Matrix of eigenvectors, relation to rotation matrix

In summary: However, in this case, S is a rotation by 45 degrees. In summary, when asked about the relationship between the rotation matrix and S, the correct answer would be that S is the rotation matrix for theta=45 degrees.
  • #1
Fluxthroughme
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So I am given [itex]B=\begin{array}{cc} 3 & 5 \\ 5 & 3 \end{array}[/itex]. I find the eigenvalues and eigenvectors: 8, -2, and (1, 1), (1, -1), respectively. I am then told to form the matrix of normalised eigenvectors, S, and I do, then to find [itex]S^{-1}BS[/itex], which, with [itex]S = \frac{1}{\sqrt{2}}\begin{array}{cc} 1 & 1 \\ 1 & -1 \end{array}[/itex], I get [itex]\begin{array}{cc} 8 & 0 \\ 0 & -2 \end{array}[/itex]. All great and dandy, that's the correct answer, and the text then asks me for the rotation matrix, which I give as [itex]\begin{array}{cc} cos\theta & -sin\theta \\ sin\theta & cos\theta \end{array}[/itex]. However, when asked how the rotation matrix and S are related, I am clearly stumped; checking the answers, they have used [itex]S = \frac{1}{\sqrt{2}}\begin{array}{cc} 1 & -1 \\ 1 & 1 \end{array}[/itex], which I think is a valid choice, since the eigenvector could have been (1,-1) or (-1,1). But since I made a different choice, I cannot get the answer they are looking for (That S is the rotation matrix for theta is 45).

Thus, have I made a mistake in my calculation? In my reasoning? In my assumptions? Or is it just that I was unlucky to have picked the way I did and the question wasn't expecting that (I doubt the latter)?
 
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  • #2
You're just unlucky. The way you chose the eigenvectors, S is a rotation combined with a reflection across the line y=x. As you saw, you can choose the eigenvectors so that S is just a rotation.
 

Related to Matrix of eigenvectors, relation to rotation matrix

1. What is a matrix of eigenvectors?

A matrix of eigenvectors is a square matrix that contains the eigenvectors of a given matrix as its columns. Each eigenvector corresponds to an eigenvalue, and together they represent the behavior of the original matrix when multiplied.

2. How is a matrix of eigenvectors related to a rotation matrix?

A rotation matrix can be represented by a matrix of eigenvectors, where the eigenvectors are the basis vectors of the rotated coordinate system. The eigenvalues represent the scaling of each eigenvector and determine the angle of rotation.

3. What is the significance of the eigenvalues in a matrix of eigenvectors?

The eigenvalues in a matrix of eigenvectors represent the scaling of each eigenvector in the original matrix. In the case of a rotation matrix, the eigenvalues determine the angle of rotation. In general, the eigenvalues and eigenvectors provide important information about the behavior of a matrix when multiplied.

4. Can a matrix of eigenvectors have more than one solution?

Yes, a matrix of eigenvectors can have multiple solutions. This occurs when there are repeated eigenvalues, meaning that there are multiple eigenvectors with the same eigenvalue. In this case, the eigenvectors form a basis for the same subspace.

5. How is a matrix of eigenvectors used in data analysis?

In data analysis, a matrix of eigenvectors can be used to perform dimensionality reduction and identify the most important features in a dataset. This is done by selecting the eigenvectors with the highest corresponding eigenvalues, which represent the most significant patterns in the data.

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