Exchange matrix and positive definiteness

In summary, to show that B = EAE is positive definite, one can show that all the eigenvalues of B are positive, which can be done using the fact that all the eigenvalues of A are positive. This can be achieved by multiplying both sides of EAEx=kx by E, and noting that for this to be a valid proof, it must first be shown that B is symmetric.
  • #1
randommacuser
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Homework Statement



Let E be the exchange matrix (ones on the anti-diagonal, zeroes elsewhere). Suppose A is symmetric and positive definite. Show that B = EAE is positive definite.

Homework Equations


The Attempt at a Solution



I've tried showing directly that for any conformable vector h, h'Bh > 0 whenever h'Ah > 0. This looks like a dead end. I suspect the easiest way to get the result is to show all the eigenvalues of B are positive, using the fact that all the eigenvalues of A are positive. However, I don't know how to show this.
 
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  • #2
randommacuser said:
I suspect the easiest way to get the result is to show all the eigenvalues of B are positive, using the fact that all the eigenvalues of A are positive.
That's a good idea. Suppose k is an eigenvalue of B. Then EAEx=kx for some nonzero x. What happens if you multiply both sides by E?

Also note that for this to be a valid proof, you have to first show that B is symmetric, but this is trivial.
 
  • #3
Beautiful. Thanks for your help.
 

1. What is an exchange matrix?

An exchange matrix is a square matrix that represents a linear transformation between two vector spaces of the same dimension. It is used to describe how the coordinates of a vector change when it is transformed by the linear transformation.

2. What is positive definiteness?

Positive definiteness is a property of a symmetric matrix, where all of its eigenvalues are positive. This means that when a vector is multiplied by the matrix, the resulting vector has a positive inner product with itself, indicating a minimum value for the quadratic form. It is often used in optimization and to determine the nature of a critical point in multivariable calculus.

3. How do you determine if a matrix is positive definite?

A matrix is positive definite if all of its eigenvalues are positive. This can be determined by finding the eigenvalues of the matrix and checking if they are all greater than zero. Alternatively, you can use the Sylvester's criterion, which states that a symmetric matrix is positive definite if and only if all of its leading principal minors are positive.

4. What is the relationship between exchange matrices and positive definiteness?

Exchange matrices can be used to determine the positive definiteness of a symmetric matrix. Specifically, if the eigenvalues of an exchange matrix are all positive, then the corresponding symmetric matrix is positive definite. This is because exchange matrices are related to orthogonal transformations, which preserve positive definiteness.

5. How are exchange matrices used in real-world applications?

Exchange matrices have applications in various fields such as computer graphics, signal processing, and machine learning. In computer graphics, they are used to transform and rotate objects in 3D space. In signal processing, they are used to represent linear filters. In machine learning, they are used in algorithms such as principal component analysis and linear discriminant analysis.

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