Embarassing question about eigenvectors

In summary, the conversation discusses how to find eigenvalues and eigenvectors for spinors in 2 dimensional complex space. The confusion arises with non-square matrices, but it is suggested to use the z component of angular momentum and Pauli matrices to find the desired values.
  • #1
Ed Quanta
297
0
Ok, so let us suppose we have a spinor which is a spin 1/2 state vector (a)
b

Now spinors exist in 2 dimensional complex space. How do I find the eigenvalues which correspond to the eigenvector
(a)
b


I am confused because we are dealing with eigenvalues for a matrix which is not a square matrix. I know for a square matrix we just find the eigenvalues such that the determinant of the matrix becomes zero. I am not sure how to deal with determinants of non-square matrices however. Helo anybody?
 
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  • #2
in spin 1/2 space, the eigen vectors are usually found using the z component of the angular momentum. Use the Pauli matricies for [tex] S_{z} [/tex] to find the eigenvalues and vectors.
 
  • #3



Hi there,

First of all, there is no need to feel embarrassed about asking this question. Understanding eigenvectors and eigenvalues can be tricky, and it is completely normal to have questions about them.

To answer your question, in order to find the eigenvalues for a spinor, we need to first convert it into a square matrix. This can be done by taking the outer product of the spinor with its conjugate transpose. For your spinor (a, b), the corresponding square matrix would be:

|a|^2 a*b
a*b |b|^2

Now, to find the eigenvalues of this matrix, we can use the same method as with a regular square matrix - set the determinant equal to zero and solve for the eigenvalues. In this case, the determinant would be:

|a|^2 * |b|^2 - |a*b|^2 = 0

Solving this equation will give you the eigenvalues for your spinor. Keep in mind that since spinors are complex numbers, the eigenvalues will also be complex numbers.

I hope this helps clarify things for you. Don't hesitate to ask for further clarification if needed.
 

1.What are eigenvectors and why are they important?

Eigenvectors are a type of vector that, when multiplied by a transformation matrix, are only scaled and not rotated. They are important because they provide a way to understand and analyze linear transformations in a simpler way, and are used in various fields such as physics, engineering, and computer science.

2. How can eigenvectors be used in data analysis?

In data analysis, eigenvectors can be used to reduce the dimensionality of a dataset and identify patterns and relationships between variables. They can also be used in machine learning algorithms, such as principal component analysis, to extract important features from a dataset.

3. Are eigenvectors and eigenvalues the same thing?

No, eigenvectors and eigenvalues are related but are not the same. Eigenvectors are the vectors that do not change direction when multiplied by a transformation matrix, while eigenvalues are the corresponding scalar values that represent the scaling factor of the eigenvectors.

4. How do you find eigenvectors?

To find eigenvectors, you need to solve the characteristic equation of a transformation matrix, which involves finding the eigenvalues. Once the eigenvalues are known, the eigenvectors can be found by solving a system of linear equations.

5. Can eigenvectors have negative values?

Yes, eigenvectors can have negative values as they represent a direction and not a magnitude. The sign of an eigenvector does not affect its properties or significance in a transformation.

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