Proving Eigenvalues Equality of A & B Matrices

In summary, the conversation discusses how to demonstrate that two matrices, A and B, have the same eigenvalues. It is suggested to show that A and B are similar, which would require finding a matrix P such that P^(-1)AP=B. Another approach is to write det (I*lambda-A)=0 and det (I*lambda-B)=0, which would give the same values for lambda. It is noted that if LU=U^(-1)ULU=LU, then the equality is true. It is also mentioned that L or U may be a unitary matrix, which would affect the solution. Ultimately, it is concluded that the problem is solved and thanks are given to those who provided assistance.
  • #1
fluidistic
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Homework Statement


Let A=LU and B=UL, where U is an upper triangular matrix and L is a lower triangular matrix. Demonstrate that A and B have the same eigenvalues.


Homework Equations


Not sure.


The Attempt at a Solution


I know that if I can show that A and B are similar (so if I can find a matrix P such that P^(-1)AP=B) they have the same eigenvalues. But I didn't find P yet, nor do I know really how to search efficiently for P.

Another route I've thought of is to write det (I*lambda-A)=0 gives the same values for lambda as det (I*lambda-B)=0. I've thought of using det (A)=det (LU)=det L * det U = det U * det L = det B... but still can't reach anything I find useful.
Any tip is greatly appreciated.
 
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  • #2
how about multiplying thorugh by the inverse of L.. you may need to show it exists
 
  • #3
Try to write A in terms of B (or the other way around), and see what you get!
 
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  • #4
hint was probably enough
 
  • #5
LU=U^(-1)ULU=LU so the equality is true. Notice that LU=A and UL=B. I have that P=U, thus LU is similar to UL (A similar to B) so they share the same eigenvalues.

Now I must justify the use of the inverse of U. Well I believe its element on the diagonal are all non zero. So if the matrix is nxn, the span of its column vector is R^n so it is invertible, hence U^-1 exists.


Is that well justified?
 
  • #6
fluidistic said:
LU=U^(-1)ULU=LU so the equality is true. Notice that LU=A and UL=B. I have that P=U, thus LU is similar to UL (A similar to B) so they share the same eigenvalues.

Now I must justify the use of the inverse of U. Well I believe its element on the diagonal are all non zero. So if the matrix is nxn, the span of its column vector is R^n so it is invertible, hence U^-1 exists. Is that well justified?

It would be if the problem stated that the elements of U along the diagonal are nonzero. Does it? Other versions of this problem I've found say that L or U is a UNIT triangular matrix. Did you miss a word in the problem statement?
 
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  • #7
Dick said:
It would be if the problem stated that the elements of U along the diagonal are nonzero. Does it? Other versions of this problem I've found say that L or U is a UNIT triangular matrix. Did you miss a word in the problem statement?

You pointed out a very interesting thing to me. In my assignment it isn't specified. But most of the exercises assigned are taken from Kincaid's book on numerical analysis. I just checked there and it clearly states L to be UNITARY.
If only they had stated this in my assignment I could have guessed that I had to take the inverse of L. :) Well I'm not 100% sure but probably.
So basically the problem is solved now... thanks guys.
 
  • #8
fluidistic said:
You pointed out a very interesting thing to me. In my assignment it isn't specified. But most of the exercises assigned are taken from Kincaid's book on numerical analysis. I just checked there and it clearly states L to be UNITARY.
If only they had stated this in my assignment I could have guessed that I had to take the inverse of L. :) Well I'm not 100% sure but probably.
So basically the problem is solved now... thanks guys.

Their bad. Very welcome.
 

1. What are eigenvalues and why are they important?

Eigenvalues are a special set of numbers that represent the scaling factor of a vector in a linear transformation. They are important because they provide information about the behavior and characteristics of a matrix, such as its invertibility and determinant.

2. How do you prove the equality of eigenvalues between two matrices A and B?

To prove the equality of eigenvalues between two matrices A and B, we need to show that they have the same characteristic polynomial. This can be done by finding the determinant of both matrices and equating them, as the characteristic polynomial is the determinant of the matrix minus a variable lambda. If the two determinants are equal, then the characteristic polynomials are also equal, and therefore the eigenvalues are equal.

3. Can two matrices have the same eigenvalues but different eigenvectors?

Yes, two matrices can have the same eigenvalues but different eigenvectors. This means that they have the same scaling factor, but the direction of the vector is different for each matrix. In other words, the matrices have similar behavior, but they are not equivalent.

4. What is the relationship between eigenvalues and diagonalization?

Eigenvalues are used in the process of diagonalization, which is a method for simplifying a matrix by transforming it into a diagonal matrix. The eigenvalues of the original matrix become the entries on the diagonal of the diagonalized matrix. This process is useful for solving systems of linear equations and finding the powers of a matrix.

5. Can a matrix have complex eigenvalues?

Yes, a matrix can have complex eigenvalues. Complex eigenvalues occur when the characteristic polynomial has complex roots, indicating that the matrix has complex eigenvalues. This is common in matrices with complex entries or when dealing with systems involving trigonometric or exponential functions.

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