Distinct Eigenvalues and Eigenvectors in Matrix Multiplication

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In summary, using the information given, JerryKelly showed that if BX=0 then B is an eigenvalue of A, and to show that X is an eigenvector of B we must consider ABX and use the information given. The remaining problem is to show that X is in fact an eigenvector of B.
  • #1
JerryKelly
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Let A be an nxn mx with n distinct eigenvalues and let B be an nxn mx with AB=BA. if X is an eigenvector of A, show that BX is zero or is an eigenvector of A with the same eigenvalue. Conclude that X is also an eigenvector of B.



I could show BX is zero or is an eigenvector of A with the same eigenvalue, but i don't know how to Conclude that X is also an eigenvector of B. Does anyone know how to do it? Thanks!
 
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  • #2
Why don't we ever get neat questions like that?

I have an idea how to tackle this, but I can't help you at this moment.
 
  • #3
why you cannot help me?
JasonRox said:
Why don't we ever get neat questions like that?
I have an idea how to tackle this, but I can't help you at this moment.
 
  • #4
The answer follows from all the definitions given in one line:

If BX=0 we're done, if not to show BX is an eigenvalue of A we consider ABX and use the information in the question.
 
  • #5
But that was what JerryKelly had already shown...
 
  • #6
matt grime said:
The answer follows from all the definitions given in one line:

If BX=0 we're done, if not to show BX is an eigenvalue of A we consider ABX and use the information in the question.

Yes, that part he said he could do. The remaining problem is to show that X is in fact an eigenvector of B.

You haven't used the fact that A has n distince eigenvalues. If that is true then there exist a basis for the vector space consisting of eigenvectors of A.
 
  • #7
How many eigenvectors does A have for any particular eigenvalue?
 
  • #8
Muzza said:
But that was what JerryKelly had already shown...
But it also proves the rest of the question (admittedly it is a trivial observation since any question that is doable is doable because of the information in the question, but here it is a case of follow your nose). We have proved B preserves (generalized) eigenspaces (of A) which are all 1-d according to the information in the question, thus answering the last part.
 
  • #9
Since matt grime is about to burst trying to give clues without giving the whole thing, I'm going to give up and spell it out in "donkey steps".

Given A and B are n by n matrices with AB= BA.
1. If x is an eigenvector of A with eigenvalue [itex]\lambda[/itex] then Bx is also also an eigenvector of A with eigenvalue [itex]\lambda[/itex].
(A(Bx))= (AB)x= (BA)x= B(Ax)= B([itex]\lambda[/itex]x)= [itex]\lambda[/itex](Bx))

2. If lambda is an eigenvalue of A then the set of all vectors x such that Ax= [itex]\lambda[/itex]x forms a subspace (often called the "eigenspace" of [itex]\lambda[/itex]). Further the eigenspaces of two distinct eigenvalues have only 0 in common.

3. Since A is an n by n matrix it is on an n dimensional vector space.

4. Since A has n distinct eigenvalues, each eigenspace has dimension 1.

5. If two vectors are in the same one-dimensional subspace then one is a multiple of the other.

6. Since x and Bx are in the same one-dimensional eigenspace, Bx is a multiple of x.
 
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  • #10
Hey, no doing the whole problem for the OP!
 
  • #11
Thanks for help,HallsofIvy! It made perfert sence! for the first step, it is so similar what i was doing. I was doing ABx=BAx=B[itex]\lambda[/itex]x=[itex]\lambda[/itex]Bx.
since A(Bx)=[itex]\lambda[/itex](Bx)
Bx=0 or Bx is eigenvector.
 
  • #12
your way is seeing better than my way. Thanks,agian! It is very helpful.
 
  • #13
Hurkyl said:
Hey, no doing the whole problem for the OP!
Mea Culpa, Mea Culpa, Mea Maxima Culpa.
 

1. What are eigenvalues and why are they important?

Eigenvalues are a mathematical concept that represent the scalar values that a linear transformation may have. They are important because they provide information about the properties of a matrix, such as its determinant, trace, and rank. They also play a crucial role in solving systems of linear equations and in applications like machine learning and signal processing.

2. How do I find the eigenvalues of a matrix?

There are several methods for finding the eigenvalues of a matrix, including using the characteristic polynomial, the power method, and the QR algorithm. These methods involve manipulating the matrix and solving equations to find the values. There are also computer programs and calculators that can calculate eigenvalues for you.

3. What does it mean for a matrix to have distinct eigenvalues?

A matrix has distinct eigenvalues if each eigenvalue is unique, meaning it does not have any repeated values. This is important because it allows us to easily find the eigenvectors for each eigenvalue, which can provide useful information about the matrix and its transformation.

4. Can a matrix have more than one distinct eigenvalue?

Yes, a matrix can have multiple distinct eigenvalues. In fact, most matrices have multiple distinct eigenvalues. This is because the eigenvalues are determined by the properties of the matrix, such as its size and elements, and there are many possible combinations that can result in distinct eigenvalues.

5. How do distinct eigenvalues impact the diagonalizability of a matrix?

A matrix with distinct eigenvalues is always diagonalizable, meaning it can be transformed into a diagonal matrix by changing the basis. This is because each eigenvalue has a corresponding eigenvector, and these eigenvectors form a basis for the matrix. This is useful for simplifying calculations and solving systems of equations involving the matrix.

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