Another reminder on finding eigenvectors

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In summary, another question with respect to finding eigenvectors. If I remember correctly, I should be able to look at certain 2 by 2 matrices and practically write down the eigenvalues and eigenvectors. For example, I have a diagonal matrix, I know immediately what the eigenvalues and eigenvectors are. E.g. M = \begin{bmatrix} 1 & 0\\[0.3em] 0 & x\end{bmatrix} would have λ_1=1 and λ_2=-x. Well, I know immediately λ_1=1, λ_2=x and that the eigenvectors are e_1
  • #1
eherrtelle59
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Another question with respect to finding eigenvectors.

If I remember correctly, I should be able to look at certain 2 by 2 matrices and practically write down the eigenvalues and eigenvectors.

For example, I have a diagonal matrix, I know immediately what the eigenvalues and eigenvectors are.

E.g. M = \begin{bmatrix}
1 &0 \\[0.3em]
0 & x \\[0.3em]

\end{bmatrix}

Well, I know immediately λ_1 =1, λ_2 = x and that the eigenvectors are e_1 = (1 0) and e_2 = (0 1).

Now, what about an upper diagonal matrix?

Take M = \begin{bmatrix}
-1 & -1 \\[0.3em]
0 & x-(1/4) \\[0.3em]

\end{bmatrix}

I can see λ_1=1 and that e_1 = (1 0)

How do you find the second eigenvalue and eigenvector?
 
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  • #2
In a "triangular matrix", just as in a diagonal matrix, the numbers on the diagonal are the eigenvalues. In your example they are -1, NOT 1, and x- (1/4).

More generally, if your matrix is
[tex]\begin{bmatrix} a & 0 \\ -1 & b\end{bmatrix}[/tex]
then a and b are the eigenvalues.

The eigenvector corresponding to eigenvalue a must satisfy
[tex]\begin{bmatrix} a & -1 \\ 0 & b\end{bmatrix}\begin{bmatrix}x \\ y\end{bmatrix}= \begin{bmatrix}ax & ay\end{bmatrix}[/tex]
so that we have equations ax- y= ax and by= ay. The first equation, on subtracting ax from both sides, gives -y= 0 so y= 0. The second equation, which is equivalent to (b- a)y= 0 also gives y= 0. An eigenvector corresponding to eigenvalue a is <1, 0> as you say.

The eigenvector corresponding to eigenvalue b must satisfy
[tex]\begin{bmatrix} a & 1 \\ 0 & b\end{bmatrix}\begin{bmatrix}x \\ y\end{bmatrix}= \begin{bmatrix}bx \\ by\end{bmatrix}[/tex]
which gives the equations ax- y= bx and by= by. The first equation is the same as y= (b- a)x while the second equation is satisfied by any y. Any eigenvector is of the form <x, y>= <x, (b-a)x>= x<1, b-a>.

In your case, with a= 1 and b= x- 1/4, 1 is an eigenvalue with corresponding eigenvector <1, 0> (or any multiple) and x- 1/4 is an eigenvalue with corresponding eigenvector <1, x- 5/4>.
 
  • #3
This makes sense, but according to what I have here, the eigenvector should be
λ_2 = <-1 x+ (3/4)>

This is assuming x-(1/4) > 0. Would that make a difference or is what I have a typo?
 
  • #4
i.e. b-a is 1-(-1/4)=3/4, right?
 
  • #5
No, 1- (-1/4)= 1+ 1/4 = 5/4, not 3/4.
 
  • #6
Typo on my part.

we get for the "b value" an equation -v_1-v_2 = (x-1/4)*v_1

This is -v_2 =(x+3/4)*v_1
 
  • #7
That is, using your equation ax- y= bx

a=-1, b=(x-1/4)
 
  • #8
perhaps you should credit the author for the quote at the bottom of your post -- Edna St. Vincent Millay
 

FAQ: Another reminder on finding eigenvectors

1. What is an eigenvector?

An eigenvector is a vector that does not change its direction when multiplied by a matrix. It only gets scaled by a constant factor, known as the eigenvalue. Eigenvectors are used in various fields of science, such as physics, engineering, and data analysis.

2. Why are eigenvectors important in matrix operations?

Eigenvectors are important because they help us understand the behavior and properties of a matrix. They are used to find the eigenvalues of a matrix, which can provide information about the matrix's stability, convergence, and other characteristics.

3. How do you find eigenvectors?

To find eigenvectors, we need to solve the characteristic equation of a matrix, which is det(A-λI) = 0. This will give us the eigenvalues of the matrix. Then, we can plug in each eigenvalue into the equation (A-λI)x=0 and solve for x to get the corresponding eigenvector.

4. What is the significance of finding eigenvectors?

Finding eigenvectors can help us understand the underlying structure of a matrix, which can be useful in a variety of applications. For example, in data analysis, eigenvectors can be used to reduce the dimensionality of a dataset, making it easier to visualize and analyze.

5. Can all matrices have eigenvectors?

No, not all matrices have eigenvectors. A matrix must be square (same number of rows and columns) and have distinct eigenvalues in order to have eigenvectors. If a matrix does not meet these criteria, it may not have eigenvectors or may have complex eigenvectors.

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