Comparing Eigenvalues for Sarah & Janie: Is It Compatible?

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Homework Help Overview

The discussion revolves around the compatibility of eigenvectors associated with different eigenvalues for a given matrix A. The original poster presents eigenvectors found by two participants, Sarah and Janie, and questions whether their solutions can coexist given they correspond to different eigenvalues.

Discussion Character

  • Conceptual clarification, Assumption checking

Approaches and Questions Raised

  • Participants explore the concept of linear independence of eigenvectors and their relationship to eigenvalues. Questions are raised about the uniqueness of eigenvectors to their respective eigenvalues and whether a vector can belong to multiple eigenvalues.

Discussion Status

The discussion is active with participants questioning the definitions and implications of eigenvectors and eigenvalues. Some guidance has been offered regarding the uniqueness of eigenvectors, but no consensus has been reached on the compatibility of the solutions presented by Sarah and Janie.

Contextual Notes

Participants are navigating the definitions of compatibility in the context of eigenvalues and eigenvectors, with some uncertainty about the implications of linear combinations of eigenvectors. The discussion reflects a need for clarity on these foundational concepts.

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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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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 \lambda_1 you have 2 eigenvectors v1 and v2 which are linearly independent. Then suppose we have an eigenvector v3 associated with a different \lambda_2. And suppose v_3 = a_1 v_1 + a_2 v_2.

What can you say about Av_3 = \lambda_2v_3 and Av_3 = A(a_1v_1 + a_2 v_2)?
 
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?
 
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.
 
Thank you! I got it now.
 

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