What Are the Eigenvalues of A Transpose A?

In summary, if A is an m x n matrix with rank(A) = m < n, we can say that 0 is an eigenvalue of A^{T}A and the spectral theorem states that for a symmetric matrix, all eigenvalues are real and the matrix is diagonalizable.
  • #1
3.141592654
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Homework Statement



Let A be an m x n matrix with rank(A) = m < n. As far as the eigenvalues of [itex]A^{T}A[/itex] is concerned we can say that...

Homework Equations





The Attempt at a Solution



If eigenvalues exist, then

[itex]A^{T}A[/itex]x = λx where x ≠ 0.

The only thing I think I can show is that 0 is an eigenvalue:

If 0 is an eigenvalue for [itex]A^{T}A[/itex] then

[itex]A^{T}A[/itex]x = (0)x where x ≠ 0.

N(A) ≠ {0}, so Ax = 0 where x ≠ 0.

Therefore [itex]A^{T}(Ax) = 0[/itex] where x ≠ 0. So λ = 0 is an eigenvalue for [itex]A^{T}A[/itex].

Is there anything else that can be said about the eigenvalues for this matrix?
 
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  • #2
Did you hear about the spectral theorem for symmetric matrices?
 
  • #3
No, that wasn't covered in my course so I suppose that's not what the professor is looking for. Is it relatively ea
 
  • #4
3.141592654 said:
No, that wasn't covered in my course so I suppose that's not what the professor is looking for. Is it relatively ea

If A^T*A*x=lambda*x what happens if you multiply both sides on the left by x^T? No, you don't need the spectral theorem.
 
  • #5
3.141592654 said:
No, that wasn't covered in my course so I suppose that's not what the professor is looking for. Is it relatively ea

Spectral theorem says that a symmetric matrix is diagonalizable.
In particular, a real nxn symmetric matrix has n real eigenvalues.
 

1. What are eigenvalues of A transpose A?

The eigenvalues of A transpose A are the set of numbers that represent the scaling factor by which a nonzero vector is transformed when multiplied by the matrix. They are also known as characteristic values or latent roots.

2. How are eigenvalues of A transpose A calculated?

Eigenvalues of A transpose A can be calculated by finding the roots of the characteristic polynomial of the matrix A. This polynomial is obtained by subtracting the variable lambda from the diagonal elements of A and taking the determinant of the resulting matrix.

3. What is the significance of eigenvalues of A transpose A?

Eigenvalues of A transpose A have several important applications in linear algebra, including determining the stability of a system of differential equations, finding the principal components of a dataset, and optimizing matrix operations.

4. Can a matrix have complex eigenvalues of A transpose A?

Yes, a matrix can have complex eigenvalues of A transpose A. Complex eigenvalues occur when the matrix has complex entries or when the matrix is not symmetric.

5. How do eigenvalues of A transpose A relate to the singular values of A?

The eigenvalues of A transpose A are equal to the squares of the singular values of A. This relationship is important in many applications, such as principal component analysis and matrix decomposition.

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