Comparing Eigenvalues for Sarah & Janie: Is It Compatible?

In summary, the conversation discusses the concept of eigenvectors and their relationship to eigenvalues for a given matrix A. Sarah and Janie both find eigenvectors that form a base for their respective eigenspaces, and the question is raised whether their solutions are compatible. The conversation also touches on the uniqueness of eigenvectors to their corresponding eigenvalues and clarifies that there can be an infinite number of eigenvectors for each eigenvalue, but each eigenvector belongs to only one eigenvalue.
  • #1
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Suppose for a given matrix A, Sarah finds the eigenvectors v1 = [1 3 4 5]' and v2 = [5 6 3 4]' form a base for eigenspace of labmda = 2. Now suppose Janie finds the eigenvectors v3 = [1 2 2 3]' and v4 = [7 8 7 6]' form a base for eigenspace of lambda = 4. Is Janie's solution compatible with Sarah's?

Okay, so I know that if v1 and v2 form a base for the eigenspace of lambda, they must be linearly independent. This same fact goes for v3 and v4. Now, my question is, to check whether or not Janie's solution is compatible with Sarah's, would I simply make sure that they are all linearly independent? If so, then they are compatible? The part that's throwing me off is that they belong to different eigenvalues.

So, can you have the same vector (or a linear combination of it), only belonging to a different eigenvalue of the same matrix? Or are the vectors unique to the eigenvalues?

Help much appreciated.
 
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  • #2
What does "compatible" here mean?
So, can you have the same vector (or a linear combination of it), only belonging to a different eigenvalue of the same matrix? Or are the vectors unique to the eigenvalues?
You can check this out easily. Suppose for an eigenvalue [tex] \lambda_1[/tex] you have 2 eigenvectors v1 and v2 which are linearly independent. Then suppose we have an eigenvector v3 associated with a different [tex] \lambda_2[/tex]. And suppose [tex]v_3 = a_1 v_1 + a_2 v_2[/tex].

What can you say about [tex]Av_3 = \lambda_2v_3[/tex] and [tex]Av_3 = A(a_1v_1 + a_2 v_2)[/tex]?
 
  • #3
Okay, so we know that if lambda is an eigenvector of A, then A(lambda) = (lambda)x, where x is the eigenvector. So in the case above Av3 = A(a1v1 + a2v2), it would be implied that a1v1 + a2v2 would also be an eigenvector belonging to lambda1.

So, then, eigenvectors are unique to the eigenvalues. Yes?
 
  • #4
You can have an infinite number of linearly dependent eigenvectors for each eigenvalue but only one eigenvalue for each eigenvector. If that is what you meant, then yes you're right.
 
  • #5
Thank you! I got it now.
 

Related to Comparing Eigenvalues for Sarah & Janie: Is It Compatible?

1. What are eigenvalues and why are they important in this comparison?

Eigenvalues are a concept in linear algebra that represent the scaling factor of a vector in a transformation. In the context of this comparison, they are used to determine the compatibility between two individuals, Sarah and Janie, by analyzing the similarities and differences in their respective data sets.

2. How are eigenvalues calculated for Sarah and Janie?

Eigenvalues are calculated using a mathematical process called diagonalization. This involves finding the characteristic polynomial of a matrix, solving for its roots, and then substituting those roots back into the original equation to find the corresponding eigenvalues.

3. What do the eigenvalues tell us about Sarah and Janie's data sets?

The eigenvalues provide information about the magnitude and direction of variation in each data set. A higher eigenvalue indicates a greater amount of variation, while a lower eigenvalue suggests less variation. The direction of the eigenvalue also indicates the dominant pattern of variation in the data set.

4. How do we determine if the eigenvalues are compatible between Sarah and Janie?

To determine compatibility, we compare the magnitudes and directions of the eigenvalues for both individuals. If the eigenvalues are similar in magnitude and direction, it suggests that there are similarities in the variation of their data sets, making them more compatible.

5. What are some limitations of using eigenvalues to compare Sarah and Janie's data sets?

Eigenvalues can only capture linear patterns of variation, so they may not be suitable for data sets with non-linear relationships. Additionally, the accuracy of the comparison relies on the quality and completeness of the data sets, so any missing or erroneous data may affect the results. Lastly, the interpretation of eigenvalues can be subjective and may require additional statistical analysis to draw meaningful conclusions.

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