What Is the Variance of an Eigenvector in a Hermitian Operator?

In summary, the author states that if \psi is an eigenvector of an Hermitian operator A with eigenvalue \lambda, then its variance is 0. However, the person asking the question is having trouble showing this and presents their attempted solution. They receive clarification that their mistake is not normalizing the eigenvector.
  • #1
foxjwill
354
0

Homework Statement


On page 5 of http://arcsecond.wordpress.com/2009...uality-and-heisenbergs-uncertainty-principle/ the author states (w/o proof) that if [tex]\psi[/tex] is an eigenvector (say with eigenvalue [tex]\lambda[/tex]) of an Hermitian operator A (I don't think the Hermitian-ness matters here), then its variance is 0; that is, [tex]\langle \psi| A^2\psi\rangle = \langle \psi| A\psi\rangle^2[/tex]. However, I've not been able to show this.


Homework Equations





The Attempt at a Solution


I keep getting
[tex]\langle \psi|A^2\psi\rangle = \lambda\langle \psi|A\psi\rangle = \lambda^2\langle\psi |\psi\rangle[/tex]​
and
[tex]\langle \psi|A\psi\rangle^2 = \left(\lambda\langle \psi|A\psi\rangle\right)^2 = \lambda^2\langle\psi |\psi\rangle^2.[/tex]​
Where am I going wrong?
 
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  • #2
Eigenvectors are typically normalized, so [itex]\langle\psi\vert\psi\rangle=1[/itex]
 
  • #3
gabbagabbahey said:
Eigenvectors are typically normalized, so [itex]\langle\psi\vert\psi\rangle=1[/itex]

D'oh! Of course! Thanks!
 

Related to What Is the Variance of an Eigenvector in a Hermitian Operator?

1. What is the variance of an eigenvector?

The variance of an eigenvector is a measure of the spread or variability of the data points around the eigenvector. It represents how much the data points diverge from the eigenvector's direction.

2. How is the variance of an eigenvector calculated?

The variance of an eigenvector is calculated by taking the dot product of the eigenvector with each data point, squaring the result and then taking the average of all these values. This can also be expressed as the eigenvalue associated with the eigenvector.

3. What does a high variance of an eigenvector indicate?

A high variance of an eigenvector indicates that the data points are spread out around the eigenvector, suggesting that this eigenvector is an important factor in explaining the variability in the data.

4. How does the variance of an eigenvector relate to the eigenvalue?

The variance of an eigenvector is directly proportional to the corresponding eigenvalue. This means that a larger eigenvalue indicates a higher variance of the eigenvector, and therefore a more important factor in explaining the data's variability.

5. Can the variance of an eigenvector be negative?

No, the variance of an eigenvector cannot be negative. It is always a positive value that represents the spread of the data points around the eigenvector's direction.

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