Eigenvalues and eigenvectors [Linear Algebra]

In summary: Therefore, the matrix should be constructed as [T]_c=\begin{bmatrix}{1}&{1}\\{0}&{2}\end{bmatrix}. In summary, the matrix transformation of the orthogonal projection over the plane XY on R^3 has eigenvalues 0 and 1, with corresponding eigenvectors (0,0,1),(1,0,0), and (0,1,0). The matrix [T]_c should be constructed with the x and y values in the columns, giving the correct results when multiplied with the given vectors.
  • #1
Telemachus
835
30

Homework Statement


Hi there. I must give the eigenvalues and the eigenvectors for the matrix transformation of the orthogonal projection over the plane XY on [tex]R^3[/tex]

So, at first I thought it should be the eigenvalue 1, and the eigenvectors (1,0,0) and (0,1,0), because they don't change. But I also tried doing the calculus, and then I've confused.

I have:

[tex][T]_c=\begin{bmatrix}{1}&{0}&{0}\\{0}&{1}&{0}\\{0}&{0}&{0}\end{bmatrix}[/tex]

From the characteristic polynomial I get to: [tex]-\lambda(1-\lambda)^2[/tex]

Then, I have as eigenvalues 0, and 1 twice.

[tex]\lambda_1=0[/tex]:

I get to: [tex]\begin{Bmatrix}x=0\\y=0\end{matrix}[/tex]
and then the eigenvector: [tex]{(0,0,1)}[/tex]

So I thought, shouldn't it be zero? because of the projection. I have doubts with this, but I know that as it is a symmetrical matrix it should be diagonalizable, and then I should get a basis from the eigenvectors, which I wouldn't find with just the first reasoning, and then I need a linear independent vector, like this one, respect to the first I gave.

And then for [tex]\lambda_2,\lambda_3=1[/tex]:

z=0,

Which gives: [tex]{(x,y,0)}[/tex], and implies: [tex]{(1,0,0),(0,1,0)}[/tex], I think that have sense.

Well, I need some help with this. Can anybody tell me if this is right, and if it isn't, what I did wrong?

Thank you!

Bye there.

PS: I have my final exam tomorrow :P
 
Last edited:
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  • #2
(0, 0, 1) is indeed an eigenvector with eigenvalue 0. Remember, the definition of eigenvector doesn't require that the vector remain unchanged, but rather that it be sent to a scalar multiple of the original vector. Observe that 0(0, 0, 1) = (0, 0, 0) = T(0, 0, 1).

Also, you mention that you know that your matrix is diagonalizable because it is symmetric. This is true, but I would point out the much more obvious fact that it's diagonalizable on account of already being a diagonal matrix. :tongue:
 
  • #3
Thanks!
 
  • #4
I have another doubt about this. Now its about the construction of a transformation matrix. The thing is I thought I was constructed in one way, and it seems to be that I was wrong.

I have this problem, which says: From a linear transformation [tex]T:\mathbb{R}^2\longrightarrow{\mathbb{R}^2}[/tex] its known that [tex]\vec{v}=(1,1)[/tex] its an eigenvector with the eigenvalue associated [tex]\lambda=2[/tex] and that [tex]T(0,1)=(1,2)[/tex].

Find [tex][T]_c[/tex]

I thought of [tex][T]_c[/tex] as [tex]T(x,y)=(x+y,2y)[/tex]

The thing is that when I constructed the matrix I did it this way:

[tex][T]_c=\begin{bmatrix}{1}&{0}\\{1}&{2}\end{bmatrix}[/tex]

But when I make the multiplication with the vectors that the problem data gives, I don't get the right results, but when I changed to [tex][T]_c=\begin{bmatrix}{1}&{1}\\{0}&{2}\end{bmatrix}[/tex] it seems to be the matrix I'm looking for.

So, it seems to be that x, and y go in the columns, and not in the rows as I thought. I was wrong then?
 
  • #5
Just like in a vector, the x and y go in the columns and not the rows.
 

1. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are concepts in linear algebra that are used to analyze and understand linear transformations. Eigenvalues represent the scale factor by which an eigenvector is stretched or compressed when it is multiplied by a transformation matrix.

2. How are eigenvalues and eigenvectors calculated?

Eigenvalues and eigenvectors can be calculated by finding the roots of the characteristic polynomial of a matrix. Alternatively, they can also be found by using algorithms such as the power method or QR algorithm.

3. What is the importance of eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are important because they allow us to understand how a matrix transforms a vector. They also have many practical applications, such as in physics, engineering, and data analysis.

4. Can a matrix have complex eigenvalues and eigenvectors?

Yes, a matrix can have complex eigenvalues and eigenvectors. This usually occurs when the matrix is not symmetric. In this case, the eigenvalues and eigenvectors will be complex conjugates of each other.

5. How are eigenvalues and eigenvectors used in data analysis?

Eigenvalues and eigenvectors are often used in data analysis to reduce the dimensionality of a dataset and to find patterns in the data. They can also be used in machine learning algorithms such as principal component analysis (PCA) to extract the most important features from a dataset.

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