Which Eigenvector is Wrong for Finding Eigenvalues of a Matrix?

In summary, the conversation is about finding eigenvalues and eigenvectors of a given matrix. The participant initially made a typo in the matrix and incorrectly concluded the eigenvalues to be -1,1,1. After realizing the mistake, they found the correct eigenvalues to be -1,1,1 and also discovered that there can be multiple eigenvectors corresponding to the same eigenvalue, forming a subspace that includes linear combinations.
  • #1
:Buddy:
3
0
Too many Eigenvectors!?

Homework Statement


I have to find the eigenvalues and eigenvectors of:

-1 2 -2
1 2 1
-1 -1 0

and I can find four eigenvectors I'm not sure how to tell which of my eigenvectors is
wrong as they all seem to satisfy Av=λv
(i also checked that they arent simply multiples of each other)

The Attempt at a Solution


i used
det(A-λI)=0
to get λ=-1,1,1

then i used the definition Av=λv

to get the eigenvectors
[0,1,-1], [1,-1,0], [1,0,-1], [1,-2,1]

im not sure which of these is wrong and why
 
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  • #2


The eigenvalues of the matrix you showed are not -1,1,1. How did you conclude that? Is there a typo?
 
  • #3


yes it was a typo sorry
the matrix was meant to be

-1 -2 -2
1 2 1
-1 -1 0
 
  • #4


:Buddy: said:
yes it was a typo sorry
the matrix was meant to be

-1 -2 -2
1 2 1
-1 -1 0

Writing v1=[1,-1,0] and v2=[0,1,-1], v1 and v2 are both independent eigenvectors corresponding to the eigenvalue 1. And you are allowed to have two of those since the eigenvalue 1 has multiplicity 2. The other two vectors you wrote are v1+v2 and v1-v2. There are lots more eigenvectors corresponding the eigenvalue 1 as well, any linear combination of v1 and v2 will do. What you are missing is the eigenvector corresponding to the eigenvalue -1.
 
Last edited:
  • #5


thanks! I hadn't realized that linear combinations would be solutions. I now have a correct ( I think) set of vectors

v1=[-1 1 0]
v2=[-1 0 1]
v3=[2 -1 1]

:)
 
  • #6


One of the very first things you should have learned about eigenvectors is there is NOT a single unique eigenvector corresponding to a given eigenvalue. In fact, the set of all eigenvectors corresponding to a given eigenvalue form a subspace which necessarily contains linear combinations.
 

Related to Which Eigenvector is Wrong for Finding Eigenvalues of a Matrix?

1. What are Eigenvectors?

Eigenvectors are a type of vector that remains in the same direction after a linear transformation is applied to it. They are often used in linear algebra to study the behavior of linear transformations.

2. How many Eigenvectors can a matrix have?

The number of Eigenvectors that a matrix can have is equal to its dimension. So, a 2x2 matrix can have a maximum of 2 Eigenvectors, a 3x3 matrix can have a maximum of 3 Eigenvectors, and so on.

3. What is the significance of Eigenvectors?

Eigenvectors are important because they help us understand the behavior of linear transformations. They also have many applications in fields such as physics, engineering, and computer science.

4. Can a matrix have too many Eigenvectors?

Yes, a matrix can have too many Eigenvectors. This happens when the matrix is not diagonalizable, meaning it cannot be written as a diagonal matrix using its Eigenvectors. In this case, the matrix will have repeated Eigenvectors, which can make it difficult to analyze and solve problems involving the matrix.

5. How do you determine the number of Eigenvectors a matrix has?

The number of Eigenvectors a matrix has is equal to its dimension. To determine the Eigenvectors of a matrix, you can use the characteristic equation and solve for the Eigenvalues. The number of distinct Eigenvalues will give you the number of unique Eigenvectors the matrix has.

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