Do Commuting Operators Always Share a Common Basis of Eigenvectors?

In summary: If A and B are hermitian operators then they commute if and only if there is a common basis of eigenvectors.
  • #1
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Hey guys,
I'm studying some quantum physics at the moment and I'm having some problems with understanding the principles behind the necessary lineair algebra:

1) If two operators do NOT commutate, is it correct to conclude they don't have a similar basis of eigenvectoren? Or are there more conditions to be verified to conclude this?

2) If two operators DO commutate, you MAY find a similar basis of eigenvectoren? If this is correct, what are the conditions for it to be absolutely true?

The context to which I am asking these question are the ladder operators for angular momentum states and especially the fact that Lx and Ly do not commutate and don't have a similar basis of eigenvectors, whereas L^2 and Lx/Ly/Lz do commutate and do have a basis of eigenvectors.

Thanks in advance!
 
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  • #2
In my ancient lecture notes from college I remember having the proof of a strong theorem:

If A and B are 2 bounded and self-adjoint linear operators with discrete spectrum on a separable Hilbert space, they commute if and only if the Hilbert space has an orthonormal basis made up of common eigenvectors of A and B.

This theorem applies to your question, with the minor notice that the orbital angular momentum ops are generally unbounded in the Hilbert space [itex] L^2 \left(\mathbb{R}^3, dx\right) [/itex], so one speaks about the closures of the these operators when referring to self-adjointness and the common dense domain involved is not the domain of the closure, but of the original operator.
 
  • #3
bigubau said:
If A and B are 2 bounded and self-adjoint linear operators with discrete spectrum on a separable Hilbert space, they commute if and only if the Hilbert space has an orthonormal basis made up of common eigenvectors of A and B.

In the standard QM setup it will be not too dangerous to assume that the following holds:

If A and B are hermitian operators then they commute if and only if there is a common basis of eigenvectors.

This basis can be discrete or continuous or partly discrete and partly continuous - whatever it may mean.
 

1. What are eigenvectors and why are they important in science?

Eigenvectors are special vectors that, when multiplied by a matrix, produce a scalar multiple of themselves. In other words, they are the "directions" that do not change when the matrix is applied. In science, eigenvectors are important because they can help us understand the behavior of complex systems, such as quantum mechanical systems, and can be used to solve important equations in physics and engineering.

2. How do eigenvectors relate to eigenvalues?

Eigenvectors are associated with eigenvalues, which are the scalars that result from multiplying an eigenvector by a matrix. The eigenvalue represents the magnitude of the change that occurs when the matrix is applied to the eigenvector. In other words, the eigenvalue tells us how much the eigenvector is stretched or squished by the matrix.

3. Can two matrices commute?

Yes, two matrices can commute if their multiplication results in the same matrix regardless of the order in which they are multiplied. In other words, if matrix A and matrix B commute, then AB = BA. This is often the case with diagonal matrices, where the eigenvectors of each matrix are the same.

4. What is the significance of matrices that do not commute?

When two matrices do not commute, it means that the order in which they are multiplied matters. This can have important implications in science, as it can affect the behavior and outcomes of complex systems. In quantum mechanics, for example, the non-commutativity of certain operators is a fundamental principle that leads to the uncertainty principle.

5. How are eigenvectors and commutation used in real-world applications?

Eigenvectors and commutation have many practical applications in science and engineering. In physics, they are used to analyze and solve problems related to quantum mechanics, electromagnetism, and fluid dynamics. In engineering, they are used in fields such as signal processing, control systems, and data analysis. Additionally, they have applications in other fields such as economics, social sciences, and computer science.

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