Eigenvalues/eigenvectors and linear transformation

In summary, the conversation discusses the problem of finding the eigenvalues and corresponding eigenvectors of a linear operator T on the vector space of nxn matrices on the real field. The characteristic polynomial and the condition for having eigenvalues are mentioned. The conversation also expresses doubts about how to determine the eigenvalues and why they should be +/- 1.
  • #1
quasar_4
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Homework Statement



Let T be a linear operator on the vector space of nxn matrices on the real field, defined by T(A) = transpose A.

Show that +/- 1 are the only eigenvalues of T, and describe corresponding eigenvectors.

Homework Equations



The characteristic polynomial is given by f(t) = det ( [T]b - tIn) = 0, where [T]b is the matrix representation of T on some basis b and In is the nxn identity matrix. The eigenvalues should be given by the roots to f(t).

Also, there are only eigenvalues if det ([T]b - tIn) = 0.

The Attempt at a Solution



The only thing I can really see is that for any nxn matrix A, its transpose will have the same diagonal elements as it. But since I'm talking about the matrix representation of T, not matrix A, I'm not really sure how this helps. If we were to choose a basis and write [T]b, I am also fairly sure that this wouldn't be an upper triangular matrix, so I don't see how we can figure out what the eigenvalues would be anyway. I definitely don't see why the eigenvalues should be +/- 1. Any help would be great -- I just need help in seeing how this is ever the case. :grumpy:
 
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  • #2
Think twice before answering.
 
  • #3
Oh, nice answer!
 

1. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are concepts in linear algebra that are associated with a linear transformation of a vector space. Eigenvalues are scalar values that represent how a particular eigenvector is scaled by the transformation. Eigenvectors are non-zero vectors that remain in the same direction after the transformation.

2. Why are eigenvalues and eigenvectors important?

Eigenvalues and eigenvectors are important because they provide a way to understand and analyze linear transformations. They can help us identify important directions or patterns in a transformation, and can also be used in a variety of applications, such as data analysis and image processing.

3. How do you find eigenvalues and eigenvectors?

To find the eigenvalues and eigenvectors of a linear transformation, we can use the characteristic polynomial of the transformation's matrix. The eigenvalues are the roots of this polynomial, and the corresponding eigenvectors can be found by solving a system of linear equations using the eigenvalues.

4. What is the relationship between eigenvalues and eigenvectors?

The relationship between eigenvalues and eigenvectors is that each eigenvector is associated with a specific eigenvalue. The eigenvalue represents how the eigenvector is scaled by the transformation. In other words, the eigenvector is the direction that remains unchanged by the transformation.

5. Can a linear transformation have multiple eigenvalues and eigenvectors?

Yes, a linear transformation can have multiple eigenvalues and eigenvectors. In fact, most linear transformations have multiple eigenvectors associated with different eigenvalues. This allows us to understand the transformation in terms of different directions or patterns that are preserved or scaled by the transformation.

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