Linear algebra, eigenvectors and eigenvalues

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SUMMARY

The discussion centers on the properties of eigenvectors in relation to an invertible matrix A. It is established that if v is an eigenvector of A, then v is also an eigenvector of A^2, confirming option B as true. Additionally, it is confirmed that v is an eigenvector of 2A and A^-1, leading to the conclusion that option E (1, 2, and 3) is correct. The mathematical reasoning provided supports these conclusions through the application of eigenvalue definitions and matrix operations.

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  • Understanding of eigenvectors and eigenvalues
  • Familiarity with matrix operations
  • Knowledge of linear transformations
  • Basic concepts of invertible matrices
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  • Study the properties of eigenvectors in relation to scalar multiplication
  • Explore the implications of eigenvalues on matrix powers
  • Learn about the spectral theorem and its applications
  • Investigate the relationship between eigenvectors and matrix inverses
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Students of linear algebra, mathematicians, and anyone interested in the theoretical aspects of matrix operations and eigenvalue problems.

ann.r221
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If v is an eigenvector of an invertible matrix A, which of the following is/are necessarily true?

(1) v is also an eigenvector of 2A
(2) v is also an eigenvector of A^2
(3) v is also an eigenvector of A^-1

A) 1 only
B) 2 only
C) 3 only
D) 1 and 3 only
E) 1,2 and 3

I am pretty sure 2 is true because if we look at Av = (lamba)v.
A^2v = A(lambda)v = (Av)(lambda) = (lambda)v(lambda) = (lambda)^2 v

So A^2v = (lambda)^2v. So that should be that v is an eigenvector for A^2 as well. I am not sure about the others can someone help?
 
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You did so well on b), isn't a) just as easy? (2A)v=? For c) use A^(-1)A=I.
 

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