Basic confusion with eigenvectors

In summary, the conversation discusses finding the eigenvector for a given eigenvalue of 0 using a given matrix. The process involves solving a system of equations and setting up the characteristic quadratic equation. One participant initially made a mistake in their solution, but later corrected it. Another participant pointed out that the solution mentioned from WolframAlpha is for a different eigenvalue of 5. The conversation ends with a clarification that there is no way to get the solution (-1,2) or (1,-2) from the given equation.
  • #1
estro
241
0
Given the matrix:
[tex]\left( \begin{array}{cc}
1 & 2 \\
2 & 4 \end{array} \right) [/tex]

I'll find eigenspace for the eigenvalue of t=0, So I have to solve:

[tex]\left( \begin{array}{cc}
1 & 2 \\
2 & 4 \end{array} \right) {(x,y)}^t=0[/tex]

Then I do [tex]R_2->R_2-2R_1[/tex] and get x+2y=0 => x=-2y => get the eigenvector (-1,2).

But wolframalpha tells me the eigenvector for this eigenvalue should be (1,2).

Where is my sin?
 
Last edited:
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  • #3
I'n my first pose I did a mistake, I wrote eigenvalue but meant eigenvector.
Fixed it.
 
  • #4
estro said:
I'n my first pose I did a mistake, I wrote eigenvalue but meant eigenvector.
Fixed it.

Still, an eigenvector of a matrix A is a vector v that satisfies:
[tex]A v = \lambda v[/tex]
for some number lambda, which is the eigenvalue.

To solve it you need to set up the so called characteristic quadratic equation and solve that...
 
  • #5
Thank you for your response, but I know what is eigenvalue, eigenvector and characteristic polynomial, I left out the technical details and only showed how i calculate the eigen space for eigenvalue 0. [this matrix has 2 eigenvalues 5 and 0]
 
  • #6
estro said:
Thank you for your response, but I know what is eigenvalue, eigenvector and characteristic polynomial, I left out the technical details and only showed how i calculate the eigen space for eigenvalue 0. [this matrix has 2 eigenvalues 5 and 0]

Sorry! My bad! I guess I didn't read carefully enough. :redface:

In that case, your solution is the wrong way around: it should be for instance (2, -1).

The solution you mentioned from WolframAlpha is the eigenvector belonging to eigenvalue 5.
 
  • #7
Your sin is an incorrect solution: from x = -2y you get (-2,1) (by putting y = 1) or (2,-1) (by putting y = -1). There is no way to get (-1,2) or (1,-2).

RGV
 
  • #8
Thanks!
 

1. What are eigenvectors and why are they important in science?

Eigenvectors are special vectors that represent the directions along which a linear transformation only changes scale, but not direction. In other words, when a linear transformation is applied to an eigenvector, the resulting vector is parallel to the original eigenvector. They are important in science because they help us understand the behavior and properties of linear systems, and are used in various applications such as image processing, data compression, and quantum mechanics.

2. How are eigenvectors calculated and what do the resulting values represent?

Eigenvectors are calculated by solving a system of linear equations, where the solution is a vector that satisfies the equation Av = λv, where A is a square matrix, v is the eigenvector, and λ is the corresponding eigenvalue. The eigenvalues represent the amount by which the vector is scaled when the linear transformation is applied, and the eigenvectors represent the direction in which the scaling occurs.

3. Can there be more than one eigenvector for a given eigenvalue?

Yes, it is possible to have multiple eigenvectors for a single eigenvalue. In fact, an eigenvalue can have an infinite number of eigenvectors associated with it, as long as they are linearly independent. This means that they are not scalar multiples of each other, and they represent different directions in space.

4. How are eigenvectors used in data analysis and machine learning?

In data analysis and machine learning, eigenvectors are used to reduce the dimensionality of a dataset. This means that they help to identify the most important features or variables in a dataset, and discard the less important ones. This can help to simplify and speed up data processing, as well as improve the accuracy of predictive models.

5. Are eigenvectors and eigenvalues always real numbers?

No, eigenvectors and eigenvalues can be complex numbers as well. This is because they are calculated using complex matrices, and the resulting vectors and values represent the complex behavior of the linear transformation. However, in many applications, real-valued eigenvectors and eigenvalues are sufficient and more commonly used.

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