# 2.7. Eigenvalues and Eigenvectors

Example 2.7.1 (Fibonacci Numbers). We can use a change of basis to create a formula for the Fibonacci numbers.

$a_n = \frac{5+\sqrt{5}}{10} \bigg(\frac{1+\sqrt{5}}{2}\bigg)^n + \frac{5-\sqrt{5}}{10}\bigg(\frac{1-\sqrt{5}}{2}\bigg)^n.$

For any matrix $A$, the eigenvalues are the roots of the characteristic polynomial $\det (\lambda I - A) = 0$. This is not a good algorithm in practice because roots of high degree polynomials are hard to compute. In practice, QR decomposition is used. For symmetric matrices, the Jacobi algorithm is used.

Definition 2.7.2 (Eigenvector, eigenvalue, multiplicity). Let $V$ be a complex vector spce and $T: V\to V$ a linear transformation. A nonzero vector $v$ such that

$Tv = \lambda v$

for some number $\lambda$ is called an eigenvector of $T$. The number $\lambda$ is the corresponding eigenvalue. The multiplicity of an eigenvalue $\lambda$ is the dimension of the eigenspace ${v\mid Tv = \lambda v}$.

Definition 2.7.3 (Eigenbasis). A basis for a complex vector space $V$ is an eigenbasis of $V$ for a linear transformation $T$ if each element of the basis is an eigenvector of $T$.

## Diagonalization and eigenvectors

Proposition 2.7.5 (Diagonalization and eigenvectors). Let $A$ be an $n\times n$ matrix and $P = [\vec v_1 , \dots, \vec v_n]$ an invertible $n\times n$ matrix.

1. The eigenvalues of $A$ and the eigenvalues of $P^{-1}AP$ coincide.
2. If $(\vec v_1, \dots, \vec v_n)$ is an eigenbasis of $\Complex^n$ for $A$, with $A\vec v_i = \lambda_i \vec v_i$, then $P^{-1}AP$ is a diagonal matrix with diagonal entires $\lambda_1, \dots, \lambda_n$.
3. Conversely, if $P^{-1}AP$ is diagonal with diagonal entries $\lambda_i$, the columns of $P$ are eigenvectors of $A$ with eigenvalues $\lambda_i$.

## Finding an eigenbasis

Suppose we have the vectors $\vec e_1, A\vec e_1, A^2 \vec e_1, \dots$ What is the first $A^k \vec e_1$ which is linearly dependent on the earlier vectors? This is just what row reduction is for!

$A^k \vec e_1 = a_0 \vec e_1 + a_1A\vec e_2 +a_2A^2\vec e_2 + \dots$

The roots of the polynomial $A^k - a_{k-1}A^{k-1} - \dots - a_0 = 0$ are eigenvalues of $A$.

We can use the coefficients $a_0\dots a_k$ to define a polynomial

$p(t) = a_1+a_1t+\dots+a_{k-1}t^{k-1}+t^k$

which satisfies $p(A)\vec e_1 = \vec 0$. By the fundamental theorem of algebra, $p$ has at least one root $\lambda$, so we can write

$p(t)=(t-\lambda)q(t)$

for some polynomial $q$ of degree $k-1$. Define $\vec v = q(A)\vec e_1$. Then,

$(A-\lambda I)\vec v = (A-\lambda I)(q(A)\vec e_1)=\Big((A-\lambda I)(q(A))\Big)\vec e_1 = p(A)\vec e_1 = \vec 0,$

so $A\vec v = \lambda \vec v$. For $\vec v$ to be an eigenvector with eigenvalue $\lambda$, we still need to check that $\vec v \neq\vec 0.$ $\vec v$ is not $\vec 0$ because it is equal to $q(A)\vec e_1$ and $q(A) = a_{k-1}A^{k-1}e_1+\dots+a_1$ is not $0$ since $k>0$.

In class example. Find the eigenvectors and eigenvalues for $A = \begin{bmatrix}1&2\-1&3\end{bmatrix}$.

By row reduction, this is equal to

$\begin{bmatrix} 1&0&-5\\0&1&4\end{bmatrix}.$

This tells us that $A^2\vec e_1 = -5\vec e_1 + 4A\vec e_1$, or $p(t) = t^2 -4t+5 = 0$.

$\lambda_{1,2} = 2 \pm\sqrt{4-5} = 2\pm i.$ $p(t) = (t-(2+i))(t-(2-i)),$

so $(A-(2-i)I)\vec e_1$ is an eigenvector for the eigenvalue $2+i$. Let’s check our work.

$(A-(2-i)I)\vec e_1 = \begin{bmatrix}1-(2-i)&2\\-1&3-(2-i)\end{bmatrix}\begin{bmatrix}1\\0\end{bmatrix}$ $= \begin{bmatrix}-1+i\\-1\end{bmatrix}.$

Is it true that

$\begin{bmatrix}1&2\\-1&3\end{bmatrix}\begin{bmatrix}-1+i\\-1\end{bmatrix}\stackrel{?}{=}(2+i)\begin{bmatrix}-1+i\\-1\end{bmatrix}$ $= \begin{bmatrix}-3+i\\-2-i\end{bmatrix}.$

They do in fact agree.

Example. Find the eigenbasis for $\begin{bmatrix}1&-1&0\-1&2&-1\0&-1&1\end{bmatrix}$.

 We first find $[\vec e_1 A\vec e_1 A^2\vec e_1 A^3 \vec e_1]$, which is
$\begin{bmatrix}1&1&2&5\\0&-1&-3&-9\\0&0&1&4\end{bmatrix}=\begin{bmatrix}1&0&0&0\\0&1&0&-3\\0&0&1&4\end{bmatrix}.$

This tells us that $A^3\vec e_1 = -3A\vec e_1 + 4A^2\vec e_1$. Our polynomial becomes

$t^3 -4t^2+3t = 0 = t(t^2-4t+3) = t(t-3)(t-1).$

For $\lambda_1 = 0$, we write $p(t) = t(t^2-4t+3) = tq_1(t)$ to find the eigenvector

$q_1(A)\vec e_1 = \begin{bmatrix}2\\-3\\1\end{bmatrix}-4\begin{bmatrix}1\\-1\\0\end{bmatrix}+3\begin{bmatrix}1\\0\\0\end{bmatrix} = \begin{bmatrix}1\\1\\1\end{bmatrix}.$

For $\lambda_2=1$, we solve $p(t) = (t-1)(t^2-3t) = (t-1)q_2(t)$.

$q_2(A)\vec e_1 = (A^2-3A)\vec e_1$ $=\begin{bmatrix}2\\-3\\1\end{bmatrix}-3\begin{bmatrix}1\\-1\\0\end{bmatrix} = \begin{bmatrix}-1\\0\\1\end{bmatrix}.$

For $\lambda_3 = 3$, $p(t) = (t-3)(t^2 - t) = (A^2 - A)\vec e_1$ $= \begin{bmatrix}1\-2\1\end{bmatrix}$.

Theorem 2.7.9 (Existence of eigenbasis). Let $p_i$ be the lowest degree nonzero polynomial satisfying $p_i(A)\vec e_i = \vec 0$. Let $A$ be an $n\times n$ complex matrix. There exists an eigenbasis in $\Complex^n$ for $A$ if and only if all the roots of all the $p_i$ are multiplicity 1 (simple roots).