {\frac{{2j\pi }}{{k + 1}}} \right) + \frac{{\cos \frac{{2j\pi }}{{k + 1}}}}{k}.

由于\frac{{2j\pi }}{{k + 1}} \in \left[ {0,\frac{\pi }{2}} \right],所以\frac{{\cos \frac{{2j\pi }}{{k + 1}}}}{k} > 0,则{f_k}\left( {\frac{{2j\pi }}{{k + 1}}} \right) = {f_{k - 1}}\left( {\frac{{2j\pi }}{{k + 1}}} \right) + \frac{{\cos \frac{{2j\pi }}{{k + 1}}}}{k} \ge - \frac{1}{2}.

{f_k}\left( {\frac{{2j\pi }}{k}} \right) = \sum\limits_{i = 1}^n {\frac{{\cos i\left( {\frac{{2j\pi }}{k}} \right)}}{i}} = {f_{k - 1}}\left( {\frac{{2j\pi }}{k}} \right) + \frac{1}{k} \ge - \frac{1}{2},又

{f_k}\left( 0 \right) = \sum\limits_{i = 1}^k {\frac{1}{i}} > - \frac{1}{2},{f_k}\left( {\frac{\pi }{2}} \right) = \sum\limits_{i = 1}^k {\frac{{\cos i\frac{\pi }{2}}}{i}} = \left\{ \begin{array}{l}\frac{1}{2}\left[ { - 1 + \left({\frac{1}{2} - \frac{1}{3}} \right) + \cdots + \left( {\frac{1}{{s - 1}} - \frac{1}{s}} \right)} \right],k = 2s + 1\\\frac{1}{2}\left[ { - 1 + \left( {\frac{1}{2} - \frac{1}{3}} \right) + \cdots + \left( {\frac{1}{{s - 1}} - \frac{1}{s}} \right)} \right],k = 2s\end{array} \right.,从而{f_k}\left( {\frac{\pi }{2}} \right) \ge - \frac{1}{2}.

 

综上归纳可知\mathop {\min }\limits_{x \in \left[ {0,\frac{\pi }{2}} \right]} {f_n}\left( x \right) = \mathop {\min }\limits_{x \in \left[ {0,\frac{\pi }{2}} \right]} \sum\limits_{k = 1}^n {\frac{{\cos kx}}{k}} \ge - \frac{1}{2},又\mathop {\min }\limits_{x \in \left[ {0,\frac{\pi }{2}} \right]} \sum\limits_{k = 1}^2 {\frac{{\cos kx}}{k}} = - \frac{1}{2},因此

\mathop {\inf }\limits_{n \ge 1} \left\{ {\mathop {\min }\limits_{x \in \left[ {0,\frac{\pi }{2}} \right]} \sum\limits_{k = 1}^n {\frac{{\cos kx}}{k}} } \right\} = - \frac{1}{2}.

cool

 

4.函数f(x)[0,1]上二次可导, f(0)=2,f'(0)=-2,f(1)=1.证明存在c\in (0,1),使得f(c)f'(c)+f''(c)=0.

enlightened证明:令F\left( x \right) = \frac{1}{2}{f^2}\left( x \right) + f'\left( x \right),则F(0)=\frac12\times 2^2-2=0.现假设不存在\xi\in (0,1]使得F(\xi)=0,则F(0,1]上不变号.倘若\forall x\in (0,1]都有F(x)<0,即\frac{1}{2}{f^2}\left( x \right) + f'\left( x \right) < 0,则f'\left( x \right) < - \frac{1}{2}{f^2}\left( x \right) \le 0,则f[0,1]上单调递减,由f(0)=2,f(1)=1可知1\leq f(x)\leq 2,f(x)\neq 0,从而\frac{{f'\left( x \right)}}{{{f^2}\left( x \right)}} < - \frac{1}{2}.由f,f'的连续性可知\int_0^1 {\frac{{f'\left( x \right)}}{{{f^2}\left( x \right)}}dx} < \int_0^1 {\left( { - \frac{1}{2}} \right)dx},即\left. { - \frac{1}{{f\left( x \right)}}} \right|_0^1 < - \frac{1}{2},得到-\frac12<-frac12,矛盾.

 

倘若\forall x\in (0,1]都有F(x)>0,即\frac{1}{2}{f^2}\left( x \right) + f'\left( x \right) > 0,由于f(1)=1\neq 0,则\forall x\in U^-(1,\delta),有\int_x^1 {\frac{{f'\left( t \right)}}{{{f^2}\left( t \right)1}}dt} > \int_x^1 {\left( { - \frac{1}{2}} \right)dt},即\left. { - \frac{1}{{f\left( t \right)}}} \right|_x^1 > \frac{{x - 1}}{2},从而\frac{1}{{f\left( x \right)}} > \frac{{x + 1}}{2}.

 

x\in U^-(1,\delta)时,有\frac{1}{{f\left( x \right)}} > \frac{{x + 1}}{2} > 0\left( {\delta \le 1} \right),故f(x)[0,1]上不为0,则\frac{1}{{f\left( 0 \right)}} > \frac{{0 + 1}}{2},则\frac12>\frac12,矛盾.

 

因此存在\xi\in (0,1]使得F(\xi)=0,从而F(0)=F(\xi)=0,由罗尔定理可知\exists c\in (0,1),使得

F'\left( c \right) = f\left( c \right)f'\left( c \right) + f''\left( c \right) = 0.

cool

 

5.AB是自然数\mathbb{N}的两个无穷子集,满足A\cap B=\text{空集},A\cup B=\mathbb{N},对于任意的自然数c>0,是否存在两个递增的数列\{a_n\},\{b_n\},\{a_n\}\in A,\{b_n\}\in B,使得\mathop {\lim }\limits_{n \to \infty } \frac{{{a_n}}}{{{b_n}}} = c.

enlightened证明:对\forall l\geq s\in \mathbb{N}_+.记s\sim l=\{s,s+1,\cdots,l\},则若对\forall N\in \mathbb{N}_+, \exists n_2\geq n_1\geq N\geq c^2x_{n_1}\sim x_{n_2}\subset B.令x_{n_1}=v_1,x_{n_2}=u_1,则v_1\sim u_1\subset B.若cv_1\sim cu_1+c[\sqrt{u_1}+1]\not\subset B,则\exists a\in A,b\in B,使得|a-cb|\leq c|a-cb|\leq \sqrt{b}+1.

 

cv_1\sim cu_1+c[\sqrt{u_1}+1]\subset B,令u_2=u_1+[\sqrt{u_1}+1],则cv_1\sim cu_2\subset B.以此类推,若始终成立c^kv_1 \sim c^k u_{k+1}\subset B,易知u_{k+1}=u_k+[\sqrt{u_k}+1]\geq u_k+\sqrt{u_1},即u_{k+1}-u_k\geq \sqrt{u_1},所以u_k有趋于无穷大的趋势,所以存在某个k_0\in \mathbb{N}_+,使得\frac{u_{k_0}}{v_1}>c.而c^{k_0}v_1\sim c^{k_0} u_{k_0+1}\subset B.令x_{n_{k_0}}\in B满足c^{k_0}v_1\sim c^{k_0}u_{k_0+1}\subset c^{k_0}v_1\sim x_{n_{k_0}}\subset B,x_{n_{k_0}}+1\in A,令a_1=x_{n_{k_0}}+1,则c^{k_0}v_1<\frac{a_1}{c}\leq x_{n_{k_0}+1},则\exists b_1\in B,使得\left| {\frac{{{a_1}}}{c} - {b_1}} \right| \le 1,即\left| {{a_1} - c{b_1}} \right| \le c.

 

若不始终成立c^kv_1\sim c^ku_{k+1},则\exists b_1\in B,a_1\in A|a_1-cb_1|\leq c|a_1-cb_1|\leq \sqrt{b_1}+1,即|a_1-cb_1|\leq \sqrt{a_1}+1.接着再取n_1\geq n_2\geq N,有x_{n_2}\geq x_{n_1}>a_1,b_1x_{n_1}\sim x_{n_2}\subset B,则\exists a_2\in A,b_2\in B|a_2-cb_2|\leq \sqrt{b_2}+1,\cdots,故存在递增数列\{a_n\}\subset A,\{b_n\}\subset B,有|a_n-cb_n|\leq \sqrt{b_n}+1,则\left| {\frac{{{a_n}}}{{{b_n}}} - c} \right| \le \frac{1}{{\sqrt {{b_n}} }} + \frac{1}{{{b_n}}},所以\mathop {\lim }\limits_{n \to \infty } \frac{{{a_n}}}{{{b_n}}} = c.cool

碰到的数学分析中的反例

  • \mathbb{R}上连续的有界函数一定一致连续吗?
kiss考察函数f(x)=\sin x^2.
  • 设函数f(x)在区间[a,b]上可微,则f'(x)[a,b]上有界?
kiss考察函数f(x)=x^{\frac32}\sin\frac1x.
  • 存不存在这样函数, f(a)=0, f(x)在区间[a,b]上连续,在区间(a,b)上可导且f(x)>0,而对任意\varepsilon>0,函数在区间(a,a+\varepsilon)是不单调递增的?
kiss考察函数x\sin \frac1x+10x.
  • 是否存在仅在一点可导而在该点之外的每一点都不可导的函数?
kiss考察函数f\left( x \right) = \left\{ \begin{array}{l}{x^2},x\text{为无理数}\\0,x\text{为有理数}\end{array} \right.在点x=0即可.