How do I find the eigenvector for a 2x2 matrix?

In summary, the conversation was about finding eigenvectors for 2x2 and 3x3 matrices. The process for finding eigenvalues for both sizes of matrices was discussed, with the final answer for the 3x3 matrix being -2, 2, and 3. There was also confusion about finding the eigenvector for a 3x3 matrix, with the conversation ending without a clear resolution.
  • #1
innightmare
35
0

Homework Statement



Find the eigenvector for each of the matrices

Homework Equations


I have a 2X2 matrice. (4, 2) which are on the top and (2,1) which are on the bottom. I understand how to get the value of the eigen, but I am confused about getting the vector.


The Attempt at a Solution



I took the value of A-I(lama). Second I did the row reduction inorder to get the top row with the pivot of one and one-half. NOW this is where I am confused. Thank in advance!
 
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  • #2
You out in one eigenvalue (one of your lambdas) in the matrix: (A-I*lambda),

Then you know that (A-I*lambda)*x=0, where x = (x_1,x_2) is the eigenvector, and 0 is the 2dim zero-vector. Now solve that system of equations.

Can you show us how you do all of this so we can help you? Write matrices and vetors as you would do in matlab, or just write them in TeX
 
  • #3
my values for the lamda's were 0 and 5
my 2x2 matrice was 4, 2 and for the bottom was 2,1

for the first lamda=0
Ill plug it into the formula, A-lamda*I which is (4 2/2 1) - (0 0 /0 0)= (4 2/2 1)
Ill do the row-reduction to get ONLY the top row of 1, 1/2.

Which Ill now multiply with x_1 and x_2.

which will turn out to be x_1= 1/2 x_2

so then my answer will turn out to be x_1= 1 and x_2= -2

Is this correct? Am I on the right path to solving for eigen vectors?? Thanks again!
 
  • #4
Also, do I solve a 3x3 matrix the same way as i do with a 2x2?
 
  • #5
Yeah I think a possible eigenvector for lamda=0 is (1, -2). Now do the same for lamda=5. Then you would have obtained the 2 eigenvectors, which are linearly independent. Note that since you have 2 linearly independent eigenvectors for 2x2 matrix, you have shown that it is diagonalizable.

For 3x3 matrix, the exact same process applies, except that we should have at least 3 linearly independent eigenvectors if the matrix is to be diagonalizable.
 
  • #6
I am having a hard time with the 3x3 matrice with finding the eigen value. I know its similar to finding a the determinant. But for some reason, I do get the right answer/values just not with the right signs. How do I know which sign to use?

I think with finding the eigen value, its a negative, then you have to take into consideration the sign of the place of the number inside the matrice. But my signs keep coming out messed up
 
  • #7
why can't you just show us the problem in its entire length and show in detail how you solve it, so we can say "hey, that's wrong".
 
  • #8
(3, 1, 1) second row: (1, 0, 2) third row: (1, 2, 0)

which i then convert into to (3-lamda) , -lamda, -lamda : all of which are diagonally.

-(3-lamda)*2x2matrice of top row: lamda, 2 second row: 2, lamda

-1*(-(2x2 matrice of top row:1, 1 second row: 2, lamda))

-1* 2x2 matrice of top row: 1, 1 second row: lamda, 2for the fiirst matrice I got: lamda-cube-3lamda^2-4lamda+12

for the second matrice I got: -lamda-2

for the third I got: -lamda-2

my three eigen values are : 1, 4, -2
alright, here goes the fun part: finding the eigen vector is not really the same as a 2x2 matricefor example for lamda =1I took the value of lamda and placed it diagonally in a matrice of all zeros. which i then subtracted from the original matrice.

gave me= top row: 2, 1, 1 second row: 1,1,2 and the third row: 1,2,1

Now I am getting confused here. because when i switch R_2 with R_1 and then do the row reduction with the other rows i am still left with the other rows which i then keep reducing to all zeros with ONLY one 1 , in the top row, left.
that is just going to give me x_1 but it'll zero out x_2 and x_3What am I doing wrong here??
 
  • #9
Sorry, for me its all a mess. And I have much to study my self for tomorrows exams. Either you write in TeX or matlab-style, or wait for someone else to help you :/
 
  • #10
It's not at all clear what you are doing. The matrix I think is
[tex]\left[\begin{array}{ccc}3 & 1 & 1 \\ 1 & 0 & 2 \\1 & 2 & 0 \end{array}\right][/tex]
and so to find the eigen values you solve the equation
[tex]\left|\begin{array}{ccc}3-\lambda & 1 & 1 \\ 1 & -\lambda & 2 \\1 & 2 & -\lambda \end{array}\right|= 0[/tex]
Expanding by the first row, that is
[tex](3- \lambda)\left|\begin{array}{cc}-\lambda & 2 \\2 & -\lambda\end{array}\right|-(-\lambda- 2)+ (2+ \lambda)[/tex]
[tex]= (3- \lambda)(\lambda^2- 4)+ \lambda+ 2+ \lambda+ 2[/tex]
Seeing that there is a [itex]\lambda+ 2[/itex] in each term, I would factor that out before I continue:
[tex]=(\lambda+ 2)((3- \lambda)(\lambda- 2)+ 2)=(\lambda+ 2)(-\lambda^2+ 5\lambda- 6)= 0[/tex]
The roots of [itex]\lambda^2- 5\lambda+ 6= 0[/itex] are 3 and 2 so the three eigenvalues are -2, 2, and 3.
 
  • #11
thats what i did. sorry but i don't have matlab, which was why i wrote it out completely. anyhow, that's what i did, but that answer in the book for the eigen value was
4, 3 -2

OK-so how do i go from here inorder to get my eigen vector? i understand how to get the eigen vector with a two by two but it gets confusing with a 3x3.

thanks
 

1. How do I find the eigen vector?

The eigen vector can be found by first finding the eigenvalues of the matrix. Then, for each eigenvalue, solve the equation (A - λI)v = 0, where A is the matrix, λ is the eigenvalue, and v is the eigen vector. This will give you a set of equations, which can be solved using various methods such as Gaussian elimination or Cramer's rule to find the eigen vector.

2. Can I find more than one eigen vector for a matrix?

Yes, a matrix can have multiple eigen vectors for a single eigenvalue. In fact, a matrix can have as many eigen vectors as its dimension. However, the eigen vectors for a given eigenvalue are not unique and can be scaled by any non-zero constant.

3. What is the significance of eigen vectors?

Eigen vectors are important in many areas of mathematics and science, including linear algebra, differential equations, and physics. They represent the directions in which a linear transformation acts simply by scaling the vector, and are used to solve various problems such as finding the principal components of a dataset or determining the stability of a system.

4. How can I check if a vector is an eigen vector?

To check if a vector is an eigen vector of a matrix, simply multiply the vector by the matrix and see if the result is a scalar multiple of the original vector. If it is, then the vector is an eigen vector. Additionally, you can also verify that the vector satisfies the equation (A - λI)v = 0 for the corresponding eigenvalue.

5. Is there a shortcut method for finding eigen vectors?

There are various methods for finding eigen vectors, such as the power method, inverse iteration method, and Jacobi method. These methods can be more efficient for certain types of matrices, but they all involve some form of iterative computation and do not provide a direct shortcut for finding eigen vectors. It is important to understand the underlying concepts and methods for finding eigen vectors in order to choose the most appropriate approach for a given matrix.

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