Calculating eigenvalues and eigenvectors

In summary, the conversation discusses finding a transition matrix in the form T=UAU^-1, where U=[V1 V2]. The original transition matrix is [0.9 0.002; 0.1 0.998]. The eigenvalues are calculated to be 0.898 and 1, with corresponding eigenvectors V1=[1;-1] and V2=[0.002;0.1]. However, the question asks to use a different result [0.02 0.707;0.9998 -0.707]^-1 = [0.9823 0.9823;1.3866 -0.0278], indicating the use of different eigenv
  • #1
James2012
2
0

Homework Statement


I'm having a problem with a question. I need to find the transition matrix in the form
T=UAU^-1
where U=[V1 V2]

Homework Equations


T=UAU^-1
where U=[V1 V2]

The Attempt at a Solution



my original transition matrix is [0.9 0.002; 0.1 0.998]
from that i calculated the eigenvalues to be 0.898 and 1
which means A=[0.898 0;0 1]
i found the eigenvectors to be V1=[1;-1] and V2=[0.002;0.1]
subbing these into the equation above i end up with the original transition matrix, however the question says to make use of the result [0.02 0.707;0.9998 -0.707]^-1 = [0.9823 0.9823;1.3866 -0.0278]


which means they use different eigenvectors, but I am not sure how they got that
 
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  • #2
I also get the same eigenvals as you but the same vectos as them.
 
  • #3
Hi, How did you get to the eigenvectors?
i used the equation (A-lamdaI)v=0

then for 0.898 i get the follwing

[0.002 0.002;0.1 0.1][V1;V2]=[0;0]
therefore the eigenvector for 0.898 is [1;-1]
 

Related to Calculating eigenvalues and eigenvectors

1. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are mathematical concepts used to describe the behavior of linear transformations or matrices. Eigenvalues represent the scalar values by which an eigenvector is scaled when it is multiplied by a matrix. Eigenvectors are the non-zero vectors that are only scaled by a scalar value when multiplied by a matrix.

2. Why are eigenvalues and eigenvectors important?

Eigenvalues and eigenvectors have various applications in mathematics, physics, and engineering. They are used to solve systems of linear equations, analyze the stability of dynamic systems, and understand the behavior of quantum systems. They also have applications in computer graphics, data analysis, and image processing.

3. How do you calculate eigenvalues and eigenvectors?

To calculate eigenvalues and eigenvectors, you first need to find the characteristic polynomial of the matrix. Then, you can find the roots of the characteristic polynomial, which will give you the eigenvalues. Next, you can substitute each eigenvalue back into the original matrix to find the corresponding eigenvector. Alternatively, you can use software programs like MATLAB or Python to calculate eigenvalues and eigenvectors.

4. What is the relationship between eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are related by the equation Av = λv, where A is the matrix, v is the eigenvector, and λ is the eigenvalue. This equation shows that an eigenvector is only scaled by a scalar value when multiplied by a matrix, and that scalar value is the eigenvalue. Additionally, the eigenvectors corresponding to different eigenvalues are orthogonal to each other.

5. Can a matrix have complex eigenvalues and eigenvectors?

Yes, a matrix can have complex eigenvalues and eigenvectors. This is especially common for matrices with complex entries. In this case, the eigenvectors will also have complex entries, but the eigenvalues will always be real numbers. In fact, complex eigenvalues and eigenvectors are often used to describe the behavior of quantum systems.

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