Orthonormality condition proof

In summary, the orthogonality condition for a complete set of states can be established by subtracting two equations derived from the eigenstates of the Hamiltonian and using the Hermiticity property. This results in either the eigenvalues being different for distinct states, proving orthogonality, or the eigenvalues being real, proving normality.
  • #1
raintrek
75
0
I'm working through a proof of the orthormality condition for a complete set of states and am struggling with one element of it:

Consider the eigenstates of the Hamiltonian in the following way:

1: [tex]\int\Psi^{*}_{m}H\Psi_{n}dV = E_{n}\int\Psi^{*}_{m}H\Psi_{n}dV[/tex]

and

2: [tex]\int\Psi^{*}_{n}H\Psi_{m}dV = E_{m}\int\Psi^{*}_{n}H\Psi_{m}dV[/tex]

Taking the complex conjugate of the second (for H real) to obtain

3: [tex]\int\Psi_{n}H\Psi^{*}_{m}dV = E_{m}\int\Psi_{n}H\Psi^{*}_{m}dV[/tex]

Now subtract them:

1-3: [tex]\int\Psi^{*}_{m}H\Psi_{n}dV - \int\Psi^{*}_{n}H\Psi_{m}dV = (E_{n} - E_{m}) \int\Psi^{*}_{m}H\Psi_{n}dV[/tex]

I don't understand why the LHS of eq. 3 in this subtraction has had it's complex conjugate taken again, yet the right hand side has remained as it is in 3. Is it something to do with the Hermiticity of the Hamiltonian? Many thanks
 
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  • #2
Orthonormality can be established as follows. Start with

[tex]H\Psi_{m} = E_{m}\Psi_{m}[/tex] and

[tex]H\Psi_{n} = E_{n}\Psi_{n}[/tex].

Multiply top by [tex]\Psi^{*}_{n}[/tex] and integrate, likewise with [tex]\Psi^{*}_{m}[/tex] for the second. Complex conjugate the resulting 2nd equation and use the Hermiticity of H

[tex]\int\Psi^{*}_{m}H\Psi_{n}dV =\int\Psi^{*}_{n}H\Psi_{m}dV[/tex]

to rewrite the left hand side. Subtracting the two equations now gives

[tex]0=(E_{m}-E^{*}_{n})\int\Psi^{*}_{m}\Psi_{n}dV.[/tex]

Now either

[tex]\int\Psi^{*}_{m}\Psi_{n}dV=0, m\neq n[/tex]

because the eigenvalues of distinct states are different, establishing orthogonality, or

[tex]E_{m}=E^{*}_{m}[/tex]

establishing that the eigenvalues are real.

Normality simply comes from normalizing the eigenfunctions so that

[tex]\int\Psi^{*}_{m}\Psi_{m}dV=1.[/tex]
 

1. What is the purpose of proving the orthonormality condition?

The orthonormality condition is a fundamental concept in linear algebra and is essential for understanding vector spaces, matrices, and their properties. By proving the orthonormality condition, we can verify that a set of vectors is orthogonal (perpendicular) and normalized (unit length). This condition is crucial in many applications, including signal processing, quantum mechanics, and machine learning.

2. How is the orthonormality condition defined?

The orthonormality condition states that a set of vectors is orthonormal if every pair of vectors in the set is orthogonal (perpendicular) and all vectors have a unit length (normalized). In other words, the dot product of any two vectors in the set is equal to zero, and the norm (magnitude) of each vector is equal to one.

3. What are the steps for proving the orthonormality condition?

The following are the general steps for proving the orthonormality condition:
Step 1: Show that the vectors in the set are orthogonal by calculating their dot products.
Step 2: Show that each vector in the set has a unit length by calculating its norm.
Step 3: Combine the results from steps 1 and 2 to prove that the set of vectors is both orthogonal and normalized, thus satisfying the orthonormality condition.

4. Can the orthonormality condition be applied to any set of vectors?

Yes, the orthonormality condition can be applied to any set of vectors in a vector space. However, it is most commonly used for sets of basis vectors, which form a basis for the vector space. In this case, the orthonormality condition is crucial for performing operations such as vector projections, transformations, and solving systems of equations.

5. Are there any real-world applications of the orthonormality condition?

Yes, the orthonormality condition has many real-world applications. Some examples include:
- In signal processing, the orthonormality condition is used to analyze signals and filter out noise.
- In quantum mechanics, the orthonormality condition is used to describe the state of a quantum system and calculate probabilities.
- In machine learning, the orthonormality condition is used to reduce the dimensionality of data and improve the performance of algorithms.

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