Extremely confused on finding eigenvectors

In summary, to find eigenvectors for a given matrix A, we first find the eigenvalues by setting the determinant of A-cI equal to zero and solving the resulting equation. Then, we use each eigenvalue to find the kernel vectors of A-cI. It is important to note that symmetric matrices will always have 3 independent eigenvectors, even if some eigenvalues are repeated. The eigenvectors can be found by solving the equation Ax = λx, and they form a subspace of the domain.
  • #1
Jorge Cantu
1
0
Extremely confused on finding eigenvectors? Below I have a picture that gives the matrice and the eigenvectors. How did the solution find these eigenvectors??
i.e. the eigenvalues are 7 and -2
2liii68.png

2hxuk4y.png

IMAGE LINKS
http://tinypic.com/r/2liii68/9
http://tinypic.com/view.php?pic=2liii68&s=9#.VkY_YfmrSUk
 
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  • #2
an eigenvalue for a matrix A is a number c such that the matrix A-cI is singular. So to find them we set the determinant of that matrix equal to zero and solve the resulting cubic equation if we can. Then afterwards, we have the numbers c that work, and using each one in its turn we actually find the kernel vectors of the matrices A-cI. Remark: Since the matrix is symmetric about the main diagonal you are guranteed to have 3 independent eigenvectors, even if some of the eigenvalues are repeated. So you have to know how to take determinants, and then you have to know how to solve a homogeneous system.
 
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  • #3
You can also see it in the equivalent sense that , for a matrix/operator A, an eigenvalue ##\lambda## is the set of solutions for ##\lambda ## to the equation :

## Ax= \lambda x ## (There may be no Real solutions or solutions over fields that are not algebraically closed. For Reals, this may be the case for square matrices of even dimension).

You can then expand , like Mathwonk said, the polinomyal ## Det(A- \lambda I )x =0 ## using , e.g., cofactor expansion and then find the roots , if any (when the base field is not algebraically closed.)

Once you find the eigenvalues, the eigenvectors are a basis for the nullspace of the above equation. Note that the collection of eigenvectors forms a subspace of the domain.
 

FAQ: Extremely confused on finding eigenvectors

What are eigenvectors and why are they important?

Eigenvectors are special vectors that do not change direction when multiplied by a transformation matrix. In other words, they are the "directions" that are preserved by the transformation. Eigenvectors are important because they help us understand how a matrix affects a vector and are used in many applications such as data analysis, image processing, and quantum mechanics.

How do you find eigenvectors?

To find eigenvectors, we first need to calculate the eigenvalues of the matrix. We can do this by solving the characteristic equation det(A-λI)=0, where A is the matrix and λ is the eigenvalue. Once we have the eigenvalues, we can plug them back into the equation (A-λI)x=0 to find the eigenvectors. This will give us a set of linearly independent eigenvectors that correspond to each eigenvalue.

Can a matrix have more than one eigenvector?

Yes, a matrix can have multiple eigenvectors. In fact, most matrices have multiple eigenvectors for each eigenvalue. This means that there are different "directions" that are preserved by the matrix for each eigenvalue.

How do eigenvectors relate to eigenvalues?

Eigenvectors and eigenvalues are closely related. Eigenvectors are the "directions" that are preserved by a transformation matrix, while eigenvalues are the scaling factors for these vectors. In other words, the eigenvalue tells us how much the eigenvector is stretched or shrunk by the transformation.

Can eigenvectors be complex numbers?

Yes, eigenvectors can be complex numbers. In fact, for some matrices, the eigenvectors are only complex numbers. This is because the eigenvalues and eigenvectors are solutions to the characteristic equation, which can have complex roots. However, in most applications, eigenvectors are represented as real numbers for easier interpretation.

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