MLF | Lecture | Week-4

Lecture Outline

Computing Eigenvectors



\[ A = \begin{bmatrix} 3 & 1\\ 0 & 2 \end{bmatrix} \]


\(\lambda = 2\) is an eigenvalue. What are the corresponding eigenvectors?



Computing Eigenvectors



\[ A = \begin{bmatrix} 3 & 1\\ 0 & 2 \end{bmatrix} \]


\(\lambda = 2\) is an eigenvalue. What are the corresponding eigenvectors?


\[ \begin{aligned} (A - 2I) x &= 0\\\\ \end{aligned} \]

Computing Eigenvectors



\[ A = \begin{bmatrix} 3 & 1\\ 0 & 2 \end{bmatrix} \]


\(\lambda = 2\) is an eigenvalue. What are the corresponding eigenvectors?


\[ \begin{aligned} (A - 2I) x &= 0\\\\ \begin{bmatrix} 1 & 1\\ 0 & 0 \end{bmatrix} \begin{bmatrix} x_1\\ x_2 \end{bmatrix} &= \begin{bmatrix} 0\\ 0 \end{bmatrix} \end{aligned} \]

Computing Eigenvectors



\[ A = \begin{bmatrix} 3 & 1\\ 0 & 2 \end{bmatrix} \]


\(\lambda = 2\) is an eigenvalue. What are the corresponding eigenvectors?


\[ \begin{aligned} (A - 2I) x &= 0\\\\ \begin{bmatrix} 1 & 1\\ 0 & 0 \end{bmatrix} \begin{bmatrix} x_1\\ x_2 \end{bmatrix} &= \begin{bmatrix} 0\\ 0 \end{bmatrix}\\\\ x_1 + x_2 &= 0 \end{aligned} \]

Computing Eigenvectors



\[ A = \begin{bmatrix} 3 & 1\\ 0 & 2 \end{bmatrix} \]


\(\lambda = 2\) is an eigenvalue. What are the corresponding eigenvectors?


\[ \begin{aligned} (A - 2I) x &= 0\\\\ \begin{bmatrix} 1 & 1\\ 0 & 0 \end{bmatrix} \begin{bmatrix} x_1\\ x_2 \end{bmatrix} &= \begin{bmatrix} 0\\ 0 \end{bmatrix}\\\\ x_1 + x_2 &= 0 \end{aligned} \] \(\begin{bmatrix}-1\\1\end{bmatrix}\) is an eigenvector of \(A\) with eigenvalue \(2\)

Computing Eigenvectors



\[ A = \begin{bmatrix} 3 & 1\\ 0 & 2 \end{bmatrix} \]


\(\lambda = 2\) is an eigenvalue. What are the corresponding eigenvectors?


\[ \begin{aligned} (A - 2I) x &= 0\\\\ \begin{bmatrix} 1 & 1\\ 0 & 0 \end{bmatrix} \begin{bmatrix} x_1\\ x_2 \end{bmatrix} &= \begin{bmatrix} 0\\ 0 \end{bmatrix}\\\\ x_1 + x_2 &= 0 \end{aligned} \] \(\begin{bmatrix}-1\\1\end{bmatrix}\) is an eigenvector of \(A\) with eigenvalue \(2\)


\(\text{span} \left ( \begin{bmatrix}-1\\1\end{bmatrix} \right)\) is the the eigenspace corresponding to \(\lambda = 2\)

Eigenspace and Nullspace



  • If \((\lambda, v)\) is an eigenpair, then \((A - \lambda I) v = 0\)

  • Then, \(v\) belongs to the nullspace of \(A - \lambda I\)

  • Every non-zero vector in \(N(A - \lambda I)\) is an eigenvector for \(\lambda\)

  • Find a basis \(B\) for the nullspace of \(A - \lambda I\)

  • \(\text{span}(B)\) is nothing but the eigenspace corresponding to \(\lambda\)