Jacqueline's question at Yahoo Answers (Eigenvalues of a composition)

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In summary, the question asks to find the eigenvalues and eigenspaces of a 3x3 matrix that rotates by 90 degrees around the z-axis and stretches by a factor of 3 along the z-axis. The resulting matrix has eigenvalues of $\pm i$ and $3$, with corresponding eigenspaces of $(0,0,1)^T$, $(i,1,0)^T$, and $(-i,1,0)^T$. Further questions can be asked in the designated forum.
  • #1
Fernando Revilla
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Here is the question:

Denote by A the 3x3 matrix which rotates by 90 degrees around the z-axis and stretches by a factor of 3 a long the z-axis. Find all eigen values and eigenspaces corresponding to the real eigenvalues.
Would it look something like this..?

0 -3 0
1 0 0
0 0 1

I believes I can the eigenvalues and eigenspaces but I just want to check if my matrix is correct first.

Here is a link to the question:

Denote by A the 3x3 matrix which rotates by 90 degrees around the z-axis? - Yahoo! Answers

I have posted a link there to this topic so the OP can find my response.
 
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  • #2
Hello Jacqueline,

The matrix $A$ corresponds to the following composition: $$A=\begin{bmatrix}{1}&{0}&{0}\\{0}&{1}&{0}\\{0}& {0}&{3}\end{bmatrix}\begin{bmatrix}{\cos \pi/2}&{-\sin \pi/2}&{0}\\{\sin \pi/2}&{\;\;\cos \pi/2}&{0}\\{0}&{0}&{1}\end{bmatrix}=\begin{bmatrix}{0}&{-1}&{0}\\{1}&{0}&{0}\\{0}&{0}&{3}\end{bmatrix}$$ The eigenvalues of $A$ are $\pm i$ and $3$. The corresponding eigenspaces: $$\ker (A-3I)\equiv \left \{ \begin{matrix}-3x_1-x_2=0\\x_1-3x_2=0\\0=0\end{matrix}\right.\Rightarrow B_{3}=\{(0,0,1)^T\}\\\ker (A-iI)\equiv \left \{ \begin{matrix}-ix_1-x_2=0\\x_1-ix_2=0\\(3-i)x_3=0\end{matrix}\right.\Rightarrow B_{i}=\{(i,1,0)^T\}$$ As $A$ is a real matrix, $B_{-i}=\{(\bar{i},\bar{1},\bar{0})^T\}=\{(-i,1,0)^T\}$.

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FAQ: Jacqueline's question at Yahoo Answers (Eigenvalues of a composition)

What are eigenvalues and why are they important in mathematics?

Eigenvalues are a mathematical concept that is used to describe the properties of a linear transformation, or a function that maps vectors from one space to another. They represent the values by which a given vector is scaled when it is transformed by a linear transformation. Eigenvalues are important because they can reveal important properties about a linear transformation, such as its stability, convergence, and other characteristics.

What is a composition of eigenvalues?

A composition of eigenvalues refers to the process of combining two or more linear transformations to create a new transformation. In this case, the eigenvalues of the composition are the eigenvalues of the individual transformations multiplied together. This allows us to analyze the properties of the composition by understanding the properties of each individual transformation.

How do you calculate the eigenvalues of a composition?

The eigenvalues of a composition can be calculated by multiplying the eigenvalues of the individual transformations together. Alternatively, you can also calculate the eigenvalues of the composition by first finding the eigenvalues of each individual transformation and then using matrix multiplication to find the eigenvalues of the overall composition matrix.

What is the relationship between eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are closely related in that eigenvectors represent the directions along which a linear transformation acts by simply scaling the vector, and the eigenvalues represent the magnitude of the scaling. In other words, eigenvectors are the "directions" of a transformation, and eigenvalues are the "strengths" of those directions.

How are eigenvalues used in real-world applications?

Eigenvalues have a wide range of applications in fields such as physics, engineering, and computer science. They are used in image processing, data compression, and pattern recognition, to name a few. In physics, eigenvalues are used to describe the behavior of systems such as vibrating strings or quantum particles. In engineering, they are used in structural analysis and control systems. In computer science, they are used in algorithms for efficient data processing and machine learning.

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