Little question about eigenvalues

In summary: This can happen if A is a diagonal matrix and one of the diagonal entries is zero. In that case, the determinant is not zero, but the inverse of A is still not invertible.
  • #1
Kubilay Yazoglu
8
0
Hey there, I'm thinking about if one of the eigenvalues is zero (means determinant is 0. right?) So, is there any possibility to non-zero eigenvalue also exists?
 
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  • #2
for a diagonal matrix, the eigenvalues are the numbers on the diagonal. so what do you think can happen?
 
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Likes Ahmed Abdullah
  • #3
Well, now that I'm more inside of these things, I realized that I've asked a stupid question :D Of course there is.
 
  • #4
another point of view is that eigenvalues are roots of the characteristic polynomial. so if one root is zero can other roots be non zero?
 
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Likes Ahmed Abdullah
  • #5
mathwonk said:
another point of view is that eigenvalues are roots of the characteristic polynomial. so if one root is zero can other roots be non zero?
Yes
 
  • #6
Well, you could have something like:
## \lambda^3 + 18\lambda^2 + 81\lambda##
Where you have eigenvalues ##\lambda_1 = 0## and ##\lambda_2 = -9##.
So, I certainly think you can have an eigenvalue of zero and other nonzero eigenvalues. And also, I'm not sure what you mean by the determinant being zero; to get a characteristic polynomial, you have to take the determinant and set it equal to zero to solve for the eigenvalues in the first place.
In other words, you find eigenvalues using:
##\det(A - \lambda I_A) = 0## (Given a matrix A and its identity matrix, IA).
 
  • #7
*EDIT*
I misspoke above; I listed two eigenvalues, whereas a ##3 \times 3## matrix (with a cubic characteristic polynomial) would have three eigenvalues. Of course, in the case I listed above, two of the eigenvalues, ##\lambda_2## and ##\lambda_3## would be the same (λ2 = λ3 = -9). I was treating them as roots of a cubic, where (with three solutions) there are only two roots, given two of them are the same. In the context of matrix algebra, though, all eigenvalues should be accounted for.
 
  • #8
Well, if there is an eigenvalue ## \lambda=0 ##, then Det(A-0I_A)=Det(A)=0.
 
  • #9
Why is that a problem? Having zero determinant just means that it's not invertible.
 

1. What are eigenvalues?

Eigenvalues are a concept in linear algebra that represent the scale factor by which a vector is stretched or compressed when multiplied by a matrix. They are also known as characteristic values or latent roots.

2. What is the significance of eigenvalues?

Eigenvalues are useful in many areas of mathematics and science, including quantum mechanics, signal processing, and data analysis. They provide important information about the behavior and properties of a linear transformation.

3. How are eigenvalues calculated?

The calculation of eigenvalues involves finding the roots of the characteristic polynomial of a matrix, which is obtained by subtracting a scalar variable from the main diagonal of the matrix and taking its determinant. This results in an algebraic equation, the solutions of which are the eigenvalues.

4. What is the relationship between eigenvectors and eigenvalues?

Eigenvectors are the corresponding vectors to eigenvalues and represent the direction of the stretching or compression. They are also known as characteristic vectors. The eigenvalue and eigenvector of a matrix are related by the equation Ax = λx, where A is the matrix, λ is the eigenvalue, and x is the eigenvector.

5. How are eigenvalues used in data analysis?

Eigenvalues are used in data analysis to reduce the dimensionality of a dataset and identify the most important features or patterns. This is done by calculating the eigenvalues and eigenvectors of the covariance matrix of the data and selecting the top eigenvectors based on their corresponding eigenvalues.

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