Does the notation used for the span part of an eigenvector matter?

In summary, the conversation discusses finding eigenvectors for a given matrix. The matrix A is given and the eigenvalue -1 is used to find the eigenvector. It is mentioned that the notation for the "span" part of the eigenvector does not matter as long as it is a real multiple of the vector.
  • #1
amcavoy
665
0
If you have the following kernel (I think that's what it's called):

[tex]A-\lambda I=\begin{pmatrix}4 & 1 \\ 4 & 1\end{pmatrix}[/tex]

You could write the eigenvector as:

[tex]\operatorname{span}\begin{pmatrix}1 \\ -4\end{pmatrix}[/tex]

My question is: does it matter how you write the "span" part of it?

For instance, would [tex]\operatorname{span}\begin{pmatrix}-1 \\ 4\end{pmatrix}[/tex] be preferred (or different) than what I have above?

Thanks for your help.
 
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  • #2
[tex]A-\lambda I=\begin{pmatrix}4 & 1 \\ 4 & 1\end{pmatrix}[/tex]

doesn't make sense- there's no λ on the right hand side! Did you mean
[tex]A=\begin{pmatrix}4 & 1 \\ 4 & 1\end{pmatrix}[/tex] and that is the linear operator you want to find eigenvectors for?

In that case, there are two eigenvalues: 0 and 2.

Taking 0 as the eigenvalue leads to 4x-y=0 or y= 4x. The eigenvectors are spaned by [1, 4] (not [-1, 4]).
Taking 2 as the eigenvalue leads to 4x-y= 2x or y= -2x. The eigenvectors are spanned by [1, -2].

Because the "span" of a single vector is all real multiples of the vector, and -1 is a real number, it doesn't matter whether you use [-1, 4] or [1, -4] or [-1,2] instead of [1,-2].
 
  • #3
HallsofIvy said:
[tex]A-\lambda I=\begin{pmatrix}4 & 1 \\ 4 & 1\end{pmatrix}[/tex]

doesn't make sense- there's no λ on the right hand side! Did you mean
[tex]A=\begin{pmatrix}4 & 1 \\ 4 & 1\end{pmatrix}[/tex] and that is the linear operator you want to find eigenvectors for?

In that case, there are two eigenvalues: 0 and 2.

Taking 0 as the eigenvalue leads to 4x-y=0 or y= 4x. The eigenvectors are spaned by [1, 4] (not [-1, 4]).
Taking 2 as the eigenvalue leads to 4x-y= 2x or y= -2x. The eigenvectors are spanned by [1, -2].

Because the "span" of a single vector is all real multiples of the vector, and -1 is a real number, it doesn't matter whether you use [-1, 4] or [1, -4] or [-1,2] instead of [1,-2].

Sorry, I should have elaborated. The original matrix (A) was:

[tex]\begin{pmatrix}3 & 1 \\ 4 & 0\end{pmatrix}[/tex]

And the eigenvalue I was using was -1. I didn't think that mattered in relation to my question (because my ? dealt with notation), but I don't really know all of the terms, so I appologize.

Thanks for answering though, the last part of your post answered my question.
 

What is an eigenvector kernel problem?

An eigenvector kernel problem is a mathematical problem that involves finding the eigenvectors of a given kernel matrix. This is important in many areas of science, as eigenvectors can provide insights into the underlying structure and behavior of complex systems.

Why is finding eigenvectors important?

Eigenvectors are important because they represent the directions in which a linear transformation acts by simply stretching or compressing. This can be useful in understanding the behavior of systems, as well as in solving various mathematical problems.

How is an eigenvector kernel problem solved?

An eigenvector kernel problem is typically solved by finding the eigenvalues of the kernel matrix, which are then used to calculate the corresponding eigenvectors. This can be done through various mathematical methods, such as the power method or the QR algorithm.

What are some applications of eigenvector kernel problems?

Eigenvector kernel problems have numerous applications in various fields, including physics, engineering, and data analysis. For example, they can be used in quantum mechanics to study the behavior of particles, or in image processing to identify patterns and features in images.

Are there any challenges associated with solving eigenvector kernel problems?

Yes, there are some challenges associated with solving eigenvector kernel problems. These can include dealing with large, complex matrices, numerical instability, and finding the appropriate mathematical method to use for a specific problem. Additionally, interpreting the results of eigenvector calculations can also be challenging and may require further analysis.

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