Can Two Hermitian Matrices be Simultaneously Diagonalized if They Commute?

In summary, the conversation discusses simultaneous diagonalization of hermitian matrices and the proof for when the matrices are both degenerate. The key idea is that if the matrices commute, they can be diagonalized by the same basis vectors. However, if two or more eigenvalues are the same, the proof becomes more complex. The conversation also includes a detailed explanation of the proof, including the use of block diagonal elements and a unitary transformation. The conversation concludes with the understanding that the transformation only affects a subset of the original basis states and the importance of the matrices being hermitian.
  • #1
Mr confusion
73
1
hello,
i am having some trouble understanding simultaneous diagonalization. i have understood the proof which tells us that two hermitian matrices can be simultaneously diagonalized by the same basis vectors if the two matrices commute. but my book then shows a proof for the case when the matrices are both degenerate. it is basically going over my head, will anyone please help me with a good proof/ sketch?
my book uses block diagonal elements for the proof which i cannot understand...:confused:
thank you.
 
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  • #2
What do you mean by "the matricies are both degenerate"?
 
  • #3
i mean that multiple eigenvectors for the same eigenvalue.
ie. the characteristic equation has multiple roots like 3,3,1 etc.
the characteristic equation of both matrices have this property.

am i clear now,sir?
 
  • #4
Let [itex]|i\rangle[/itex] be an eigenstate of [itex]A[/itex] with eigenvalue [itex]a_i[/itex]. Then, sandwiching [itex]AB-BA=0[/itex] between [itex]\langle i|[/itex] and [itex]|j\rangle[/itex], we get

[tex](a_i-a_j)\langle i|B|j\rangle = 0.[/tex]

So, if [itex]a_i\ne a_j[/itex], then [itex]B_{ij}\equiv \langle i|B|j\rangle = 0[/itex]. If all the eigenvalues of [itex]A[/itex] are different, then [itex]a_i\ne a_j[/itex] whenever [itex]i\ne j[/itex], and so all the off-diagonal elements of [itex]B_{ij}[/itex] are zero. Thus [itex]B[/itex] is diagonal in the same basis as [itex]A[/itex].

But if two or more eigenvalues of [itex]A[/itex] are the same (say [itex]a_1=a_2=\ldots=a_N[/itex]) then we do not know anything about [itex]B_{ij}[/itex] for [itex]i,j = 1,\ldots,N[/itex].

Let [itex]b[/itex] be the [itex]N\times N[/itex] hermitian matrix with matrix elements [itex]B_{ij}[/itex], [itex]i,j = 1,\ldots,N[/itex]. We can diagonalize this matrix with a unitary transformation, [itex]b=UdU^\dagger[/itex], where [itex]d[/itex] is diagonal. (It does not matter whether or not any of the diagonal elements of [itex]d[/itex] are equal.) Now define new basis states [itex]|\tilde i\rangle[/itex]; these are the same as the old basis states for [itex]i>N[/itex], and for [itex]1\le i\le N[/itex],

[tex]|i'\rangle = \sum_{j=1}^N U_{ij}|j\rangle.[/tex]

Now the [itex]|i'\rangle[/itex] states are eigenstates of both [itex]A[/itex] and [itex]B[/itex]. For [itex]i=1,\ldots,N[/itex], the eigenvalue of [itex]A[/itex] is [itex]a_1[/itex], and the eigenvalue of [itex]B[/itex] is [itex]d_i[/itex].
 
  • #5
many many thanks avodyne. i am just starting this subject, so i am inexperienced . but i thought that the first step will give us (ai*-aj).
also please tell me how i can be sure that the new basis,' i ' prime will still remain the basis of A after the unitary passive transformation that diagonalises B?
please don't be angry with me. i am slow and take much time to understand things.:frown:
 
  • #6
Mr confusion said:
i thought that the first step will give us (ai*-aj).
Since A is hermitian, the eigenvalues are real.
Mr confusion said:
also please tell me how i can be sure that the new basis,' i ' prime will still remain the basis of A after the unitary passive transformation that diagonalises B?
We are making the transformation only among the [itex]N[/itex] states that all have the same eigenvalue of [itex]A[/itex]. In this subspace, [itex]A[/itex] can be written as the number [itex]a_1[/itex] (the eigenvalue of [itex]A[/itex]) times the [itex]N\times N[/itex] identity matrix [itex]I[/itex]. A unitary transformation in this subspace therefore leaves [itex]A[/itex] unchanged.
 
  • #7
understood. thanks.:biggrin:
 

FAQ: Can Two Hermitian Matrices be Simultaneously Diagonalized if They Commute?

What is simultaneous diagonalization?

Simultaneous diagonalization is a mathematical process used to transform a set of matrices into a diagonal form, where all elements off the main diagonal are zero. This process is often used in linear algebra and has applications in fields such as physics, engineering, and computer science.

How is simultaneous diagonalization different from regular diagonalization?

Regular diagonalization involves transforming a single matrix into diagonal form, while simultaneous diagonalization involves transforming a set of matrices into diagonal form simultaneously. This means that the diagonal elements of the transformed matrices will be the same, allowing for easier calculations and analysis.

What are the benefits of simultaneous diagonalization?

Simultaneous diagonalization can simplify complex mathematical calculations by reducing the number of non-zero elements in a set of matrices, making it easier to solve problems and analyze data. It also has applications in areas such as quantum mechanics, control theory, and signal processing.

What are the conditions for simultaneous diagonalization?

The conditions for simultaneous diagonalization are that the matrices must commute (i.e. they can be multiplied in any order and still produce the same result) and have a complete set of eigenvectors that form a basis for the vector space. In other words, the matrices must share the same set of eigenvectors.

How is simultaneous diagonalization used in real-world applications?

Simultaneous diagonalization has many practical applications, such as in quantum mechanics where it is used to solve systems of equations and analyze quantum states. It is also used in control theory to study the behavior of systems and in signal processing to analyze and filter data. Additionally, it has applications in engineering and physics for solving complex mathematical problems.

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