Diagonalization of Eigenvalues: A Mistake in Homework Answer?

In summary, the conversation is about a student who believes their teacher made a mistake in their homework answer and wants to verify it. The student's answer has two eigenvalues of 4 and 2, with two corresponding eigenvectors of [-1 1 0]T and [0 0 1]T. However, the teacher only has one eigenvector of [-1 1 0]T, leading them to believe that the matrix A is not diagonalizable. The student asks for help in verifying their steps for finding the eigenvectors and the conversation continues with a discussion about the correctness of the student's approach.
  • #1
hpayandah
18
0

Homework Statement



I think my teacher made a mistake in his homework answer. I need to verify this for practice. The answer I got is below. The answer the teacher has is in the pdf.

Homework Equations



Please refer to attached pdf

The Attempt at a Solution



So there is two eigenvalues= 4 and 2
but the eigenvalue 2 has 2 eigenvectors [-1 1 0]T and [0 0 1]T but my teacher has only one [-1 1 0]T. That's why he says A is not diagonalizable. Do you think it's correct?
 

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  • #2


I get the same result as your teacher for [tex] \lambda = 2 [/tex]

[tex] A= \begin{bmatrix}
3 & 1 & 0\\
0 & 2 & 1\\
1 & 1 & 3
\end{bmatrix}
[/tex]

so for lamda = 2,
[tex] (A-2I)\vec{v}=\vec{0} [/tex]
[tex] \begin{bmatrix}
1 & 1 & 0\\
0 & 0 & 1\\
1 & 1 & 1
\end{bmatrix}
\begin{bmatrix}
v_1\\
v_2\\
v_3
\end{bmatrix}
= \begin{bmatrix}
0\\
0\\
0\\
\end{bmatrix}
[/tex]
I've personally always found it easier to not do row operations here and just jump straight in. From that you get
[tex] v_3 = 0 [/tex] and [tex] v_1 = -v_2 [/tex]
so therefore [tex] \vec{v} = \begin{bmatrix}
1\\
-1\\
0
\end{bmatrix}
[/tex]

Since it is a 3x3 matrix, it needs 3 eigenvectors to be diagonalizable.

Hope this helps.
 
  • #3


Hi, thanks for replying. Attached is how I got my vectors, do you think my steps are correct.
 

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  • #4


No. Why do you think (0, 0, 1) is an eigenvector? You seem to just pull that out of thin air.
 
  • #5


You seem to have made a mistake in the step
[tex] (A-2I)\vec{v}=\vec{0} [/tex]
Why is your second row 1 0 1 instead of 0 0 1 ?
Have you said [tex] v_2= \delta [/tex] ? I'm not sure if I have read that correctly. If it is a delta, you can't say that unless the whole row equals zero. i.e.
[tex] \begin{bmatrix}
0 & 0 & 0
\end{bmatrix}
\begin{bmatrix}
v_1
\end{bmatrix}
= \begin{bmatrix}
0
\end{bmatrix}
[/tex]
 

1. What is diagonalization of eigenvalues?

Diagonalization of eigenvalues is a process in linear algebra where a square matrix is transformed into a diagonal matrix through a change of basis. This process is used to simplify calculations and solve for unknown variables in a system of linear equations.

2. How is diagonalization of eigenvalues related to eigenvectors and eigenvalues?

Diagonalization of eigenvalues is only possible for matrices that have a full set of linearly independent eigenvectors. The diagonal elements of the resulting matrix are the eigenvalues of the original matrix, and the corresponding eigenvectors form the basis for the new coordinate system.

3. Why is diagonalization of eigenvalues important?

Diagonalization of eigenvalues is important because it allows for easier computation of powers and inverses of matrices, as well as solving systems of linear equations. It also helps to identify the dominant behavior of a system and understand the dynamics of a system better.

4. What is the process of diagonalization of eigenvalues?

The process of diagonalization of eigenvalues involves finding the eigenvectors and eigenvalues of a square matrix, and then using those eigenvectors to create a new matrix with the eigenvalues along the diagonal. This new matrix is then used to transform the original matrix into a diagonal matrix through a change of basis.

5. Can all matrices be diagonalized?

No, not all matrices can be diagonalized. A matrix can only be diagonalized if it has a full set of linearly independent eigenvectors. If a matrix does not have a full set of eigenvectors, it is called defective and cannot be diagonalized.

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