Can the Eigenvalues of A Determine if A^3=A?

In summary, the eigenvalues of a matrix A can determine if A^3=A by examining the relationship between the eigenvalues and the characteristic polynomial of A. If the characteristic polynomial has distinct roots, then A^3=A is possible. However, if the characteristic polynomial has repeated roots, then A^3=A is not possible. Additionally, if A is diagonalizable, then A^3=A is possible if and only if all eigenvalues are either 0 or 1.
  • #1
noon0788
22
0
Hey, I'm wondering if I have a known set of eigenvalues (-1, +1, 0) for A, if I can prove that the matrix A = A3?

I can prove that if A3 = A, that the eigenvalues would be −1, +1, and 0. The following is the proof:

A*k=lambda*k
A3*k=lambda3*k
Since A=A3, A*k=A3*k
lambda*k=lambda3*k
lambda*k - lambda3*k = 0
lambda - lambda3 = 0
lambda*(1-lambda2) = 0
lambda = 0, -1, +1


Is there any way to prove it the other way around? If I know that the eigenvalues are 0, -1, and +1, can I prove that A3 = A?

Thanks!
 
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  • #2
Welcome to PF!

Hi noon0788! Welcome to PF! :smile:

Hint: A is linear, so if three eignevectors are p q and r, what is A3(ap + bq + cr) ? :wink:

(alternatively, write A = QPQ-1)
 
  • #3
Thanks tiny-tim, but I'm not quite sure what you mean by A3(ap + bq + cr).

When you say A = QPQ-1, do you mean I should diagonalize the matrix? Here's what I'm thinking:

C-1AC = D (where d is the diagonalized matrix)

D = [1, 0, 0; 0, −1, 0; 0, 0, 0]

det(C-1)*det(A)*det(C) = det(D)
det(C-1)*det(C)*det(A) = det(D)
det(A) = det(D)
det(D) = 0
det(A) = 0

Maybe I'm way off...
 
  • #4
I mean where a b and c are any numbers.

(And why are you using determinants?? :confused:)
 
  • #5
:/ I don't know why I'm using determinants. I guess what I'm confused by is your notation of R3(ap+bq+cr). Is that I just don't recognize that notation. I'm familiar with matrices, but maybe not as familiar as I thought I was.
 
  • #6
A3(ap+bq+cr) is matrix A followed by matrix A followed by matrix A followed by the sum of a times vector p plus b times vector q plus c times vector r, with Ap = p, Aq = -q, Ar = 0.
 
  • #7
Thanks tiny-tim, but I think my question might just be a bit over my head. I'm confused as to why p q are r are vectors now. I thought they were eigenvalues. Also, the Ap=p stuff is confusing me too. Here's what I've got so far...

A3(a(1) + b(-1) + c(0))
A3(a-b)
A3a - A3b

Thanks for your help.
 
  • #8
are a &b scalars or vectors?

I'll try to explain the idea better: the three eigenvectors are independent, therefore they create a basis to a three-tuple column vectors. Let's call them [tex]e_{1},e_{0},e_{-1}[/tex] (the index corresponds the eigenvalue).
Now any other vector will be:

[tex]\vec{v}=v_{1}\vec{e}_{1}+v_{0}\vec{e}_{0}+v_{-1}\vec{e}_{-1}[/tex]

Now, what is Av and what is [tex]A^{3}v[/tex]?
 
  • #9
Hi noon0788! :smile:

(just got up :zzz: …)
noon0788 said:
Thanks tiny-tim, but I think my question might just be a bit over my head. I'm confused as to why p q are r are vectors now. I thought they were eigenvalues.

No, they're eigenvectors.

(That's why Ap = p, with p's eigenvalue = 1 :wink:)

ok, now, for any numbers a b and c, what is A(ap + bq + cr) ? :smile:
 
  • #10
A(ap + bq + cr)
= A(ap) + A(bq) + A(cr)
= a(Ap) + b(Aq) + c(Ar)
= a + b
right?
 
  • #11
noon0788 said:
A(ap + bq + cr)
= A(ap) + A(bq) + A(cr)
= a(Ap) + b(Aq) + c(Ar)
= a + b
right?
Well, no! For one thing, on the left side you have a vector, A(ap+ bq+cr), and on the right you have a number, a+ b. p, b, and q are eigenvectors of A with eigenvalues of 1, -1, and 0, respectively.

A(ap+ bq+ cr)= (1)ap+ (-1)bq+ (0)cr= ap- bq. A2(ap+bq+cr)= A(ap- bq)= ap-(-bq)= ap+ bq. And, finally, A3(ap+ bq+ cr)= A(ap+bq)= ap- bq.

My first thought was to use the fact that every matrix satisfies its own characteristic equation. Since A has eigenvalues 1, -1, and 0, its characteristic equation is x(x+1)(x- 1)= x^3- x.
 
  • #12
I think I understand it. Just one more question. Am I allowed to distribute A in A(ap + bq + cr)? So it would become aAp + bAq + cAr?
 
  • #13
Yes, because A is linear (and a b and c are scalars). :smile:
 

1. What are eigenvalues?

Eigenvalues are values associated with a square matrix that represents how the matrix stretches or compresses a vector in a particular direction.

2. How are eigenvalues calculated?

Eigenvalues are calculated by solving the characteristic equation of a matrix, which is given by det(A - λI) = 0, where A is the matrix and λ is the eigenvalue.

3. What is the significance of A^3=A in relation to eigenvalues?

The equation A^3=A is known as the Cayley-Hamilton theorem and it states that every square matrix satisfies its own characteristic equation. This means that the eigenvalues of A are also the roots of its characteristic equation.

4. How is the proof for A^3=A derived?

The proof for A^3=A is based on the Cayley-Hamilton theorem and the fact that the characteristic polynomial of a matrix can be expressed in terms of its eigenvalues. By substituting the eigenvalues into the characteristic polynomial, we can show that A^3=A.

5. What are some real-world applications of eigenvalues and A^3=A?

Eigenvalues and A^3=A have various applications in fields such as physics, engineering, and computer science. They are used to analyze the stability of physical systems, solve differential equations, and compress data in image and signal processing.

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