Understanding Eigenvectors and Eigenvalues in Linear Algebra

In summary, the conversation is about finding an eigenvector for a given matrix and discussing the properties of eigenvectors. The final step results in two different eigenvectors, but they are both valid as any scalar multiple of an eigenvector is also an eigenvector. The conversation concludes with a clarification that both eigenvectors correspond to the eigenvalue of 0.
  • #1
TheSpaceGuy
25
0
I am trying to get an eigenvector for the following matrix, I am up to the final step.
4 1
0 0

I got it to be
-1
4

is this the same as
1
-4



sorry I am pretty new to linear algebra.
 
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  • #2
TheSpaceGuy said:
I am trying to get an eigenvector for the following matrix, I am up to the final step.
4 1
0 0

I got it to be
-1
4

is this the same as
1
-4
sorry I am pretty new to linear algebra.

Of course, they aren't the same vector. But if x is an eigenvector then c*x is also an eigenvector for any constant c. In your example the c is (-1). Both of those are fine eigenvectors.
 
  • #3
As Dick said, any scalar multiple of an eigenvector is an eigenvector- in fact, any linear combination of eigenvectors is an eigenvector.

Of course, a good way to check if any vector is any eigenvector is to use the definition of "eigenvector". If v is an eigenvector of A, corresponding to eigenvalue [itex]\lambda[/itex], then [itex]Av= \lambda v[/itex].

Here,
[tex]\begin{bmatrix}4 & 1 \\ 0 & 0\end{bmatrix}\begin{bmatrix}-1 \\ 4\end{bmatrix}= \begin{bmatrix}-4+ 4 \\ 0\end{bmatrix}= \begin{bmatrix}0 \\ 0\end{bmatrix}= 0\begin{bmatrix}-1 \\ 4\end{bmatrix}[/tex]
and
[tex]\begin{bmatrix}4 & 1 \\ 0 & 0\end{bmatrix}\begin{bmatrix} 1 \\ -4\end{bmatrix}= \begin{bmatrix}4- 4 \\ \end{bmatrix}= \begin{bmatrix}0 \\ 0 \end{bmatrix}= 0\begin{bmatrix}1 \\ -4\end{bmatrix}[/tex]

So these are both eigenvectors corresponding to eigenvalue 0.
 
Last edited by a moderator:
  • #4
Thanks to the both of you. That really clears things up for me!
 

1. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are mathematical concepts that are used to represent the behavior of a linear transformation or a square matrix. Eigenvalues are scalars that represent the amount of stretching or shrinking that occurs along a particular eigenvector when the linear transformation is applied. Eigenvectors are non-zero vectors that remain in the same direction after the linear transformation is applied.

2. Why are eigenvalues and eigenvectors important?

Eigenvalues and eigenvectors are important because they provide a way to simplify and analyze complex systems in mathematics, physics, and engineering. They are used in various applications such as image processing, quantum mechanics, and data analysis.

3. How do you find eigenvalues and eigenvectors?

To find eigenvalues and eigenvectors, you need to solve the characteristic equation of the square matrix. The characteristic equation is obtained by setting the determinant of the matrix minus a multiple of the identity matrix equal to zero. Once you find the eigenvalues, you can find the corresponding eigenvectors by solving a system of equations using the eigenvalues.

4. What is the relationship between eigenvalues and eigenvectors?

The relationship between eigenvalues and eigenvectors is that each eigenvector corresponds to a specific eigenvalue. This means that when a linear transformation is applied to an eigenvector, the resulting vector will be a multiple of the original eigenvector, with the multiple being the eigenvalue.

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

Yes, a matrix can have multiple eigenvalues and corresponding eigenvectors. In fact, the number of distinct eigenvalues for a matrix is equal to its dimension. This means that a 3x3 matrix can have up to 3 distinct eigenvalues and eigenvectors.

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