Eigenvalue of product of matrices

In summary, the matrices A and B are real symmetric matrices with specific properties, and the question is how the eigenvalues of their product, as well as the trace of the product, are related to the eigenvalues and non-diagonal terms of A and B. The bounds for the trace of AB are derived from the properties of A and B, with considerations for the sparsity of A and the size of the matrices.
  • #1
mnov
3
0
I have two real symmetric matrices [itex]A[/itex] and [itex]B[/itex] with the following additional properties. I would like to know how the eigenvalues of the product [itex]AB[/itex], is related to those of [itex]A[/itex] and [itex]B[/itex]? In particular what is [itex]\mathrm{trace}(AB)[/itex]?

[itex]A[/itex] contains only 0s on its diagonal. Off diagonal terms are either 0 or 1.
[itex]B[/itex] also contains only 0s on its diagonal. Its off diagonal terms are positive real numbers.

If equalities don't exist, some bounds would also be helpful.

Thanks.
 
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  • #2
mnov said:
I have two real symmetric matrices [itex]A[/itex] and [itex]B[/itex] with the following additional properties. I would like to know how the eigenvalues of the product [itex]AB[/itex], is related to those of [itex]A[/itex] and [itex]B[/itex]? In particular what is [itex]\mathrm{trace}(AB)[/itex]?

[itex]A[/itex] contains only 0s on its diagonal. Off diagonal terms are either 0 or 1.
[itex]B[/itex] also contains only 0s on its diagonal. Its off diagonal terms are positive real numbers.

If equalities don't exist, some bounds would also be helpful.

Thanks.
Let ##a_{i,j}## be the element of matrix A from row i and column j, and let ##b_{i,j}## be the element of matrix A from row i and column j. Then,
$$\operatorname{tr}(\textbf{AB})=\sum_{i}\sum_{j}a_{j,i}b_{i,j}.$$
Thus, from the fact that the non-diagonal terms of A are either 0 or 1, we obtain the bounds that
$$0 \leq \operatorname{tr}(\textbf{AB}) \leq \sum_{i}\sum_{j}b_{i,j}.$$
 
  • #3
Thanks.
I forgot to mention that A is sparse, i.e., most of its off diagonal terms are zero. So the above bound would be pretty bad.
 
  • #4
mnov said:
Thanks.
I forgot to mention that A is sparse, i.e., most of its off diagonal terms are zero. So the above bound would be pretty bad.
Do you have bounds on the sparsity of the matrix? I could probably do a little better with an idea of how dense the matrix is. Also, is there any idea as to the size of the matrices?

I have nothing to do and I want something to work on. :-p
 
  • #5
A is an n x n matrix. m = constant * n of the off diagonal terms are 1. n is large.
 
  • #6
So you have a bound like the trace is smaller than
[tex] m* max_{i,j}\left{ b_{ij} \right} [/tex]
 

FAQ: Eigenvalue of product of matrices

What is the definition of eigenvalue of product of matrices?

The eigenvalue of a product of matrices is a scalar value that represents the magnitude of the vector that remains unchanged when multiplied by the product of the matrices. It is a key concept in linear algebra and is used to solve systems of linear equations.

How is the eigenvalue of product of matrices calculated?

The eigenvalue of product of matrices can be calculated by finding the eigenvalues of each individual matrix and then multiplying them together. Alternatively, it can also be calculated by finding the characteristic polynomial of the product of matrices and solving for its roots.

What is the significance of eigenvalue of product of matrices in real-world applications?

The eigenvalue of product of matrices is used in a variety of real-world applications, such as in computer graphics, where it is used to rotate and scale objects in three-dimensional space. It is also used in physics and engineering to solve systems of equations and model complex systems.

Can the eigenvalue of product of matrices be negative?

Yes, the eigenvalue of product of matrices can be negative. The sign of the eigenvalue depends on the orientation of the vector that remains unchanged when multiplied by the product of the matrices. If the vector is reversed, the eigenvalue will be negative.

Are there any relationships between the eigenvalues of the individual matrices and the eigenvalue of their product?

Yes, there are several relationships between the eigenvalues of the individual matrices and the eigenvalue of their product. For example, the eigenvalues of the product of two matrices will always be the product of their individual eigenvalues. Additionally, if one of the matrices is invertible, the eigenvalues of the product will be the same as the eigenvalues of the other matrix.

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