# Diagonalization of a Matrix (with Examples)

• By David A. Smith, Founder & CEO, Direct Knowledge
• David Smith has a B.S. and M.S. in Mathematics and has enjoyed teaching calculus, linear algebra, and number theory at both Tarrant County College and the University of Texas at Arlington. David is the Founder and current CEO of Direct Knowledge.

An $n\times n$ matrix $A$ is called diagonalizable if $A$ is similar to some diagonal matrix $D.$ If the matrix of a linear transformation $T$ with respect to some basis is diagonal then we call $T$ diagonalizable.

## Diagonalization Theorem

Theorem. An $n\times n$ matrix $A$ is diagonalizable if and only if it has $n$ linearly independent eigenvectors. In that case, the diagonal matrix $D$ is similar to $A$ and is given by $$\label{diagmat} D= \begin{bmatrix} \lambda_1 & 0 & \cdots & 0 \\ 0 & \lambda_2 & \cdots & 0 \\ \vdots & \vdots & \ddots& \vdots \\ 0 & 0 & \cdots & \lambda_n \end{bmatrix}$$ where $\lambda_1, \ldots, \lambda_n$ are the eigenvalues of $A.$ If $C$ is a matrix whose columns are linearly independent eigenvectors of $A$, then $D=C^{-1}A C.$

Corollary. Let $T$ be a linear transformation given by $T(x)=A x$ where $A$ is a square matrix. If $\mathcal{D}=(v_1, \ldots,v_n)$ is an eigenbasis for $T$, with $A v_i=\lambda_i v_i$, then the $\mathcal{D}$-matrix $D$ of $T$ given in \eqref{diagmat} is $$D=[ v_1, \ldots, v_n]^{-1}A [ v_1, \ldots, v_n].$$

Corollary. A matrix $A$ is diagonalizable if and only if there exists an eigenbasis for $A.$ In particular, if an $n\times n$ matrix $A$ has $n$ distinct eigenvalues, then $A$ is diagonalizable.

Example. Let $T: \mathcal{P}_2\to \mathcal{P}_2$ be the linear transformation defined by $$T(a_0+a_1 x+a_2x^2)=(a_0+a_1+a_2)+(a_1+a_2)x+a_2x^2.$$ Show that $T$ is not diagonalizable. The matrix of $T$ with respect to the usual basis $(, x, x^2)$ for $\mathcal{P}_2$ is easily seen to be $$A= \begin{bmatrix} 1 & 1 & 1 \\ 0 & 1 & 1 \\ 0 & 0 & 1 \end{bmatrix}.$$

The characteristic polynomial is $f_A(x)=-(x-1)^3$ since $A$ is upper triangular. So $T$ has only one (repeated) eigenvalue $\lambda=1.$ A nonzero polynomial $g$ with $g(x)=a_0+a_1 x+a_2 x^2$ is an eigenvector if and only if $$\label{notdiageq} \begin{bmatrix} 0 & 1 & 1 \\ 0 & 0 & 1 \\ 0 & 0 & 0 \end{bmatrix} \begin{bmatrix}a_0 \\ a_1 \\ a_2\end{bmatrix} = \begin{bmatrix}0 \\ 0 \\ 0\end{bmatrix}.$$

Thus $a_1=0$ and $a_2=0$, so there is only one linearly independent eigenvector for $\lambda=1.$ Thus $T$ is not diagonalizable.

## Examples

Example. Let $T:\mathcal{P}_2\to \mathcal{P}_2$ be the linear transformation defined by $$T(f(x))=x^2f”(x)+(3x-2)f'(x)+5 f(x).$$

Find a basis for $\mathcal{P}_2$ such that the matrix representation of $T$ with respect to $\mathcal{B}$ is diagonal. Since $T(x^2)=13x^2-4x$, $T(x)=8x-2$, and $T(1)=5$ the matrix representation of $T$ with respect to the basis $\mathcal{B}=(x^2,x,1)$ is $$A=\begin{bmatrix}13 & 0 & 0 \\ -4 & 8 & 0 \\ 0 & -2 & 5 \end{bmatrix}.$$ Hence $$f_T(x)=f_A(x)=\begin{vmatrix} x-13 & 0 & 0 \\ 4 & x-8 & 0 \\ 0 & 2 & x-5 \end{vmatrix}=(x-13)(x-8)(x-5).$$

The eigenvalues of $T$ are $\lambda_1=13$, $\lambda_2=8$ and $\lambda_3=5.$ Solving each of the homogenous systems $(A-13I_3) x= 0$, $(A-8I_3) x= 0$, and $(A-5I_3) x= 0$ yields the eigenvectors $v_1=5x^2-4x+1$, $v_2=3x-2$, and $v_3=1$, respectively. Notice $v_1, v_2, v_3$ are 3 linearly independent vectors, so $T$ is diagonalizable. We let $\mathcal{D}=( v_1, v_2, v_3)$ and since $T( v_1)=13 v_1$,$T( v_2)=8 v_2$, and $T( v_3)=5 v_3$, the matrix representation of $T$ with respect to $\mathcal{D}$ is the diagonal matrix $$\begin{bmatrix}13 & 0 & 0 \\ 0 & 8 & 0 \\ 0 & 0 & 5 \end{bmatrix}$$

The method of diagonalization is illustrated with the following example.

Example. Let $T:\mathcal{P}_3\to \mathcal{P}_3$ be the linear transformation defined by $$T(f(x))=xf'(x)+f(x+1).$$

Find a basis for $\mathcal{P}_3$ such that the matrix representation of $T$ with respect to $\mathcal{B}$ is diagonal. Since $T(x^3)=4x^3+3x^2+3x+1$, $T(x^2)=3x^2+2x+1$, $T(x)=2x+1$, and $T(1)=1$ the matrix representation of $T$ with respect to the basis $\mathcal{B}=(x^3,x^2,x,1)$ is $$A=\begin{bmatrix} 4 & 0 & 0 & 0 \\ 3 & 3 & 0 & 0 \\ 3 & 2 & 2 & 0 \\ 1 & 1 & 1 & 1 \end{bmatrix}.$$

Since $A$ is lower triangular, $f_T(x)=f_A(x)=(x-4)(x-3)(x-2)(x-1)$; and so the eigenvalues are $\lambda_1=4$, $\lambda_2=3$, $\lambda_3=2$, and $\lambda_4=1.$ Solving for a basis for each eigenspace of $A$ yields $$E_{\lambda_1}=\left(\begin{bmatrix}6 \\ 18 \\ 27 \\ 17\end{bmatrix}\right), \quad E_{\lambda_2}=\left(\begin{bmatrix}0 \\ 2 \\ 4 \\ 3\end{bmatrix}\right), \quad E_{\lambda_3}=\left(\begin{bmatrix}0 \\ 0 \\ 1 \\ 1\end{bmatrix}\right),\quad E_{\lambda_4}=\left(\begin{bmatrix}0 \\ 0 \\ 0 \\ 1\end{bmatrix}\right).$$

By taking the polynomial corresponding to the basis vectors, we let $\mathcal{D}=( v_1, v_2, v_3, v_4)$ where $v_1=6x^3+18x^2+27x+17$, $v_2=2x^2+4x+3$, $v_3=x+1$, and $v_4=1.$ The diagonal matrix $$\begin{bmatrix} 4 & 0 & 0 & 0 \\ 0 & 3 & 0 & 0 \\ 0 & 0 & 2 & 0 \\ 0 & 0 & 0 & 1 \end{bmatrix}$$ is the matrix representation of $T$ in $\mathcal{D}$-coordinates and has the eigenvalues of $T$ on its main diagonal. The transition matrix $P$ from $\mathcal{B}$-coordinates to $\mathcal{D}$-coordinates is $$P=\begin{bmatrix} 6 & 0 & 0 & 0 \\ 18 & 2 & 0 & 0 \\ 27 & 4 & 1 & 0 \\ 17 & 3 & 1 & 1 \end{bmatrix}$$ and satisfies the required relation $D=P^{-1}AP$ as can be verified.

Example. If $A$ is similar to $B$, show that $A^n$ is similar to $B^n$, for any positive integer $n.$

Example. Suppose that $C^{-1}AC=D.$ Show that for any integer $n$, $A^n=CD^nC^{-1}.$

Example. Let $a$ and $b$ be real numbers. By diagonalizing $M= \begin{bmatrix} a & b-a \\ 0 & b \end{bmatrix},$ prove that $M^n= \begin{bmatrix} a^n & b^n-a^n \\ 0 & b^n \end{bmatrix}$ for all positive integers $n.$ We need a basis of $\mathbb{R}^2$ consisting of eigenvectors of $M.$ One such basis is $v_1= e_1$ and $v_2= e_1+ e_2$ where $a$ and $b$ are eigenvalues for corresponding to these eigenvectors, respectively. Let $P=\begin{bmatrix} v_1 & v_2\end{bmatrix}$, then the diagonalization is \begin{equation*} D=\begin{bmatrix} v_1 & v_2\end{bmatrix}^{-1}M \begin{bmatrix} v_1 & v_2\end{bmatrix}=\begin{bmatrix}a & 0 \\ 0 & b \end{bmatrix}. \end{equation*} Therefore \begin{equation*} M^n=(PDP^{-1})^n=(PDP^{-1})\cdots (PDP^{-1}) =PD^n P^{-1} =\begin{bmatrix} a^n & b^n-a^n \\ 0 & b^n \end{bmatrix}. \end{equation*} as desired.

Why is diagonalization useful?

Is there orthogonal diagonalization?