Learning Outcomes
- Find the eigenvalues of a \(2\times 2\) matrix.
Section 5.3 Eigenvalues and Characteristic Polynomials (GT3)
Subsection 5.3.1 Class Activities
Activity 5.3.1.
An invertible matrix \(M\) and its inverse \(M^{-1}\) are given below:
\begin{equation*}
M=\left[\begin{array}{cc}1&2\\3&4\end{array}\right]
\hspace{2em}
M^{-1}=\left[\begin{array}{cc}-2&1\\3/2&-1/2\end{array}\right]
\end{equation*}
Which of the following is equal to \(\det(M)\det(M^{-1})\text{?}\)
- \(\displaystyle -1\)
- \(\displaystyle 0\)
- \(\displaystyle 1\)
- \(\displaystyle 4\)
Fact 5.3.2.
For every invertible matrix \(M\text{,}\)
\begin{equation*}
\det(M)\det(M^{-1})= \det(I)=1
\end{equation*}
so \(\det(M^{-1})=\frac{1}{\det(M)}\text{.}\)
Furthermore, a square matrix \(M\) is invertible if and only if \(\det(M)\not=0\text{.}\)
Observation 5.3.3.
Consider the linear transformation \(A : \IR^2 \rightarrow \IR^2\) given by the matrix \(A = \left[\begin{array}{cc} 2 & 2 \\ 0 & 3 \end{array}\right]\text{.}\)
It is easy to see geometrically that
\begin{equation*}
A\left[\begin{array}{c}1 \\ 0 \end{array}\right] =
\left[\begin{array}{cc} 2 & 2 \\ 0 & 3 \end{array}\right]\left[\begin{array}{c}1 \\ 0 \end{array}\right]=
\left[\begin{array}{c}2 \\ 0 \end{array}\right]=
2 \left[\begin{array}{c}1 \\ 0 \end{array}\right]\text{.}
\end{equation*}
It is less obvious (but easily checked once you find it) that
\begin{equation*}
A\left[\begin{array}{c} 2 \\ 1 \end{array}\right] =
\left[\begin{array}{cc} 2 & 2 \\ 0 & 3 \end{array}\right]\left[\begin{array}{c}2 \\ 1 \end{array}\right]=
\left[\begin{array}{c} 6 \\ 3 \end{array}\right] =
3\left[\begin{array}{c} 2 \\ 1 \end{array}\right]\text{.}
\end{equation*}
Definition 5.3.4.
Let \(A \in M_{n,n}\text{.}\) An eigenvector for \(A\) is a vector \(\vec{x} \in \IR^n\) such that \(A\vec{x}\) is parallel to \(\vec{x}\text{.}\)
In other words, \(A\vec{x}=\lambda \vec{x}\) for some scalar \(\lambda\text{.}\) If \(\vec x\not=\vec 0\text{,}\) then we say \(\vec x\) is a nontrivial eigenvector and we call this \(\lambda\) an eigenvalue of \(A\text{.}\)
Activity 5.3.5.
Finding the eigenvalues \(\lambda\) that satisfy
\begin{equation*}
A\vec x=\lambda\vec x=\lambda(I\vec x)=(\lambda I)\vec x
\end{equation*}
for some nontrivial eigenvector \(\vec x\) is equivalent to finding nonzero solutions for the matrix equation
\begin{equation*}
(A-\lambda I)\vec x =\vec 0\text{.}
\end{equation*}
(a)
If \(\lambda\) is an eigenvalue, and \(T\) is the transformation with standard matrix \(A-\lambda I\text{,}\) which of these must contain a non-zero vector?
- The kernel of \(T\)
- The image of \(T\)
- The domain of \(T\)
- The codomain of \(T\)
(b)
Therefore, what can we conclude?
- \(A\) is invertible
- \(A\) is not invertible
- \(A-\lambda I\) is invertible
- \(A-\lambda I\) is not invertible
(c)
And what else?
- \(\displaystyle \det A=0\)
- \(\displaystyle \det A=1\)
- \(\displaystyle \det(A-\lambda I)=0\)
- \(\displaystyle \det(A-\lambda I)=1\)
Fact 5.3.6.
The eigenvalues \(\lambda\) for a matrix \(A\) are exactly the values that make \(A-\lambda I\) non-invertible.
Thus the eigenvalues \(\lambda\) for a matrix \(A\) are the solutions to the equation
\begin{equation*}
\det(A-\lambda I)=0.
\end{equation*}
Definition 5.3.7.
The expression \(\det(A-\lambda I)\) is called characteristic polynomial of \(A\text{.}\)
For example, when \(A=\left[\begin{array}{cc}1 & 2 \\ 5 & 4\end{array}\right]\text{,}\) we have
\begin{equation*}
A-\lambda I=
\left[\begin{array}{cc}1 & 2 \\ 5 & 4\end{array}\right]-
\left[\begin{array}{cc}\lambda & 0 \\ 0 & \lambda\end{array}\right]=
\left[\begin{array}{cc}1-\lambda & 2 \\ 5 & 4-\lambda\end{array}\right]\text{.}
\end{equation*}
Thus the characteristic polynomial of \(A\) is
\begin{equation*}
\det\left[\begin{array}{cc}1-\lambda & 2 \\ 5 & 4-\lambda\end{array}\right]
=
(1-\lambda)(4-\lambda)-(2)(5)
=
\lambda^2-5\lambda-6
\end{equation*}
and its eigenvalues are the solutions \(-1,6\) to \(\lambda^2-5\lambda-6=0\text{.}\)
Activity 5.3.8.
Let \(A = \left[\begin{array}{cc} 5 & 2 \\ -3 & -2 \end{array}\right]\text{.}\)
(a)
Compute \(\det (A-\lambda I)\) to determine the characteristic polynomial of \(A\text{.}\)
(b)
Set this characteristic polynomial equal to zero and factor to determine the eigenvalues of \(A\text{.}\)
Activity 5.3.9.
Find all the eigenvalues for the matrix \(A=\left[\begin{array}{cc} 3 & -3 \\ 2 & -4 \end{array}\right]\text{.}\)
Activity 5.3.10.
Find all the eigenvalues for the matrix \(A=\left[\begin{array}{cc} 1 & -4 \\ 0 & 5 \end{array}\right]\text{.}\)
Activity 5.3.11.
Find all the eigenvalues for the matrix \(A=\left[\begin{array}{ccc} 3 & -3 & 1 \\ 0 & -4 & 2 \\ 0 & 0 & 7 \end{array}\right]\text{.}\)
Subsection 5.3.2 Videos
Subsection 5.3.3 Slideshow
Slideshow of activities available at
https://teambasedinquirylearning.github.io/linear-algebra/2023/GT3.slides.html.Exercises 5.3.4 Exercises
Exercises available at
https://teambasedinquirylearning.github.io/linear-algebra/2023/exercises/#/bank/GT3/.Subsection 5.3.5 Mathematical Writing Explorations
Exploration 5.3.12.
What are the maximum and minimum number of eigenvalues associated with an \(n \times n\) matrix? Write small examples to convince yourself you are correct, and then prove this in generality.Subsection 5.3.6 Sample Problem and Solution
Sample problem Example B.1.24.
