Finding the Basis for Repeated Eigenvalues: Explained

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In summary, when trying to find the basis for a particular matrix with 3 eigenvalues, if two of them are identical, you may only obtain two linearly independent eigenvectors. To find the basis for the repeated eigenvalue, you will need to learn about Jordan Normal Form.
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mathrocks
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I'm trying to find the basis for a particular matrix and I get a 3 eigenvalues with two of them being identical to each other. What do I do to find the basis for the repeated eigenvalue? Will it have the same basis as the original number?

Thanks!
 
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The eigenvectors may or may not span the original space. That is there may only be two eigenvectors. If you solve as usual for the eigenvector you may obtain two linearly independent vectors for it (the repeated eigenvalue) or you may only get one.

If you want the geometric interpetation of this then you need to learn about Jordan Normal Form, or Jordan Canonical Form.
 
  • #3


When dealing with repeated eigenvalues, it is important to remember that each eigenvalue corresponds to a unique eigenvector. This means that even though two eigenvalues may be identical, their corresponding eigenvectors may be different. So, to find the basis for the repeated eigenvalue, you will need to find all the linearly independent eigenvectors associated with that eigenvalue.

To do this, you can use the method of elimination. Start by finding one eigenvector for the repeated eigenvalue by solving the characteristic equation (det(A-λI)=0) and plugging in the repeated eigenvalue. Then, find a second eigenvector by plugging in the same eigenvalue but using a different basis vector. Continue this process until you have found all the linearly independent eigenvectors associated with the repeated eigenvalue.

It is also important to note that the basis for the repeated eigenvalue may not be the same as the original basis. This is because the eigenvectors associated with the repeated eigenvalue may be different from the original eigenvectors. However, the basis for the repeated eigenvalue will still span the same subspace as the original basis.

In summary, when dealing with repeated eigenvalues, you will need to find all the linearly independent eigenvectors associated with that eigenvalue to determine the basis. This basis may be different from the original basis, but it will still span the same subspace. I hope this helps clarify the process for finding the basis for repeated eigenvalues.
 

1. What are repeated eigenvalues?

Repeated eigenvalues, also known as degenerate eigenvalues, are eigenvalues that have a multiplicity greater than 1. This means that there is more than one eigenvector associated with the same eigenvalue.

2. Why do repeated eigenvalues occur?

Repeated eigenvalues occur when the characteristic polynomial of a matrix has a repeated root, meaning that the roots of the polynomial are not distinct. This can happen when there is a linear dependence between the columns of the matrix.

3. How do you handle repeated eigenvalues?

When dealing with repeated eigenvalues, we need to find the corresponding eigenvectors for each repeated eigenvalue. This can be done by solving the system of equations using the eigenvalue as the coefficient. If the eigenvectors are linearly independent, then they can form a basis for the eigenspace associated with the repeated eigenvalue.

4. What is the significance of repeated eigenvalues?

Repeated eigenvalues can provide important information about the structure and behavior of a matrix. They can indicate symmetries, patterns, and other properties of a matrix. They are also important in solving systems of differential equations and in understanding the behavior of dynamical systems.

5. Can a matrix have only repeated eigenvalues?

Yes, a matrix can have only repeated eigenvalues. This means that all of the eigenvalues have multiplicities greater than 1. In this case, the matrix is not diagonalizable, and the eigenvectors cannot form a basis for the vector space.

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