Find Eigenvectors for σ⋅n: Solving the Equations

In summary, the eigenvector for the eigenvalue 1 is a(sinθcosφ +isinθsinφ) and the eigenvector for the eigenvalue -1 is a(sinθcosφ -isinθsinφ).
  • #1
1missing
14
0
1. n = sinθcosφ i + sinθsinφ j + cos k
σ = σx i + σy j + σz k , where σi is a Pauli spin matrix

Find the eigen vectors for the operator σ⋅n

2. Determinant of (σ⋅n - λI), where I is the identity matrix, needs to equal zero
(σ⋅n - λI)v = 0, where v is an eigen vector, and 0 is the zero vector

3.
Not entirely sure how to type out these matrices, but the math is fairly straight forward. If it's needed I can supply pictures of hand written work.

To find the eigenvalues, I took the determinant to get an equation for λ, and I found it was equal to ±1. I then tried to find the eigen vector for the eigenvalue 1. The two equations I ended up with were:

a(cosθ - 1) + b(sinθcosφ - isinθsinφ) = 0

a(sinθcosφ +isinθsinφ) - b(cosθ+1) = 0

No matter how I try to solve for a or b, I end up canceling the unknowns entirely and end up with something like 1 = 1. Is there something I'm not seeing? Not getting a meaningful answer has me questioning my approach, is there something else I could try?
 
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  • #2
1missing said:
Is there something I'm not seeing?
You are not seeing that the two equations you get are not linearly independent, cannot be linearly independent, because you set the determinant equal to zero in the first place. One normally finds the eigenvectors by getting a as a constant times b from one of the equations and then use the normalization condition. If you give the matter a little thought you will see that you can multiply the eigenvector v by an arbitrary scalar and still satisfy the eigenvalue equation. However there is only one scalar that normalizes the eigenvector.
 
  • #3
Derp. Once I read normalization the lightbulb went off in my head.

b = a(sinθcosφ +isinθsinφ) / (cos + 1)

1 - |b2| = |a2|

|a2| = (cosθ + 1)2 / ((cosθ + 1)2 + (sin2θcos2φ +sin2θsin2φ)

|a2| = (cosθ + 1)2 / (2cosθ + 2)

a = √(cosθ + 1)/2 = cos(θ/2)Thank you for the help!
 

1. What is the purpose of finding eigenvectors for σ⋅n?

The purpose of finding eigenvectors for σ⋅n is to determine the directions in which a linear transformation represented by σ⋅n will simply scale the vector without changing its direction. These eigenvectors are important in various applications, such as in physics, engineering, and computer graphics.

2. How do you solve the equations for finding eigenvectors of σ⋅n?

To solve the equations for finding eigenvectors of σ⋅n, you would first need to calculate the determinant of the matrix σ⋅n. Next, you would set up and solve a characteristic equation using the determinant. Finally, you would use the resulting eigenvalues to find the corresponding eigenvectors by solving a system of equations.

3. What is the relationship between eigenvectors and eigenvalues?

Eigenvectors and eigenvalues are closely related. An eigenvector is a vector that remains unchanged in direction when multiplied by a linear transformation represented by a matrix, while an eigenvalue is the scalar value that scales the eigenvector. In other words, eigenvectors and eigenvalues are the solutions to the equation Ax=λx, where A is the matrix, x is the eigenvector, and λ is the eigenvalue.

4. Can a matrix have multiple eigenvectors?

Yes, a matrix can have multiple eigenvectors. In fact, most matrices have multiple eigenvectors, each with its corresponding eigenvalue. The number of eigenvectors a matrix has is equal to its dimension.

5. How are eigenvectors and eigenvalues used in real-world applications?

Eigenvectors and eigenvalues have numerous applications in various fields such as physics, engineering, and computer graphics. They are used to solve systems of differential equations, determine stability in dynamical systems, and analyze the behavior of particles in quantum mechanics. They are also used in data analysis and machine learning algorithms for dimensionality reduction and feature extraction.

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