Are Eigen Vectors the Same for Eigen Values of Transpose A and A?

In summary, the conversation discusses the relationship between eigenvalues and eigenvectors of a square matrix A over a field F. It is stated that if k is an eigenvalue of A, then it is also an eigenvalue of A^t and has the same eigenvectors. The solution space for A is found by solving (A-kI)x=0 and has a dimension of n - ro(a). It is also mentioned that rho(a)=rho(a^t) and |A|=|A^t|. The conversation concludes with a question about proving that the eigenvectors are the same for every eigenvalue.
  • #1
nhrock3
415
0
A is a square matrix over F field

if k is the eigen value of A



prove that k is eigen value of A^t too

and has the same eigen vectors

??



eigen vectors are the solution space P(A)

is found by solving (A-kI)x=0

dim P(A)=dim n -dim (ro(a))

rho(a)=rho(a^t)

|A|=|A^t|



these are the laws i maneged to come up with to solve it



??
 
Last edited:
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  • #2
We know that the eigenvalues of a matrix A are all the scalars k that satisfy the characteristic equation det( A - k In ) = 0.

We also know that det( A ) = det( A^t ) for any square matrix A.

Can we use these facts to complete a proof?
 
  • #3
ok i prooved that the eigen values are the same
hw to prove that the eigen vectors are the same to for every eigen value
 

Related to Are Eigen Vectors the Same for Eigen Values of Transpose A and A?

What are eigenvalues of the transpose and why are they important?

The eigenvalues of the transpose are the same as the eigenvalues of the original matrix. They are important because they provide insights into the properties of the matrix, such as its rank and determinant, and can be used to solve systems of linear equations.

How do I calculate the eigenvalues of a transpose?

To calculate the eigenvalues of a transpose, you can use the same methods as for calculating eigenvalues of the original matrix. This includes finding the characteristic polynomial and solving for its roots, or using matrix decomposition techniques such as diagonalization or triangularization.

What is the relationship between the eigenvalues of a matrix and its transpose?

The eigenvalues of a matrix and its transpose are the same. This is because the transpose simply switches the rows and columns of a matrix, but does not change its underlying properties or eigenvalues.

Can the eigenvalues of a transpose be negative?

Yes, the eigenvalues of a transpose can be negative. This is because the sign of an eigenvalue depends on the sign of its corresponding eigenvector, and the transpose does not change the sign of the eigenvectors.

How can the eigenvalues of a transpose be used in data analysis?

The eigenvalues of a transpose can be used in data analysis to find patterns and relationships between variables. They can also be used in dimensionality reduction techniques, such as principal component analysis, to identify the most important features of a dataset.

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