Eigenvalues and eigenvectors: Eigenvalues and eigenvectors
Solving an eigenvalue problem
When you determine eigenvalues of a matrix and corresponding eigenspaces, then you solve an eigenvalue problem for a matrix. Below is an example.
Solve the eigenvalue problem for the matrix \[
A = \matrix{-5 & -4 \\ 12 & 9}
\]In other words, determine the eigenvalues and vectors.
A = \matrix{-5 & -4 \\ 12 & 9}
\]In other words, determine the eigenvalues and vectors.
The characteristic equation is \[\det\matrix{-5-\lambda & -4 \\ 12 & 9-\lambda }=0\] We first rewrite the characteristic polynomial of \(A\):
\[ \begin{aligned}
\det(A-\lambda I) = \left\vert \begin{array}{cc} -5-\lambda & -4 \\ 12 & 9-\lambda \end{array} \right\vert &= (-5-\lambda)(9-\lambda)+4\cdot12 \\
&= (-5-\lambda)(9-\lambda)+48 \\ &= \lambda^2-4\,\lambda+3
\end{aligned}\] To solve this quadratic equation, we can factor it in the following way: \[ \lambda^2-4\,\lambda+3 = (\lambda-3)(\lambda-1) \] So, the eigenvalues are \(\lambda_1 = 3\) and \(\lambda_2 = 1\).
Let \(\lambda = 3\) be an eigenvalue of the matrix \[A=\matrix{-5 & -4 \\ 12 & 9}\] Then there must be a vector \(\vec{v}\) such that \(A\vec{v}=3\vec{v}\), this is, \[(A-3 I)\vec{v}=\vec{0}\text.\] In other words, we must find the kernel of the matrix \(A-3 I\). We can do this through row reduction of the matrix \[A-3 I = \matrix{-5 & -4 \\ 12 & 9} - \matrix{3 & 0 \\0& 3 }=\matrix{ -8 & -4 \\ 12 & 6}\] This can be done as follows:
\[\begin{aligned}
\matrix{-8&-4\\12&6\\}&\sim\matrix{1&{{1}\over{2}}\\12&6\\}&{\blue{\begin{array}{c}-{{1}\over{8}}R_1\\\phantom{x}\end{array}}}\\\\ &\sim\matrix{1&{{1}\over{2}}\\0&0\\}&{\blue{\begin{array}{c}\phantom{x}\\R_2-12R_1\end{array}}} \end{aligned}\] So the eigenspace for \(\lambda = 3\) equals \(\left\{ r \cv{1\\-2} \middle|\;r\in\mathbb R\right\}=\left\langle\cv{1\\-2}\right\rangle\).
Let \(\lambda = 1\) be an eigenvalue of the matrix \[A=\matrix{-5 & -4 \\ 12 & 9}\] Then there must be a vector \(\vec{v}\) such that \(A\vec{v}=1\vec{v}\), that is, \[(A-I)\vec{v}=\vec{0}\text.\] In other words, we must find the kernel of the matrix \(A-I\). We can do this through row reduction of the matrix \[A-I = \matrix{-5 & -4 \\ 12 & 9} - \matrix{1 & 0 \\0& 1 }=\matrix{ -6 & -4 \\ 12 & 8}\] This can be done as follows:
\[\begin{aligned}
\matrix{-6&-4\\12&8\\}&\sim\matrix{1&{{2}\over{3}}\\12&8\\}&{\blue{\begin{array}{c}-{{1}\over{6}}R_1\\\phantom{x}\end{array}}}\\\\ &\sim\matrix{1&{{2}\over{3}}\\0&0\\}&{\blue{\begin{array}{c}\phantom{x}\\R_2-12R_1\end{array}}} \end{aligned}\] So the eigenspace for \(\lambda = 1\) equals \(\left\{ r \cv{2\\-3} \middle|\;r\in\mathbb R\right\}=\left\langle\cv{2\\-3}\right\rangle\).
If possible, we avoided fractions in the solution.
\[ \begin{aligned}
\det(A-\lambda I) = \left\vert \begin{array}{cc} -5-\lambda & -4 \\ 12 & 9-\lambda \end{array} \right\vert &= (-5-\lambda)(9-\lambda)+4\cdot12 \\
&= (-5-\lambda)(9-\lambda)+48 \\ &= \lambda^2-4\,\lambda+3
\end{aligned}\] To solve this quadratic equation, we can factor it in the following way: \[ \lambda^2-4\,\lambda+3 = (\lambda-3)(\lambda-1) \] So, the eigenvalues are \(\lambda_1 = 3\) and \(\lambda_2 = 1\).
Let \(\lambda = 3\) be an eigenvalue of the matrix \[A=\matrix{-5 & -4 \\ 12 & 9}\] Then there must be a vector \(\vec{v}\) such that \(A\vec{v}=3\vec{v}\), this is, \[(A-3 I)\vec{v}=\vec{0}\text.\] In other words, we must find the kernel of the matrix \(A-3 I\). We can do this through row reduction of the matrix \[A-3 I = \matrix{-5 & -4 \\ 12 & 9} - \matrix{3 & 0 \\0& 3 }=\matrix{ -8 & -4 \\ 12 & 6}\] This can be done as follows:
\[\begin{aligned}
\matrix{-8&-4\\12&6\\}&\sim\matrix{1&{{1}\over{2}}\\12&6\\}&{\blue{\begin{array}{c}-{{1}\over{8}}R_1\\\phantom{x}\end{array}}}\\\\ &\sim\matrix{1&{{1}\over{2}}\\0&0\\}&{\blue{\begin{array}{c}\phantom{x}\\R_2-12R_1\end{array}}} \end{aligned}\] So the eigenspace for \(\lambda = 3\) equals \(\left\{ r \cv{1\\-2} \middle|\;r\in\mathbb R\right\}=\left\langle\cv{1\\-2}\right\rangle\).
Let \(\lambda = 1\) be an eigenvalue of the matrix \[A=\matrix{-5 & -4 \\ 12 & 9}\] Then there must be a vector \(\vec{v}\) such that \(A\vec{v}=1\vec{v}\), that is, \[(A-I)\vec{v}=\vec{0}\text.\] In other words, we must find the kernel of the matrix \(A-I\). We can do this through row reduction of the matrix \[A-I = \matrix{-5 & -4 \\ 12 & 9} - \matrix{1 & 0 \\0& 1 }=\matrix{ -6 & -4 \\ 12 & 8}\] This can be done as follows:
\[\begin{aligned}
\matrix{-6&-4\\12&8\\}&\sim\matrix{1&{{2}\over{3}}\\12&8\\}&{\blue{\begin{array}{c}-{{1}\over{6}}R_1\\\phantom{x}\end{array}}}\\\\ &\sim\matrix{1&{{2}\over{3}}\\0&0\\}&{\blue{\begin{array}{c}\phantom{x}\\R_2-12R_1\end{array}}} \end{aligned}\] So the eigenspace for \(\lambda = 1\) equals \(\left\{ r \cv{2\\-3} \middle|\;r\in\mathbb R\right\}=\left\langle\cv{2\\-3}\right\rangle\).
If possible, we avoided fractions in the solution.
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