What do i do now? Eigan vectors wee

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In summary, our professor was able to get a quadratic equation from a 3 by 3 matrix, but with different eigenvalues.
  • #1
mr_coffee
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hello everyone, I'm trying to find all the eigenvalues and eigenvectors. Then construct D and P such that A = PDP^-1;
A =
2 0 1
-1 3 -1
0 10 1
well when i took the determinant of [tex]A-\lambda = 0[/tex] I got:
[tex]\lambda^2-4\lambda+13[/tex] and got Eigenvalues of 2 +/- 6i;
but now I'm going to find the first eigenvector so i let
[tex]\lambda= 2+6i[/tex] I'm stuck on how I'm suppose to let x = a so i can get an eigenvector.
here is the rest of the work:http://img407.imageshack.us/img407/1271/lastscan0qp.jpg
thanks.
 
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  • #2
mr_coffee said:
hello everyone, I'm trying to find all the eigenvalues and eigenvectors. Then construct D and P such that A = PDP^-1;
A =
2 0 1
-1 3 -1
0 10 1
well when i took the determinant of [tex]A-\lambda = 0[/tex] I got:
[tex]\lambda^2-4\lambda+13[/tex] and got Eigenvalues of 2 +/- 6i;
but now I'm going to find the first eigenvector so i let
[tex]\lambda= 2+6i[/tex] I'm stuck on how I'm suppose to let x = a so i can get an eigenvector.
here is the rest of the work:http://img407.imageshack.us/img407/1271/lastscan0qp.jpg
thanks.

How did you get a quadratic equation out of a 3 by 3 matrix? I come out with completely differerent eigenvalues. I got, as the eigenvalue equation,
[tex]-\lambda^3+ 6\lambda^2- 21\lambda+ 16k= 0[/tex]
which has [tex]\lambda= 1[/tex] as one solution. Factoring [tex](\lambda- 1)[/tex] out leaves
[tex]-\lambda^2+ 5\lambda- 16= 0[/tex] to be solved. That has complex solutions but the imaginary part is irrational.
By the way- your TEX wasn't showing properly because you were using
"\tex" to end rather than "/tex". I fixed that.
 
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  • #3
Ahh thanks alot! Our professor couldn't figure this out, well he could, but he said he didn't want too, so he isn't making us solve it but thanks or clearing that up! I later went back and did it, its quite ugly.
 

FAQ: What do i do now? Eigan vectors wee

What are Eigen vectors?

Eigen vectors are special vectors in linear algebra that represent the directions along which a linear transformation has a simple effect, such as scaling or shearing. They are also known as characteristic vectors.

How are Eigen vectors used in science?

Eigen vectors are used in many fields of science, including physics, engineering, and computer science. They are particularly useful in data analysis and machine learning, as they can help identify the most important variables in a dataset.

How do I calculate Eigen vectors?

To calculate Eigen vectors, you will need to find the Eigen values of a square matrix, then use those values to solve for the corresponding Eigen vectors. This process can be done manually or using software such as MATLAB or Python.

What is the significance of Eigen vectors?

Eigen vectors have many applications in science, but their main significance lies in their ability to simplify complex data and systems. By identifying the Eigen vectors of a system, scientists can better understand its underlying structure and behavior.

Can Eigen vectors be negative?

Yes, Eigen vectors can have negative values. The sign of an Eigen vector is not important; what matters is its direction. Negative Eigen vectors simply indicate that the transformation is occurring in the opposite direction of the vector.

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