Eigenvectors and Eigenvalues: Finding Solutions for a Matrix

In summary, the conversation discusses the process of finding eigenvalues and associated eigenvectors of a matrix. The speaker mentions a theorem from their lecture notes about invertible matrices and the zero vector, but it is not relevant to the topic of eigenvalues and eigenvectors. The question of how 1, 2, and 3 can be eigenvalues is raised, but the speaker admits to not fully understanding what eigenvalues and eigenvectors are.
  • #1
DiamondV
103
0

Homework Statement


Find the eigenvalues and associated eigenvector of the following matrix:
32e0fe9a83.png


Homework Equations

The Attempt at a Solution


302cba53a4.jpg


We have a theorem in our lectures notes that states that if a matrix is invertible the only eigenvector in its kernel will be the zero vector. In order to find out if it is invertible we get the det(A) and see if its equal to 0 or not, if it is equal to 0(you can't divide by 0) then there is no inverse, if it is not equal to 0(like in this case I got 6) then it is invertible and the only vector is the zero vector in the kernel. So technically I should stop my calculations at this point and say the zero vector is the only one.
However in the solutions given to use they have an answer that is not a 0 vector.
3b0554f97a.png

1,2,3 are eigenvalues.
How is this possible?
 
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  • #2
DiamondV said:

Homework Statement


Find the eigenvalues and associated eigenvector of the following matrix:
32e0fe9a83.png


Homework Equations

The Attempt at a Solution


302cba53a4.jpg


We have a theorem in our lectures notes that states that if a matrix is invertible the only eigenvector in its kernel will be the zero vector. In order to find out if it is invertible we get the det(A) and see if its equal to 0 or not, if it is equal to 0(you can't divide by 0) then there is no inverse, if it is not equal to 0(like in this case I got 6) then it is invertible and the only vector is the zero vector in the kernel. So technically I should stop my calculations at this point and say the zero vector is the only one.
However in the solutions given to use they have an answer that is not a 0 vector.
3b0554f97a.png

1,2,3 are eigenvalues.
How is this possible?

There is no such theorem as the one you state from your lectures. The zero vector is not considered as an eigenvector at all.

There is a theorem stating that if a matrix is invertible (has non-zero determinant) then the only vector (NOT EIGENVECTOR!) in the kernel is the zero vector. That has nothing at all to do with eigenvalues and eigenvectors.

Do you actually know what eigenvalues and eigenvectors are?
 
  • #3
Ray Vickson said:
There is no such theorem as the one you state from your lectures. The zero vector is not considered as an eigenvector at all.

There is a theorem stating that if a matrix is invertible (has non-zero determinant) then the only vector (NOT EIGENVECTOR!) in the kernel is the zero vector. That has nothing at all to do with eigenvalues and eigenvectors.

Do you actually know what eigenvalues and eigenvectors are?

Oh. Not really. Our lecture notes haven't shown any graphs with vectors on them or any sort of visualisation for this. I just know that there's these things called eigenvectors and eigenvalues that are really useful for some reason.
 

1. What are eigenvectors and eigenvalues?

Eigenvectors and eigenvalues are mathematical concepts used to analyze linear transformations. An eigenvector is a vector that does not change direction during a linear transformation, only its magnitude may change. An eigenvalue is a scalar value that represents the amount by which the eigenvector is scaled during the transformation.

2. What is the significance of eigenvectors and eigenvalues?

Eigenvectors and eigenvalues are useful in many areas of mathematics and science, especially in the fields of linear algebra and quantum mechanics. They provide a way to simplify complex systems and understand their behavior. In practical applications, they can be used for data compression, image processing, and machine learning algorithms.

3. How are eigenvectors and eigenvalues calculated?

The calculation of eigenvectors and eigenvalues involves solving the characteristic equation of a square matrix. The eigenvectors are the solutions to this equation, and the eigenvalues are the corresponding scalars. There are various methods for calculating eigenvectors and eigenvalues, such as the power method and the QR algorithm.

4. Can a matrix have more than one eigenvector and eigenvalue?

Yes, it is possible for a matrix to have multiple eigenvectors and eigenvalues. The number of eigenvectors and eigenvalues a matrix has is equal to its dimension. However, not all matrices have distinct eigenvectors and eigenvalues. In some cases, there may be repeated eigenvalues or a matrix may have only one eigenvector.

5. What is the relationship between eigenvectors and eigenvalues?

The eigenvectors and eigenvalues of a matrix are closely related. Each eigenvector corresponds to a specific eigenvalue, and the eigenvectors form a basis for the vector space in which the matrix operates. The eigenvalues determine the scaling factor for each eigenvector. Additionally, the sum of the eigenvalues is equal to the trace of the matrix, and the product of the eigenvalues is equal to the determinant of the matrix.

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