Eigenvectors and diagnolization

In summary, the first question asks if the reasoning is correct in stating that a 3x3 matrix with a last row of all zeroes cannot be diagonalizable because the determinant is 0 and there are no eigenvalues. The second question asks for a proof that an n x n matrix in C space with n distinct eigenvalues is diagonalizable, and the correct reasoning is that n distinct eigenvalues implies n independent eigenvectors and thus the matrix is diagonalizable.
  • #1
Hallingrad
29
0
Two questions. First, I'm given a 3x3 matrix with the last row all zeroes. I'm asked to diagonalizable it, but the determinant is 0, so there are no eigenvalues. Am I reasoning correctly here? It seems an odd question to ask.

Second, I'm asked to prove that if A n x n matrix in C space, then if A has n distinct eigenvalues, A is then diagonalizable. The way I proved it is to say that for A to be diagonalizable, it must have n distinct eigenvectors, which is only the case if it has n distinct eigenvalues. This seems far too easy, am I missing something?
 
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  • #2
If the determinant is 0 then 0 is an eigenvalue for the matrix
 
  • #3
Hallingrad said:
Two questions. First, I'm given a 3x3 matrix with the last row all zeroes. I'm asked to diagonalizable it, but the determinant is 0, so there are no eigenvalues. Am I reasoning correctly here? It seems an odd question to ask.

Second, I'm asked to prove that if A n x n matrix in C space, then if A has n distinct eigenvalues, A is then diagonalizable. The way I proved it is to say that for A to be diagonalizable, it must have n distinct eigenvectors, which is only the case if it has n distinct eigenvalues. This seems far too easy, am I missing something?
Pretty much nothing you have said is right. First of all, you are going the "wrong way". You were asked to prove that if A has n distinct eigenvalues, then it is diagonalizable.

What you are trying to prove is that "if A is diagonalizable then it has n distinct eigenvalues".

You cannot prove that because it is NOT true! Further, it is not true that a matirix having n distinct eigenvectors "is only the case if it has n distinct eigenvalues. The n by n identity matrix has n independent eigenvectors but all n of its eigenvalues is the same.


The other way is correct. Since eigenvectors corresponding to distinct eigenvalues are independent, if an n by n matrix has n distinct eigenvalues then it must have n independent ("distinct" is not enough) eigenvectors and so is diagonalizable. (Write the linear transformation corresponding in a basis consisting of those eigenvectors.)
 

1. What are eigenvectors and why are they important?

Eigenvectors are special vectors in a matrix that do not change direction when multiplied by that matrix. They are important because they help us understand how a matrix transforms space and can be used to simplify complex systems of equations.

2. How do you find eigenvectors?

To find eigenvectors, we first need to find the eigenvalues of a matrix. This can be done by solving the characteristic equation. Once we have the eigenvalues, we can use them to find the corresponding eigenvectors by solving a system of equations.

3. What is the significance of a diagonalized matrix?

A diagonalized matrix is a matrix that has been transformed into a diagonal form, where all the non-zero entries are on the main diagonal. This reduces the complexity of the matrix and makes it easier to work with, as well as revealing important information about the matrix's properties and behavior.

4. Can any matrix be diagonalized?

Not every matrix can be diagonalized. In order for a matrix to be diagonalizable, it must have a full set of linearly independent eigenvectors. This means that the matrix must have as many distinct eigenvalues as it has dimensions.

5. How is diagonalization used in real-world applications?

Diagonalization has many real-world applications, such as in physics, engineering, and computer science. It is used to solve systems of differential equations, analyze complex networks, and perform image and signal processing, among other things. It is also a key concept in quantum mechanics and is used to simplify and understand the behavior of quantum systems.

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