How Do You Normalize Eigenvectors for an Observable Matrix?

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In summary, the conversation discusses finding the normalized eigenvectors and corresponding eigenvalues of an observable represented by a matrix. The eigenvectors are found to be (1,0,-1), (1,-\sqrt{2},1), and (1,\sqrt{2},1), and the corresponding eigenvalues are 0, -1, and 1. The term "normalized" refers to finding eigenvectors with a norm of 1, which is achieved by dividing each component by the square root of the sum of their squares.
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KeyToMyFire
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Homework Statement



An observable is represented by the matrix

0 [itex]\frac{1}{\sqrt{2}}[/itex] 0
[itex]\frac{1}{\sqrt{2}}[/itex] 0 [itex]\frac{1}{\sqrt{2}}[/itex]
0 [itex]\frac{1}{\sqrt{2}}[/itex] 0

Find the normalized eigenvectors and corresponding eigenvalues.





The Attempt at a Solution



I found the eigenvalues to be 0, -1, and 1

and the eigenvectors to be (1,0,-1), (1,-[itex]\sqrt{2}[/itex],1), and ((1,[itex]\sqrt{2}[/itex],1) (respectively)

I'm pretty sure these are right.


My problem comes with the word "normalized". The only place my lecture notes and book mention normalized eigenvectors is after the matrix is diagonalized, which seems unnecessary.
 
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You want to find eigenvectors (x,y,z) with norm one. So you want eigenvectors (x,y,z) such that [itex]\sqrt{x^2+y^2+z^2}=1[/itex]. That is what normalized means.

The eigenvectors you list are correct, but they are not yet normalized.
 

FAQ: How Do You Normalize Eigenvectors for an Observable Matrix?

What are normalized eigenvectors?

Normalized eigenvectors are eigenvectors that have been divided by their length to have a magnitude of 1. This allows for easier interpretation and comparison of the eigenvectors.

Why are normalized eigenvectors important?

Normalized eigenvectors are important because they represent the direction and magnitude of the eigenvectors, making it easier to understand and interpret the results of an eigenvector analysis.

How are eigenvectors normalized?

Eigenvectors are normalized by dividing each element of the eigenvector by its length (also known as its norm). This results in a vector with a magnitude of 1.

What is the significance of the magnitude of normalized eigenvectors?

The magnitude of normalized eigenvectors represents the strength of the relationship between the variables in a dataset. A larger magnitude indicates a stronger relationship, while a smaller magnitude indicates a weaker relationship.

Can eigenvectors be normalized for non-symmetric matrices?

Yes, eigenvectors can be normalized for non-symmetric matrices. However, the resulting normalized eigenvectors may not be orthogonal, which can affect the interpretation of the results.

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