Processing math: 6%

2020年10月5日 星期一

國立臺灣大學一百零八學年度轉學生入學考試試題詳解

※注意:請於試卷上「非選擇題作答區」標明題號並依序作答。

  1. (25 points) Let A=(21211171582624123). Find the Jordan canonical form of A. Compute exp(tA) and derive the general solution to x(t)=Ax(t), where x(t) is a 4-dimensional column vector.
  2. 訣竅按照 Jordan canonical form 的求法計算之,其中注意到每行加總的值相同,故可據此簡化特徵多項式的計算。
    解法首先計算 A 的特徵多項式如下

    chA(x)=det

    計算特徵向量與廣義特徵向量如下
    • x=2,則 A-2I=\begin{bmatrix}-4&-1&2&-1\\11&5&-1&5\\-8&-2&4&-2\\4&1&-2&1\end{bmatrix},如此可取 v_1=\begin{bmatrix}1\\-2\\1\\0\end{bmatrix}v_2=\begin{bmatrix}0\\-1\\0\\1\end{bmatrix} 滿足 Av_1=2v_1Av_2=2v_2
    • x=5,則 A-5I=\begin{bmatrix}-7&-1&2&-1\\11&2&-1&5\\-8&-2&1&-2\\4&1&-2&-2\end{bmatrix},如此可取 v_3=\begin{bmatrix}-1\\2\\-2\\1\end{bmatrix} 滿足 Av_3=5v_3。進一步地,我們可取 v_4=\begin{bmatrix}0\\1\\0\\0\end{bmatrix} 滿足 \left(A-5I\right)v_4=v_3
    至此我們取 Q=\begin{bmatrix}v_1&v_2&v_3&v_4\end{bmatrix}=\begin{bmatrix}1&0&-1&0\\-2&-1&2&1\\1&0&-2&0\\0&1&1&0\end{bmatrix}Q^{-1}AQ=J:=\begin{bmatrix}2&0&0&0\\0&2&0&0\\0&0&5&1\\0&0&0&5\end{bmatrix}

    現在我們來計算 e^{tA} 如下:

    \begin{aligned}e^{tA}&=Qe^{tJ}Q^{-1}=\begin{bmatrix}1&0&-1&0\\-2&-1&2&1\\1&0&-2&0\\0&1&1&0\end{bmatrix}\begin{bmatrix}e^{2t}&0&0&0\\0&e^{2t}&0&0\\0&0&e^{5t}&te^{5t}\\0&0&0&e^{5t}\end{bmatrix}\begin{bmatrix}2&0&-1&0\\1&0&-1&1\\1&0&-1&0\\1&1&1&1\end{bmatrix}\\&=\begin{bmatrix}2e^{2t}-e^{5t}-te^{5t}&-te^{5t}&-e^{2t}+e^{5t}-te^{5t}&-te^{5t}\\-5e^{2t}+2e^{5t}+2te^{5t}&e^{5t}+2te^{5t}&-3e^{2t}-e^{5t}+2te^{5t}&-e^{2t}+e^{5t}+2te^{5t}\\2e^{2t}-2e^{5t}-2te^{5t}&-2te^{5t}&-e^{2t}+2e^{5t}-2te^{5t}&-2te^{5t}\\e^{2t}+e^{5t}+te^{5t}&te^{5t}&-e^{2t}-e^{5t}+te^{5t}&e^{2t}+te^{5t}\end{bmatrix}\end{aligned}

    因此聯立微分方程組的通解為

    x\left(t\right)=e^{tA}c=c_1\begin{bmatrix}2e^{2t}-e^{5t}-te^{5t}\\-5e^{2t}+2e^{5t}+2te^{5t}\\2e^{2t}-2e^{5t}-2te^{5t}\\e^{2t}+e^{5t}+te^{5t}\end{bmatrix}+c_2\begin{bmatrix}-te^{5t}\\e^{5t}+2te^{5t}\\-2te^{5t}\\te^{5t}\end{bmatrix}+c_3\begin{bmatrix}-e^{2t}+e^{5t}-te^{5t}\\-3e^{2t}-e^{5t}+2te^{5t}\\-e^{2t}+2e^{5t}-2te^{5t}\\-e^{2t}-e^{5t}+te^{5t}\end{bmatrix}+c_4\begin{bmatrix}-te^{5t}\\-e^{2t}+e^{5t}+2te^{5t}\\-2t^{5t}\\e^{2t}+te^{5t}\end{bmatrix}.


  3. (25 points) Let A and B be any n\times n complex matrices. Show that \exp\left(A\right)\exp\left(B\right)=\exp\left(A+B\right) if A and B commute.
    Hint: You may consider the norm \left\|C\right\|=\max\left\{\left|C_{ij}\right|\right\} for C=\left(C_{ij}\right)\in M_n\left(\mathbb{C}\right) and the remainder term

    \displaystyle R_p=\sum_{i=0}^{2p}\frac{\left(A+B\right)^i}{i!}-\sum_{j=0}^p\frac{A^j}{j!}\sum_{k=0}^p\frac{B^k}{k!}.

  4. 訣竅按照提示估算矩陣指數的誤差,從而確認兩端的誤差隨次數的增加而趨於零。
    解法考慮等式左右兩端的差的前 2p+1 項的值為

    \displaystyle R_p=\sum_{i=0}^{2p}\frac{\left(A+B\right)^i}{i!}-\sum_{j=0}^p\frac{A^j}{j!}\sum_{k=0}^p\frac{B^k}{k!}=\sum_{i=0}^{2p}\sum_{s=0}^i\frac{A^sB^{i-s}}{s!\left(i-s\right)!}-\sum_{j=0}^p\frac{A^j}{j!}\sum_{k=0}^p\frac{B^k}{k!}

    此時注意第一項運用變數變換(令 s=ji=j+k),可拆為三項如下

    \displaystyle\sum_{i=0}^{2p}\sum_{s=0}^i\frac{A^sB^{i-s}}{s!\left(i-s\right)!}=\sum_{j=0}^{2p}\sum_{k=0}^{2p-j}\frac{A^jB^k}{j!k!}=\sum_{j=0}^p\sum_{k=0}^p\frac{A^jB^k}{j!k!}+\sum_{j=p+1}^{2p}\sum_{k=0}^{2p-j}\frac{A^jB^k}{j!k!}+\sum_{j=0}^p\sum_{k=p+1}^{2p-j}\frac{A^jB^k}{j!k!}

    因此

    \displaystyle R_p=\sum_{j=p+1}^{2p}\frac{A^j}{j!}\sum_{k=0}^{2p-j}\frac{B^k}{k!}+\sum_{j=0}^p\frac{A^j}{j!}\sum_{k=p+1}^{2p-j}\frac{B^k}{k!}

    至此,取 C=\max\left\{\left\|A\right\|,\left\|B\right\|,1\right\},那麼由 \left\|AB\right\|\leq n\left\|A\right\|\left\|B\right\|,可以發現

    \displaystyle\left\|R_p\right\|\leq\sum_{j=p+1}^{2p}\sum_{k=0}^{2p-j}\frac{n^{j+k}C^{j+k}}{j!k!}+\sum_{j=0}^p\sum_{k=p+1}^{2p-j}\frac{n^{j+k}C^{j+k}}{j!k!}\leq\frac{p\left(p+1\right)n^{2p}C^{2p}}{\left(p+1\right)!}\leq\frac{n^{2p}C^{2p}}{\left(p-1\right)!}\to0~~\mbox{as}~p\to\infty.


  5. (25 points) Show that

    \displaystyle\det\begin{pmatrix}X_0&X_1&X_2&\cdots&X_{n-1}\\X_{n-1}&X_0&X_1&\cdots&X_{n-2}\\\vdots&\vdots&\vdots&\ddots&\vdots\\X_1&X_2&X_3&\cdots&X_0\end{pmatrix}=\prod_{j=0}^{n-1}\left(\sum_{k=0}^{n-1}\zeta^{jk}X_k\right)

    where \zeta is a primitive n-th root of unity.
    Hint: You may first compute, for example, \begin{pmatrix}X_0&X_1&X_2&X_3\\X_3&X_0&X_1&X_2\\X_2&X_3&X_0&X_1\\X_1&X_2&X_3&X_0\end{pmatrix}\begin{pmatrix}1&1&1&1\\1&\zeta&\zeta^2&\zeta^3\\1&\zeta^2&\zeta^4&\zeta^6\\1&\zeta^3&\zeta^6&\zeta^9\end{pmatrix}.
  6. 訣竅藉由提示與所欲證的等式考慮到特徵值的乘積。
    解法設欲求行列式的矩陣為 X 以及 Z=\left[z_{ij}\right],其中 z_{ij}=\zeta^{\left(i-1\right)\left(j-1\right)}。此外方便起見約定 X_k:=X_{n+k},其中 -\left(n-1\right)\leq k\leq-1。那麼觀察 XZ 的第 j 行可知

    \begin{aligned}X\begin{bmatrix}\zeta^{\left(1-1\right)\left(j-1\right)}\\\zeta^{\left(2-1\right)\left(j-1\right)}\\\zeta^{\left(3-1\right)\left(j-1\right)}\\\vdots\\\zeta^{\left(n-1\right)\left(j-1\right)}\end{bmatrix}&=\left[\begin{array}{c}\sum\limits_{i=1}^nX_{i-1}\zeta^{\left(i-1\right)\left(j-1\right)}\\\sum\limits_{i=1}^nX_{i-2}\zeta^{\left(i-1\right)\left(j-1\right)}\\\sum\limits_{i=1}^nX_{i-3}\zeta^{\left(i-1\right)\left(j-1\right)}\\\vdots\\\sum\limits_{i=1}^nX_{i-n}\zeta^{\left(i-1\right)\left(j-1\right)}\end{array}\right]=\left[\begin{array}{c}\sum\limits_{i=1}^nX_{i-1}\zeta^{\left(i-1\right)\left(j-1\right)}\\\sum\limits_{i=0}^{n-1}X_{i-1}\zeta^{\left(i-1\right)\left(j-1\right)}\zeta^{j-1}\\\sum\limits_{i=-1}^{n-2}X_{i-1}\zeta^{\left(i-1\right)\left(j-1\right)}\zeta^{2\left(j-1\right)}\\\vdots\\\sum\limits_{i=-n+2}^1X_{i-1}\zeta^{\left(i-1\right)\left(j-1\right)}\zeta^{\left(n-1\right)\left(j-1\right)}\end{array}\right]\\&=\left(\sum_{i=1}^nX_{i-1}\zeta^{\left(i-1\right)\left(j-1\right)}\right)\begin{bmatrix}\zeta^{\left(1-1\right)\left(j-1\right)}\\\zeta^{\left(2-1\right)\left(j-1\right)}\\\zeta^{\left(3-1\right)\left(j-1\right)}\\\vdots\\\zeta^{\left(n-1\right)\left(j-1\right)}\end{bmatrix}=\left(\sum_{i=0}^{n-1}X_i\zeta^{i\left(j-1\right)}\right)\begin{bmatrix}\zeta^{\left(1-1\right)\left(j-1\right)}\\\zeta^{\left(2-1\right)\left(j-1\right)}\\\zeta^{\left(3-1\right)\left(j-1\right)}\\\vdots\\\zeta^{\left(n-1\right)\left(j-1\right)}\end{bmatrix}\end{aligned}

    因此對每個 1\leq j\leq n 而言,\sum\limits_{i=0}^{n-1}X_i\zeta^{i\left(j-1\right)} 都是特徵值。類似地,改變編號可知對於 0\leq j\leq n-1 而言,\sum\limits_{i=0}^{n-1}X_i\zeta^{ij} 都是特徵值。又行列式值為所有特徵值之乘積,故

    \displaystyle\det\left(X\right)=\prod_{j=0}^{n-1}\left(\sum_{i=0}^{n-1}\zeta^{ij}X_i\right).

    證明完畢。

  7. (25 points) Let V and W be vector spaces over the same field F, and let T:V\to W be a linear transformation. Let \mathcal{B}\left(V\right) and \mathcal{B}\left(W\right) denote the spaces of bilinear forms on V and W respectively. For any H\in\mathcal{B}\left(W\right), define \hat{T}\left(H\right):V\times V\to F by \hat{T}\left(H\right)\left(x,y\right)=H\left(T\left(x\right),T\left(y\right)\right) for any x,y\in V.
    1. Show that \hat{T}\left(H\right) is a bilinear form on V.
    2. Show that if T is an isomorphism, then so is \hat{T}:\mathcal{B}\left(W\right)\to\mathcal{B}\left(V\right).
  8. 訣竅直接驗證即可。
    解法
    1. 按照定義檢驗 \hat{T}\left(H\right) 是雙線性形式:對於任何 x,y,z\in V 與 c\in F,我們有

      \begin{aligned}\hat{T}\left(H\right)\left(cx+y,z\right)&=H\left(T\left(cx+y\right),T\left(z\right)\right)\\&=cH\left(T\left(x\right),T\left(z\right)\right)+H\left(T\left(y\right),T\left(z\right)\right)=c\hat{T}\left(H\right)\left(x,z\right)+\hat{T}\left(H\right)\left(y,z\right),\end{aligned}

      \begin{aligned}\hat{T}\left(H\right)\left(x,cy+z\right)&=H\left(T\left(x\right),T\left(cy+z\right)\right)\\&=cH\left(T\left(x\right),T\left(y\right)\right)+H\left(T\left(x\right),T\left(z\right)\right)=c\hat{T}\left(H\right)\left(x,y\right)+\hat{T}\left(H\right)\left(x,z\right).\end{aligned}

      因此 \hat{T}\left(H\right)V 上的雙線性形式。
    2. 如果 T 為同構函數,亦即 T 單射且滿射,那麼我們可以證明 \hat{T} 為單射與雙射如下:
      • \hat{T}\left(H_1\right)=\hat{T}\left(H_2\right),那麼對任何 x',y'\in W,由 T 滿射可取 x,y\in VT\left(x\right)=x'T\left(y\right)=y',從而對這樣的 x,y

        H_1\left(T\left(x\right),T\left(y\right)\right)=\hat{T}\left(H_1\right)\left(x,y\right)=\hat{T}\left(H_2\right)\left(x,y\right)=H_2\left(T\left(x\right),T\left(y\right)\right)

        此即

        H_1\left(x',y'\right)=H_2\left(x',y'\right)

        故由 x',y'\in W 的任意性可知 H_1=H_2,故 \hat{T} 單射。
      • 對任何 K\in\mathcal{B}\left(V\right),我們可取 H_s\in\mathcal{B}\left(W\right) 如下

        H_s\left(x',y'\right)=K\left(T^{-1}\left(x'\right),T^{-1}\left(y'\right)\right)~~~\mbox{for}~x',y'\in W.

        那麼容易檢驗發現

        \hat{T}\left(H_s\right)\left(x,y\right)=H_s\left(T\left(x\right),T\left(y\right)\right)=K\left(x,y\right)

        至此有 \hat{T}\left(H_s\right)=K,即 \hat{T} 滿射。
      綜上可知 \hat{T} 也是同構函數。

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