Section 6.2 Computing eigenvalues
An easy but important fact is used to compute eigenvalues.
Proposition 6.2.1. Eigenvalues \(x\) satisfy \((A-\lambda I)\vec x=\vec0\).
Proof.
If \(A\vec x=\lambda\vec x\text{,}\) then
Corollary 6.2.2.
\(\lambda\) is an eigenvalue of \(A\) if and only if \(A-\lambda I\) is singular.
Proof.
One of the equivalent conditions of singularity given in Theorem 4.4.21 is that \(B\) is singular if \(B\vec x=\vec 0\) for some \(\vec x\not=\vec 0\text{.}\) The matrix \(A-\lambda I\) plays the role of \(B\text{.}\)
Corollary 6.2.3.
\(\lambda\) is an eigenvalue of \(A\) if and only if \(\det (A-\lambda I)=0\)
Proof.
\(B\) is singular if and only if \(\det B=0\text{.}\)
We now can see how these corollaries allow us to compute eigenvalues. We look at the matrix from Example 6.1.3.
Example 6.2.4.
We use
and
Evaluating the determinant gives
So we see that \(\det(A-\lambda I)=0\) only if \(\lambda=2\text{,}\) \(\lambda=4\) or \(\lambda=6\text{.}\) The evaluation of \(\det(A-\lambda I)=0\) gave us a polynomial \(p(\lambda)=-(\lambda-2)(\lambda-4)(\lambda-6)\) whose roots are the eigenvalues.
Definition 6.2.5. The characteristic polynomial.
The characteristic polynomial of a square matrix \(A\) is the polynomial
Note 6.2.6.
Some sources define the characteristic polynomial as \(\det(A-\lambda I)\text{.}\) Since \(\lambda I-A=-(A-\lambda I)\text{,}\) it follows that for any square matrix \(A\) of size \(n\text{,}\)
and so both polynomials have the same roots.
We look at the matrix from Example 6.1.4.
Example 6.2.7.
\(A=\begin{bmatrix}2\amp 1\amp 4\\ 0\amp 3\amp 0\\ 2\amp -2\amp -5 \end{bmatrix}\text{.}\)
and so the eigenvalues are \(\lambda=-6\) and \(\lambda=3\)