Eigenvectors of 2*2 rotation matrix

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Discussion Overview

The discussion revolves around the eigenvectors of a 2x2 rotation matrix represented as \begin{bmatrix}\cos \theta& \sin \theta \\ - \sin \theta & \cos \theta \end{bmatrix}. Participants explore the calculation of eigenvalues and eigenvectors, addressing specific cases such as when \(\theta = 0\), and the implications of these calculations on orthogonality of the resulting eigenvectors.

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • Some participants calculate the eigenvalues of the rotation matrix to be \(\lambda_1=e^{i \theta}\) and \(\lambda_2=e^{-i \theta}\).
  • Participants derive equations for eigenvectors but express confusion about how to proceed after reaching a certain point in their calculations.
  • One participant suggests dividing the equations by \(\sin \theta\) under the assumption that \(\sin \theta \neq 0\).
  • There is a discussion about potential contradictions arising from the derived equations for eigenvectors.
  • Participants question the scenario when \(\theta = 0\) and how it affects the eigenvalues and eigenvectors.
  • One participant asserts that the eigenvectors for \(\lambda_1\) and \(\lambda_2\) are \(\begin{bmatrix}1\\ i\end{bmatrix}\) and \(\begin{bmatrix}1\\ 1/i\end{bmatrix}\), respectively.
  • Concerns are raised about the orthogonality of the eigenvectors based on the inner product calculation, leading to a clarification about the definition of the inner product for complex vectors.

Areas of Agreement / Disagreement

Participants express differing views on the implications of their calculations, particularly regarding the orthogonality of the eigenvectors and the handling of cases where \(\sin \theta = 0\). There is no consensus on the resolution of these issues.

Contextual Notes

Participants note the dependence on the assumption that \(\sin \theta \neq 0\) and the implications of the identity matrix when \(\theta = 0\). The discussion also highlights the need for careful application of the inner product definition in complex vector spaces.

bugatti79
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Hi Folks,

I calculate the eigenvalues of \begin{bmatrix}\cos \theta& \sin \theta \\ - \sin \theta & \cos \theta \end{bmatrix} to be \lambda_1=e^{i \theta} and \lambda_2=e^{-i \theta}

for \lambda_1=e^{i \theta}=\cos \theta + i \sin \theta I calculate the eigenvector via A \lambda = \lambda V as

\begin{bmatrix}\cos -(\cos \theta+ i \sin \theta) & \sin \theta \\ - \sin \theta & \cos -(\cos \theta+ i \sin \theta)\end{bmatrix} \begin{bmatrix}v_1\\ v_2\end{bmatrix}=\vec{0}

which reduces to

- i \sin \theta v_1+ \sin \theta v_2=0
-\sin \theta v_1-i \sin \theta v_2=0

I am stumped at this point...how shall I proceed?
 
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bugatti79 said:
Hi Folks,

I calculate the eigenvalues of \begin{bmatrix}\cos \theta& \sin \theta \\ - \sin \theta & \cos \theta \end{bmatrix} to be \lambda_1=e^{i \theta} and \lambda_2=e^{-i \theta}

for \lambda_1=e^{i \theta}=\cos \theta + i \sin \theta I calculate the eigenvector via A \lambda = \lambda V as

\begin{bmatrix}\cos -(\cos \theta+ i \sin \theta) & \sin \theta \\ - \sin \theta & \cos -(\cos \theta+ i \sin \theta)\end{bmatrix} \begin{bmatrix}v_1\\ v_2\end{bmatrix}=\vec{0}

which reduces to

- i \sin \theta v_1+ \sin \theta v_2=0
-\sin \theta v_1-i \sin \theta v_2=0

I am stumped at this point...how shall I proceed?
Divide those equations by $\sin\theta$ (assuming that $\sin\theta\ne0$).
 
Opalg said:
Divide those equations by $\sin\theta$ (assuming that $\sin\theta\ne0$).

Then we get

- i v_1+ v_2=0 (1)
- v_1-i v_2=0 (2)

v_2=i v_1 from 1

v_2=-v_1/i from 2

1) These contradict? How is the eigenvector obtained from this?

2) what if we have a situation where \theta=0? Then \sin \theta=0
 
bugatti79 said:
Then we get

- i v_1+ v_2=0 (1)
- v_1-i v_2=0 (2)

v_2=i v_1 from 1

v_2=-v_1/i from 2

1) These contradict? How is the eigenvector obtained from this?

2) what if we have a situation where \theta=0? Then \sin \theta=0
1) They don't contradict, because $i^2=-1$ and so $-1/i = i$.

2) If $\theta=0$ then the matrix becomes $\begin{bmatrix}1&0 \\0&1 \end{bmatrix}$ (the identity matrix), with a repeated eigenvalue $1$.
 
Opalg said:
1) They don't contradict, because $i^2=-1$ and so $-1/i = i$.

2) If $\theta=0$ then the matrix becomes $\begin{bmatrix}1&0 \\0&1 \end{bmatrix}$ (the identity matrix), with a repeated eigenvalue $1$.

1) Ok, so the eigenvector for

\lambda_1=e^{i \theta} is \begin{bmatrix}1\\ i\end{bmatrix}

and

\lambda_2=e^{-i \theta} is \begin{bmatrix}1\\ 1/i\end{bmatrix}

To show these 2 vectors are orthogonal I get the inner product

<v_1,v_2>=(1*1)+(i*1/i)\ne 0 but I expect 0...?
 
bugatti79 said:
1) Ok, so the eigenvector for

\lambda_1=e^{i \theta} is \begin{bmatrix}1\\ i\end{bmatrix}

and

\lambda_2=e^{-i \theta} is \begin{bmatrix}1\\ 1/i\end{bmatrix}

To show these 2 vectors are orthogonal I get the inner product

<v_1,v_2>=(1*1)+(i*1/i)\ne 0 but I expect 0...?
The definition of the inner product of two complex vectors is that you have to take the complex conjugate of the second one: if $x = ( x_1,x_2)$ and $y = (y_1,y_2)$ then $\langle x,y\rangle = x_1\overline{y_1} + x_2\overline{y_2}.$
 

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