Prove: (\lambda)^m is an Eigenvalue of A^m

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In summary, using math induction, it can be shown that for m≥1, (λ)^m is an eigenvalue of A^m and \mathbf{x} is an eigenvector of A^m belonging to (\lambda)^m, based on the given information that A\mathbf{x}=\lambda\mathbf{x} and the assumption that p(k) is true.
  • #1
Dustinsfl
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Let [tex]\lambda[/tex] be an eigenvalue of [tex]A[/tex] and let [tex]\mathbf{x}[/tex] be an eigenvector belonging to [tex]\lambda[/tex]. Use math induction to show that, for [tex]m\geq1[/tex], [tex](\lambda)^m[/tex] is an eigenvalue of [tex]A^m[/tex] and [tex]\mathbf{x}[/tex] is an eigenvector of [tex]A^m[/tex] belonging to [tex](\lambda)^m[/tex].

[tex]A\mathbf{x}=\lambda\mathbf{x}[/tex]

[tex]p(1): A^1\mathbf{x}=(\lambda)^1\mathbf{x}[/tex]\

[tex]p(k): A^k\mathbf{x}=(\lambda)^k\mathbf{x}[/tex]

[tex]p(k+1): A^{k+1}\mathbf{x}=(\lambda)^{k+1}\mathbf{x}[/tex]

Assume p(k) is true.

Since p(k) is true, [tex]p(k+1): A*(A^k\mathbf{x})[/tex]

Not sure if this is correct path to take to the end result. Let alone, if it is if I am going about it right.
 
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  • #2
You were doing ok, except you don't want A^(k+1)(lambda). You want p(k+1): A^(k+1)(x)=A*(A^k(x))=A*(lambda^k*x)=lambda^k*A(x)=?
 
  • #3
Ok I gotcha.
 

1. What does it mean for (\lambda)^m to be an Eigenvalue of A^m?

An Eigenvalue is a special number associated with a square matrix. In this case, when (\lambda)^m is an Eigenvalue of A^m, it means that when A is raised to the mth power, the result is a scalar multiple of the original matrix A. In other words, A^m is a multiple of the identity matrix, with the scalar multiple being (\lambda)^m.

2. How is this statement proven?

This statement can be proven using the definition of Eigenvalues and Eigenvectors. Specifically, we can show that (\lambda)^m is an Eigenvalue of A^m by finding a nonzero vector v such that A^m v = (\lambda)^m v. This can be done by solving a system of linear equations.

3. Why is this statement important?

Proving that (\lambda)^m is an Eigenvalue of A^m can provide important insights into the behavior of the matrix A. It can also be useful in solving systems of linear equations and understanding the dynamics of complex systems.

4. Is this statement always true?

No, this statement is not always true. In order for (\lambda)^m to be an Eigenvalue of A^m, the matrix A must have at least one Eigenvalue. If A has no Eigenvalues, then this statement is not true.

5. How does this statement relate to the concept of matrix powers?

This statement is directly related to the concept of matrix powers. It states that when a matrix A is raised to the mth power, one of its Eigenvalues, (\lambda)^m, will also be the Eigenvalue of the resulting matrix. This shows a relationship between the Eigenvalues of a matrix and its powers.

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