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+# Lecture 22
+
+## Chapter V Eigenvalue and Eigenvectors
+
+### Upper Triangular Matrices 5C
+
+#### Theorem 5.44
+
+Let $T\in \mathscr{L}(V)$ be a linear operator, then $T$ has an upper triangular matrix (with respect to some basis), if the minimal polynomial is $(z-\lambda_1)...(z-\lambda_m)$ for $\lambda_1,..,\lambda_m\in \mathbb{F}$
+
+Proof:
+
+$\implies$ easy
+
+$\impliedby$ Suppose the minimal polynomial of $T$ is $(z-\lambda_1)...(z-\lambda_m)$
+
+Then we do induction on $m$.
+
+Base case: $m=1$, then $T-\lambda_1 I=0$, $T=\lambda I$ but $\lambda I$ has an upper triangular matrix, 
+
+Induction step: $m>1$, Suppose the results holds for smaller $m$. Let $u=range(T-\lambda_m I)$, $U$ is invariant under $T$, consider $T\vert_u$.
+
+Note that if $u\in U$, $(T-\lambda_1 I)...(T-\lambda_{m-1} I)u=(T-\lambda_1 I)...(T-\lambda_m I)v=0$. Thus the minimal polynomial of $T\vert_U$ divides $(z-\lambda_1)...(z-\lambda_{m-1})$
+
+#### Corollary 5.47 (staring point for Jordan Canonical Form)
+
+Suppose $V$ is a finite dimensional complex vector space, and $T\in \mathscr{L}(V)$, then $T$ has an upper triangular matrix with respect to some basis.
+
+Recall: $T$ is upper triangular $\iff$ $Tv_k\in Span\ (v_1,...,v_k)$. where $v_1,...,v_n$ is a basis.
+
+Let $u_1,...,u_r$ be a basis for $U$ such that $Tu_k\in Span\ (v_1,...,v_k)$ (such thing exists because $T$ is upper triangular.
+
+Extend to a basis of $V$, $u_1,..,u_r,v_1,...,v_s$, then 
+
+$$
+Tv_k=((T-\lambda_m I)+\lambda_m I)v_k=(T-\lambda_m I)v_k+\lambda_m v_k
+$$
+
+and $(T-\lambda_m I)v_k\in U, \lambda_m v_k\in Span\ (u_1,..,u_r,v_k)$
+
+Thus with respect to the same basis $u_1,..,u_r,v_1,...,v_s$ $T$ is upper triangular.
+
+$$
+M(T)=\begin{pmatrix}
+    M(T\vert_U) &\vert & *\\
+    \rule{2cm}{1pt}&&\rule{4cm}{0.4pt}\\
+    0  & \vert&\lambda \textup{ on the diagonal line}
+\end{pmatrix}
+$$
+
+Example:
+
+$M(T)=\begin{pmatrix}
+    2&0&1\\
+    0&2&1\\
+    1&1&3
+\end{pmatrix}$ and the minimal polynomial is $(z-2)(z-2)(z-3)$
+
+$v_1=(1,-1,0), v_2=(1,0,-1), v_3=(-1,1,0)$
+
+$M(T,(v_1,v_2,v_3))=\begin{pmatrix}
+    2&1&0\\
+    0&2&0\\
+    0&0&3
+\end{pmatrix}$ which is upper triangular.
+
+### 5D Diagonalizable Operations
+
+#### Definition 5.48
+
+A **Diagonal matrix** is a matrix where all entries except the diagonal is zero
+
+Example: $I,0,\begin{pmatrix}
+    2&0&0\\
+    0&2&0\\
+    0&0&3
+\end{pmatrix}$
+
+#### Definition 5.50
+
+An operator $T\in\mathscr{L}(V)$ is diagonalizable if $M(T)$ is diagonalizable with respect to some basis.
+
+Example:
+
+$T:\mathbb{F}->\mathbb{F^2}$
+
+$M(T)=\begin{pmatrix}
+    3&-1\\
+    -1&3&
+\end{pmatrix} v_1=(1,-1), v_2=(1,1)$, $T(v_1)=(4,-4)=4v_1, T(v_2)=(2,2)=2v_2$, so the eigenvalues are $2$ with eigenvector $v_2$, and $4$ with eigenvector $v_1$. The eigenvectors for $z$ are $Span (v_2)\ \{0\}$
+
+$M(T,(v_1,v_2))=\begin{pmatrix}
+    4&0\\
+    0&2
+\end{pmatrix}$ and $T$ is diagonalizable.
+
+#### Definition 5.52
+
+Let $T\in \mathscr{L}(V),\lambda \in \mathbb{F}$. the **eigenspace** of $T$ corresponding to $\lambda$ is the subspace $E(\lambda, T)\in V$ defined by
+
+$$
+E(\lambda, T)=null\ (T-\lambda I)=\{ v\in V\vert Tv=\lambda v\}
+$$
+
+Example:
+
+$E(2,T)=Span\ (v_2)$ $E(4,T)=Span\ (v_1)$, $E(3,T)=\{0 \}$
+
+#### Theorem 5.54
+
+Suppose $T\in \mathscr{L}(V)$ $\lambda_1,...,\lambda_m$ are distinct eigenvalues of $T$, Then
+
+$$
+E(\lambda_1, T)+...+E(\lambda_m,T)
+$$
+
+is a direct sum. In particular if $V$ is finite dimensional.
+
+$$
+dim\ (E(\lambda_1, T))+...+dim\ (E(\lambda_m,T))\leq dim\ V
+$$
+
+Proof:
+
+Need to show that if $v_k\in E(\lambda_k,T)$ for $k=1,...,m$ then $v_1+...+v_m=0\iff v_k=0$ for $k=1,...,m$. i.e eigenvectors for distinct eigenvalues are linearly independent. (Prop 5.11)