Hermitian operators, matrices and basis

In summary, the conversation discusses the representation of operators as matrices and the concept of diagonal matrices in different bases. It is established that hermitian operators can be represented by diagonal matrices in the basis of their eigenvectors, with the diagonal elements being the eigenvalues of the operator. However, this may not always be possible in infinite-dimensional vector spaces.
  • #1
Trixie Mattel
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Hello, I would just like some help clearing up some pretty basic things about hermitian operators and matricies.

I am aware that operators can be represented by matricies. And I think I am right in saying that depending on the basis used the matrices will look different, but all our valid representations of the operator.

As I understand it, there exists a basis where hermitian operators can be represented by a diagonal matrix

Is the only basis that this can occur in the basis of the eigenvectors of the hermitian operators. And the diagonal matrix elements of the hermitian operators are the eigenvalues of the operator??

In summary I am asking, is the only basis in which a hermitian operator is represented by a diagonal basis the basis of the eigenvectors? And for the diagonal matrix are the elements the eigenvalues of the operator?Thank you
 
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  • #2
Trixie Mattel said:
Hello, I would just like some help clearing up some pretty basic things about hermitian operators and matricies.

I am aware that operators can be represented by matricies. And I think I am right in saying that depending on the basis used the matrices will look different, but all our valid representations of the operator.

As I understand it, there exists a basis where hermitian operators can be represented by a diagonal matrix

Is the only basis that this can occur in the basis of the eigenvectors of the hermitian operators. And the diagonal matrix elements of the hermitian operators are the eigenvalues of the operator??

In summary I am asking, is the only basis in which a hermitian operator is represented by a diagonal basis the basis of the eigenvectors? And for the diagonal matrix are the elements the eigenvalues of the operator?Thank you

Given any basis [itex]|\psi_j\rangle[/itex], you can use that basis to represent arbitrary states as column matrices. Letting [itex]\mathcal{R}(|\psi\rangle)[/itex] mean the matrix representation of [itex]|\psi\rangle[/itex], we can choose [itex]\mathcal{R}[/itex] so that:

[itex]\mathcal{R}(|\psi_1\rangle) = \left( \begin{array} \\ 1 \\ 0 \\ . \\ . \\ . \end{array} \right) [/itex]

[itex]\mathcal{R}(|\psi_2\rangle) = \left( \begin{array} \\ 0 \\ 1 \\ . \\ . \\ . \end{array} \right) [/itex]

[itex]\mathcal{R}(|\psi_3\rangle) = \left( \begin{array} \\ 0 \\ 0 \\ 1 \\ 0 \\ . \\ . \\ . \end{array} \right) [/itex]

To say that an operator [itex]\hat{O}[/itex] is diagonal in this basis is to say that its representation is given by:

[itex]\mathcal{R}(\hat{O}) = \left( \begin{array} \\ \lambda_1 & 0 & ... \\ 0 & \lambda_2 & 0 & ... \\ 0 & 0 & \lambda_3 & ... \\ . \\ . \\ . \end{array} \right) [/itex]

Yes, [itex]\lambda_1, \lambda_2, ...[/itex] are the eigenvalues of [itex]\hat{O}[/itex] and [itex]|\psi_j\rangle[/itex] are the eigenvectors.
 
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  • #3
Trixie Mattel said:
In summary I am asking, is the only basis in which a hermitian operator is represented by a diagonal basis the basis of the eigenvectors? And for the diagonal matrix are the elements the eigenvalues of the operator?

Yes, but note that you can put the eigenvalues to many different orders on the diagonal, in which case different unit vectors of Rn correspond to the values. Also, an operator can be defined on an infinite-dimensional vector space, in which case you can't write it as a matrix.
 
  • #4
hilbert2 said:
Also, an operator can be defined on an infinite-dimensional vector space, in which case you can't write it as a matrix.

Why not?
 
  • #5
PeroK said:
Why not?

Well, you can do it if the basis vectors can be ordered in such a way that there's some obvious pattern in the matrix elements, but think about a vector space that has a basis with the same cardinality as the set of real numbers.
 
  • #6
hilbert2 said:
Well, you can do it if the basis vectors can be ordered in such a way that there's some obvious pattern in the matrix elements, but think about a vector space that has a basis with the same cardinality as the set of real numbers.

Got it, thank you!
 

1. What is a Hermitian operator?

A Hermitian operator is a type of linear operator in mathematics that has special properties. It is a square matrix that is equal to its own conjugate transpose, meaning that the complex conjugate of each element is equal to the element in the same position, but with the rows and columns reversed.

2. How do Hermitian operators relate to quantum mechanics?

In quantum mechanics, Hermitian operators are used to represent physical observables, such as position, momentum, and energy. The eigenvalues and eigenvectors of these operators correspond to the possible outcomes and states of a quantum system.

3. What is the difference between a Hermitian matrix and a normal matrix?

A Hermitian matrix is a square matrix that is equal to its own conjugate transpose, while a normal matrix is only equal to its own transpose. In other words, a Hermitian matrix is a special case of a normal matrix where the elements are complex numbers.

4. What is a basis in linear algebra?

In linear algebra, a basis is a set of linearly independent vectors that span a vector space. This means that any vector in the space can be written as a unique combination of the basis vectors. It is often used to represent mathematical objects, such as matrices and operators, in a more concise and efficient way.

5. How are matrices used to represent linear transformations?

Matrices can be used to represent linear transformations by mapping the basis vectors to their respective images. This allows for a more efficient and compact representation of the transformation, as well as easy computation of its effects on vectors. The matrix representation of a linear transformation is unique for a given basis.

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