Group averaging - Inner product

Gdg \langle\phi|\hat{U}(g)|\phi\rangle = \int_Gdg ||\phi||^2. Since ||\phi||^2 \geq 0, the integral can only equal 0 if ||\phi||^2 = 0, which means that \phi = 0. Therefore, we can conclude that \int_Gdg \langle\phi|\hat{U}^{-1}(g)|\phi\rangle = 0 \Rightarrow \int_Gdg \langle\phi|\hat{U}^{-1}(g) = 0 if and only if \phi = 0.In summary, to
  • #1
kakarukeys
190
0

Homework Statement


Does anyone know how to show the inner product obtained from group averaging is positive definite?

Show that
[tex]\int_Gdg \langle\phi|\hat{U}^{-1}(g)|\phi\rangle \geq 0[/tex]
[tex]\int_Gdg \langle\phi|\hat{U}^{-1}(g)|\phi\rangle = 0 \Rightarrow \int_Gdg \langle\phi|\hat{U}^{-1}(g) = 0[/tex]

Homework Equations


[tex]\langle\langle \eta\phi_1, \eta\phi_2\rangle\rangle_{phys} = \int_Gdg \langle\phi_2|\hat{U}^{-1}(g)|\phi_1\rangle[/tex]
[tex]\eta[/tex] is the rigging map (not important here, just ignore the L.H.S.)

[tex]\eta: \Phi \longrightarrow \Phi'[/tex]
[tex]\eta\phi = \int_Gdg \langle\phi|\hat{U}^{-1}(g)[/tex]

[tex]\phi, \phi_1, \phi_2\in\Phi\subset H[/tex] are vectors in a dense subspace of the Hilbert space H.
[tex]\hat{U}(g)[/tex] is a unitary representation of G (densely defined) on the Hilbert space H.
[tex]dg[/tex] is a bi-invariant measure on G

The Attempt at a Solution


No idea at all. Not sure what kind of maths is needed.
 
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  • #2




Thank you for your question. To show that the inner product obtained from group averaging is positive definite, we need to prove that \langle\phi|\hat{U}^{-1}(g)|\phi\rangle \geq 0 for all \phi\in\Phi. This can be done using the properties of a unitary representation and the bi-invariant measure on G.

Firstly, we know that \hat{U}(g) is a unitary operator, which means that it preserves the inner product. This can be written as \langle\phi_1|\hat{U}(g)|\phi_2\rangle = \langle\hat{U}(g)\phi_1|\hat{U}(g)\phi_2\rangle for all \phi_1, \phi_2\in H. Therefore, we can rewrite the inner product obtained from group averaging as \langle\phi|\hat{U}^{-1}(g)|\phi\rangle = \langle\hat{U}(g)\phi|\phi\rangle.

Next, we can use the bi-invariant measure on G to rewrite the integral as \int_Gdg \langle\hat{U}(g)\phi|\phi\rangle = \int_Gdg \langle\phi|\hat{U}(g)|\phi\rangle. This is because the bi-invariant measure is invariant under left and right translations on G, and therefore the order of the elements in the integral does not matter.

Now, since \hat{U}(g) is a unitary representation, we know that it preserves the norm of a vector. This means that ||\hat{U}(g)\phi|| = ||\phi|| for all \phi\in H. Therefore, we can rewrite the integral as \int_Gdg \langle\phi|\hat{U}(g)|\phi\rangle = \int_Gdg ||\phi||^2 \geq 0. This shows that the inner product obtained from group averaging is positive definite, as the integral is always greater than or equal to 0.

To show that \int_Gdg \langle\phi|\hat{U}^{-1}(g)|\phi\rangle = 0 \Rightarrow \int_Gdg \langle\phi|\hat{U}^{-1}(g) = 0, we can use the fact that \hat{U}(g)
 

1. What is group averaging?

Group averaging is a statistical method used to combine multiple measurements or observations from different groups or categories into a single overall estimate. It is typically used when comparing the average values of a certain variable between different groups.

2. How is group averaging performed?

Group averaging is performed by taking the average of each group's values and then calculating a weighted average of these averages, with the weights determined by the sizes of the groups. This results in a single overall average value that represents the combined data from all the groups.

3. What is the purpose of group averaging?

The purpose of group averaging is to reduce the effects of random variation within each group and to provide a more accurate estimate of the overall average value. It is commonly used in research and data analysis to compare the means of different groups and to make more informed conclusions.

4. Can group averaging be used with non-numerical data?

Yes, group averaging can be used with non-numerical data, such as categorical or binary data. In this case, the average values for each group would be calculated based on the proportion of observations falling into each category or group.

5. What is the role of the inner product in group averaging?

The inner product is a mathematical operation used in group averaging to calculate the weighted average of the individual group averages. It assigns a weight to each group's average based on the similarity or dissimilarity of their values, which helps to account for potential biases or differences between the groups.

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