What Properties of a Matrix Can Eigenvalues and Eigenvectors Reveal?

AI Thread Summary
Eigenvalues and eigenvectors are critical for evaluating various properties of matrices, including determining the determinant, assessing invertibility, and diagonalizing matrices for power calculations. The trace of a matrix, which is the sum of its eigenvalues, can also be derived from these values. It is possible to reconstruct the original matrix using its eigenvalues and eigenvectors by forming a diagonal matrix with the eigenvalues and a matrix of the eigenvectors. The relationship can be expressed as M = VDV^T, where V is the matrix of eigenvectors and D is the diagonal matrix of eigenvalues. This reconstruction allows for the determination of all properties of the original matrix, provided the eigenvectors are orthonormal. However, there are limitations to what eigenvalues and eigenvectors can reveal about a matrix, suggesting that not all matrix characteristics can be inferred solely from these components.
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We are aware that by knowing the eigenvalues and eigenvectors we can evaluate the determinant, say if it is invertible and diagonalize to find powers of matrices.
Is there a list of properites of a matrix we can find by eigenvalues and eigenvectors?
Are there things that e.values and e.vectors cannot tell us about the matrix?
 
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You can also find the trace, which is the sum of all Eigenvalues.

I believe that you can construct the entire matrix from the Eigenvalues and Eigenvectors, but I can't remember the exact formula off the top of my head. You construct a diagonal matrix with the Eigenvectors on the diagonal, and a matrix composed of all the Eigenvectors.

It goes something like this:

Say v_i is the Eigenvector with Eigenvalue \lambda_i

M \cdot v_i = \lambda_i v_i,

Define the diagonal matrix with the Eigenvalues

D_{ij} = \delta_{ij} \lambda_i

and a matrix composed of all the Eigenvectors

V_{ij} = (v_i)_j

Then you should get
(V \cdot D \cdot V^T) \cdot v_i = \lambda_i v_i

We have therefore reconstructed the original matrix
M = V \cdot D \cdot V^T

(Somebody please check, I'm making this up as we go along)

Since you can construct the original matrix from the Eigenvectors and Eigenvalues, you can determine each and every property of the original matrix.
 
Eigenvectors have to be orthonormal for this to work, btw.
 
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