Solving for Eigenvalues and Determinant of a Singular Matrix

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In summary, the conversation discusses a singular 3x3 matrix with det(A-I)=0 and rank(A+3I)=2. The characteristic polynomial of A is found to be t(t-1)(t+3). It is determined that A-3I is invertible and the determinant and trace of A are 0 and -2 respectively. It is also mentioned that the matrix is singular if it has any zero eigenvalues.
  • #1
daniel_i_l
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Homework Statement


You have a singular 3x3 matrix where det(A-I) = 0 , rank(A+3I) = 2
a) find the characteristic polynomial of A.
b) Is A-3I invertible?
c) Find det(A) and tr(A)


Homework Equations





The Attempt at a Solution


a) First I'll find the eigenvalues:
- 0 is obviously one since A is singular
-det(A-I)=0 => det(I-A) = 0 and so 1 is also an eigenvalue.
-rank(A+3I)=2 => rank(-3I - A) = 2 and so det(-3I - A) = 0 and so -3 is also one.
So the polynomial is t(t-1)(t+3)

b) No because if det(A-3I)=0 => det(3I-A)=0 => 3 is an eigenvalue but we already found 3 and that's the maximum.

c) det(A) =0 , and the polynomial is t(t^2+2t -3) = t^3 +2t^2 -3t and so tr(A) = 2.

Is that right? Am I missing anything?
Thanks.
 
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  • #2
Basically fine, except that you argument in b) shows A-3I IS invertible, correct? And an alternative way to get the trace is just to sum the eigenvalues 0+1-3=-2. So since the characteristic polynomial is (t-e1)(t-e2)(t-e3) you can see that the coefficient of the t^2 is NEGATIVE the sum of the eigenvalues.
 
  • #3
For part b:

Do you know the theorem that says that if you have any zero eigenvalue then the matrix is singular? If not, does it make sense to you why this is true?
 
  • #4
Thanks everyone.
 

FAQ: Solving for Eigenvalues and Determinant of a Singular Matrix

What are Eigenvectors and Eigenvalues?

Eigenvectors and eigenvalues are mathematical concepts used to understand the behavior of linear transformations. Eigenvectors are non-zero vectors that remain in the same direction after a linear transformation, while eigenvalues are the scalar values that determine the amount of stretch or compression in that direction.

What are the applications of Eigenvectors and Eigenvalues?

Eigenvectors and eigenvalues have various applications in fields such as physics, engineering, and computer science. They are used to solve systems of differential equations, analyze the stability of dynamical systems, and perform dimensionality reduction techniques in machine learning.

How are Eigenvectors and Eigenvalues calculated?

To find the eigenvectors and eigenvalues of a matrix, we need to solve the characteristic equation det(A-λI)=0, where A is the matrix and λ is the eigenvalue. The solutions to this equation are the eigenvalues, and the corresponding eigenvectors can be found by solving the system (A-λI)x=0.

What is the relationship between Eigenvectors and Eigenvalues?

The eigenvectors and eigenvalues of a matrix are related, as the eigenvalues determine the scaling factor for the eigenvectors. Eigenvectors with larger eigenvalues are stretched more, while those with smaller eigenvalues are compressed. Additionally, eigenvectors with different eigenvalues are orthogonal to each other.

What is the significance of Eigenvectors and Eigenvalues in data analysis?

In data analysis, eigenvectors and eigenvalues are used in principal component analysis (PCA) to reduce the dimensionality of high-dimensional data. By finding the eigenvectors and eigenvalues of the data's covariance matrix, we can identify the most important directions in the data and project the data onto a lower-dimensional subspace.

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