Finding eigenvectors of a matrix that has 2 equal eigenvalues

In summary, the matrix A has three eigenvalues: d1 = -3, d2 = 3, and d3 = 3, but it does not necessarily have three linearly independent eigenvectors. The number of eigenvectors can vary from one to the number of equal eigenvalues, and it is possible to have fewer eigenvectors than eigenvalues. Additionally, when solving for eigenvectors using the equation [A-dI]v=0, it is important to double check calculations to avoid mistakes.
  • #1
aija
15
0
Matrix A=
2 1 2
1 2 -2
2 -2 -1

It's known that it has eigenvalues d1=-3, d2=d3=3Because it has 3 eigenvalues, it should have 3 linearly independent eigenvectors, right?

I tried to solve it on paper and got only 1 linearly independent vector from d1=-3 and 1 from d2=d3=3.

The method I used was:
[A-dI]v=0
and from this equation I used Gaussian elimination to find v1, v2 and v3

Even wolfram alpha finds only 1 solution from this:
http://www.wolframalpha.com/input/?i=-x+++y+++2z+=+0,+x+-+5y+-+2z+=+0,+2x+-+2y+-+4z+=+0
^
this is the system of equations from [A-3I]v=0 (3 is the eigenvalue d2=d3)

I don't see any way to get 2 linearly independent vectors from this solution
y=0, z=x/2

all i get is vectors
t*[2 0 1]T, t is a member of ℝ

here's matrix A in wolfram alpha: http://www.wolframalpha.com/input/?i={{2,+1,+2},+{1,+2,+-2},+{2,+-2,+-1}}

It shows that there is an eigenvector v3 = [1 1 0]T, but i don't see how to get it. Obviously my way to solve this problem doesn't work, so what did I forget to do in my solution or what did I do wrong and why doesn't it work this way?

PS. I'm not sure if this should be in the homework section, because this is more like a general problem and I don't understand why doesn't it work the way i tried to solve it. Matrix A could be any matrix with two equal eigenvalues.
 
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  • #2
when a matrix has r equal eigenvalues, the number of eigenvectors (using complex numbers) can be anywhere from one to r.

e.g. a square 2by2 matrix which has a "1" in the upper right hand corner, and all other entries zero, has only one eigenvector.

a square r by r matrix which has s ones just above the diagonal and all other entries zero, should have r-s eigenvectors.
 
  • #3
mathwonk said:
when a matrix has r equal eigenvalues, the number of eigenvectors (using complex numbers) can be anywhere from one to r.

e.g. a square 2by2 matrix which has a "1" in the upper right hand corner, and all other entries zero, has only one eigenvector.

a square r by r matrix which has s ones just above the diagonal and all other entries zero, should have r-s eigenvectors.
Ok, but according to wolfram alpha this matrix still has 3 eigenvectors, and I'm wondering why can i only find the first two eigenvectors using the method i used?
 
  • #4
When I solve (A-3I)X=0, I find two linearly independent solutions, i.e. eigenvectors to the eigenvalue 3:
[1 1 0]T and [2 0 1]T.
 
  • #5
Erland said:
When I solve (A-3I)X=0, I find two linearly independent solutions, i.e. eigenvectors to the eigenvalue 3:
[1 1 0]T and [2 0 1]T.
Thanks,

I tried it again and now I get it. I just made a little mistake calculating 2-3 (not -5)

It's weird because I counted this twice (did the same mistake twice) and checked that I had counted everything totally right but didn't notice this.
 
  • #6
my point was that your question:

"Because it has 3 eigenvalues {-3,3,3}, it should have 3 linearly independent eigenvectors, right?"

has answer
"no, not right."

and misunderstanding this general principle is more harmful in the long run than adding 2 and -3 and getting -5.
 
  • #7
mathwonk said:
my point was that your question:

"Because it has 3 eigenvalues {-3,3,3}, it should have 3 linearly independent eigenvectors, right?"

has answer
"no, not right."

and misunderstanding this general principle is more harmful in the long run than adding 2 and -3 and getting -5.
yes, i understood that as well, thanks
 

1. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are mathematical concepts used to describe certain properties of a square matrix. Eigenvalues are scalar values that represent the amount by which an eigenvector is scaled when multiplied by the matrix. Eigenvectors are non-zero vectors that, when multiplied by a matrix, result in a scalar multiple of the original vector.

2. How do you find the eigenvectors of a matrix with 2 equal eigenvalues?

To find the eigenvectors of a matrix with 2 equal eigenvalues, you can use the standard method of solving for eigenvectors. This involves setting up and solving a system of linear equations, where the coefficients of the equations are based on the matrix being analyzed. In the case of 2 equal eigenvalues, the resulting equations will have infinitely many solutions, meaning there will be an infinite number of eigenvectors for that matrix.

3. What do equal eigenvalues indicate about a matrix?

Equal eigenvalues indicate that the matrix has at least one eigenvector that is a linear combination of the other eigenvectors. This means that the matrix has a repeated characteristic root, which can have implications for the behavior and properties of the matrix.

4. Can a matrix have more than 2 equal eigenvalues?

Yes, a matrix can have more than 2 equal eigenvalues. In fact, a matrix can have any number of equal eigenvalues, depending on the size and properties of the matrix. The number of equal eigenvalues also affects the number of linearly independent eigenvectors that the matrix has.

5. How are eigenvectors used in scientific applications?

Eigenvectors are commonly used in scientific applications to analyze and understand the properties and behavior of complex systems. They can be used to model and predict the behavior of physical systems, such as in fluid dynamics or quantum mechanics. Eigenvectors are also used in data analysis and machine learning, where they can help identify patterns and relationships in large datasets.

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