Eigenvalue/Eigenvector by Inspection

  • Thread starter BraedenP
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In summary, without calculation, the eigenvalues of A are 0 and 15, and the two linearly independent eigenvectors are <0, 1, -1> and <1, 0, -1>. These can be found by observing the properties of the matrix, such as the fact that all elements are the same and the columns are linearly dependent.
  • #1
BraedenP
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Homework Statement


Without calculation, find one eigenvalue and two linearly independent eigenvectors of [tex]A=\begin{bmatrix}
5 & 5 & 5\\
5 & 5 & 5\\
5 & 5 & 5
\end{bmatrix}[/tex]

Justify your answer.


Homework Equations



N/A

The Attempt at a Solution



This question would be incredibly easy if I could calculate the answers, but I'm not allowed to do that. How would I go about finding the eigenvalues/vectors simply by inspection?

I could use an eigenvalue of 0 to find one eigenvector, but given that the two eigenvectors I need have to be independent, I can't simply use another multiple of that eigenvector to be my second one.. I think I need to find the other eigenvalue, which I calculated to be 15, by inspection somehow.
 
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  • #2
Since all of the elements of A are the same, there's an obvious eigenvector with the same property. That gives you one nonzero eigenvalue. Also, since the columns of A are linearly dependent, you know at least one other eigenvalue. Since the 3 columns are actually equal, you know that this eigenvalue is repeated. Since the value of this repeated eigenvalue is very special, you can easily guess at the form of the other 2 eigenvectors.
 
  • #3
We clearly have x+ y+ z= 0 for any eigenvector <x, y, z>. Taking x=0, z= -y and taking y= 0, z= -x. n That gives the two independent eigenvectors.
 

FAQ: Eigenvalue/Eigenvector by Inspection

What is an eigenvalue?

An eigenvalue is a scalar quantity associated with a particular linear transformation or matrix. It represents the amount by which a vector is scaled when it is transformed by the matrix.

What is an eigenvector?

An eigenvector is a vector that remains in the same direction after being transformed by a linear transformation or matrix. Its corresponding eigenvalue represents the scale factor of the transformation.

What is the importance of eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are important because they allow us to understand how a linear transformation or matrix affects vectors in a given space. They also have numerous applications in fields such as physics, engineering, and data analysis.

How are eigenvalues and eigenvectors calculated by inspection?

Eigenvalues and eigenvectors can be calculated by inspection for smaller matrices by following a few steps. First, find the characteristic equation for the matrix. Then, solve for the roots of the equation, which will be the eigenvalues. Finally, plug in each eigenvalue into the characteristic equation and solve for the corresponding eigenvector.

What are the limitations of calculating eigenvalues and eigenvectors by inspection?

Calculating eigenvalues and eigenvectors by inspection becomes much more difficult and time-consuming for larger matrices. It also requires a good understanding of linear algebra and mathematical equations. Additionally, it may not always be possible to find exact eigenvalues and eigenvectors by inspection, and numerical methods may be needed instead.

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