Properties of hermitian matrices

In summary, the conversation discusses four hermitian matrices obeying the property Mi Mj + Mj Mi = 2δij I, where I is the identity matrix and i,j=1,2,3,4. Part a) shows that the eigenvalues of Mi are +/-1 by using the equation for i=j and considering the eigenbasis of Mi. Part b) uses the relation Mi Mi = -Mj Mi for i≠j to show that Mi are traceless. Part c) concludes that the matrices cannot be odd dimensional because it would not produce a trace of 0, which is necessary for the property to hold.
  • #1
shinobi20
269
20

Homework Statement


Consider hermitian matrices M1, M2, M3, M4 that obey the property Mi Mj + Mj Mi = 2δij I where I is the identity matrix and i,j=1,2,3,4

a) Show that the eigenvalues of Mi=+/- 1 (Hint: Go to the eigenbasis of Mi and use the equation for i=j)
b) By considering the relation Mi Mi= -Mj Mi for i≠j. Show that Mi are traceless (Hint: Tr(ABC)=Tr(CBA)
c) Show that they cannot be odd dimensional matrices

Homework Equations

The Attempt at a Solution


For part a), det(Mi) det(Mj) + det(Mj) det(Mi) = 2δij det(I) implies 2det(Mi)2 = 2det(I). This means det(Mi)=+/-1. Since Mi is hermitian and diagonalizable. The diagonalized matrix will have the same determinant with eigenvalues +/-1 therefore the eigenvalues of Mi are +/-1. This is how I think it can be proved, I'm not sure what the hint is telling. For part b) and c) I really don't know how to start.
 
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  • #2
b) Multiply ##M^i M^j = - M^j M^i## with ##(M^j)^{-1}## from the left. This will give ##(M^j)^{-1} M^i M^j = - M^i##. Then calculate the trace of both sides of the equation and apply the cyclic permutation property of a trace. (Note that you might have made a typo in writing the hint. That relation should follow cyclic permutation, which is not in the way you wrote it).
c) It follows from b) and use the invariant property of a trace under change of basis. The trace is zero, and the eigenvalues of any of those matrices can only be either 1 or -1, so?
 
  • #3
blue_leaf77 said:
b) Multiply ##M^i M^j = - M^j M^i## with ##(M^j)^{-1}## from the left. This will give ##(M^j)^{-1} M^i M^j = - M^i##. Then calculate the trace of both sides of the equation and apply the cyclic permutation property of a trace. (Note that you might have made a typo in writing the hint. That relation should follow cyclic permutation, which is not in the way you wrote it).
c) It follows from b) and use the invariant property of a trace under change of basis. The trace is zero, and the eigenvalues of any of those matrices can only be either 1 or -1, so?

blue_leaf77 said:
b) Multiply ##M^i M^j = - M^j M^i## with ##(M^j)^{-1}## from the left. This will give ##(M^j)^{-1} M^i M^j = - M^i##. Then calculate the trace of both sides of the equation and apply the cyclic permutation property of a trace. (Note that you might have made a typo in writing the hint. That relation should follow cyclic permutation, which is not in the way you wrote it).
c) It follows from b) and use the invariant property of a trace under change of basis. The trace is zero, and the eigenvalues of any of those matrices can only be either 1 or -1, so?
For part b) Tr((Mj)-1 Mj Mi) = -Tr(Mi) imples 2Tr(Mi) = 0 which means Tr(Mi) = 0. For part c) they cannot be odd dimensional because it would not produce a trace that is 0, suppose it is a 3x3 matrix then the eigenvalues would be degenerate and produce a trace either 1 or -1. Is this correct?
 
  • #4
Yes, that's correct.
 
  • #5
blue_leaf77 said:
Yes, that's correct.
How about part a)? I think I did something wrong because I forgot the nonlinearity of determinants.
 
  • #6
shinobi20 said:
Mi Mj + Mj Mi = 2δij
That relation is valid in all basis. You can then assume that they are already diagonalized. Since for ##i=j##, ##(M^i)^2= I##, this means any eigenvalue ##\lambda_k## will satisfy ##\lambda_k^2 = 1##.
 
  • #7
blue_leaf77 said:
That relation is valid in all basis. You can then assume that they are already diagonalized. Since for ##i=j##, ##(M^i)^2= I##, this means any eigenvalue ##\lambda_k## will satisfy ##\lambda_k^2 = 1##.
So you mean I can just assume that they are diagonal and set i=j then add the matrices, that is Mi Mi + Mi Mi = 2I ⇒ 2Mi Mi = 2I ⇒ Mi Mi = I ⇒ det(Mi Mi) = det(I) ⇒ (det(Mi))2 = 1 ⇒ det(Mi)=±1. Since Mi is diagonal ±1 are exactly the eigenvalues of Mi. Is this correct?
 
  • #8
You shouldn't use determinant because determinant is not more unique than the element-by-element comparison. For example I can have the eigenvalues to be 4 and 1/4 (2 by 2 matrix) and their determinant is still one.
 
  • #9
blue_leaf77 said:
You shouldn't use determinant because determinant is not more unique than the element-by-element comparison. For example I can have the eigenvalues to be 4 and 1/4 (2 by 2 matrix) and their determinant is still one.
So maybe I can just manipulate each arbitrary diagonal element mi and show that mi2=±1. Then conclude that EACH eigenvalue can only have values ±1?
 
  • #10
shinobi20 said:
each arbitrary diagonal element
Arbitrary diagonal element in the diagonalized version of the matrix, remember the question asks you about the eigenvalues.
 
  • #11
blue_leaf77 said:
b) Multiply ##M^i M^j = - M^j M^i## with ##(M^j)^{-1}## from the left. This will give ##(M^j)^{-1} M^i M^j = - M^i##. Then calculate the trace of both sides of the equation and apply the cyclic permutation property of a trace. (Note that you might have made a typo in writing the hint. That relation should follow cyclic permutation, which is not in the way you wrote it).
c) It follows from b) and use the invariant property of a trace under change of basis. The trace is zero, and the eigenvalues of any of those matrices can only be either 1 or -1, so?
I just thought of now that how can (Mj)-1(Mj) be the identity matrix, M is just stated to be hermitian, not unitary?
 
  • #12
##(M^j)^{-1}## is the inverse matrix of ##M^j##.
 
  • #13
blue_leaf77 said:
##(M^j)^{-1}## is the inverse matrix of ##M^j##.
AM very sorry for that lousssssy question. Thanks!
 
  • #14
shinobi20 said:
For part b) Tr((Mj)-1 Mj Mi) = -Tr(Mi) imples 2Tr(Mi) = 0 which means Tr(Mi) = 0. For part c) they cannot be odd dimensional because it would not produce a trace that is 0, suppose it is a 3x3 matrix then the eigenvalues would be degenerate and produce a trace either 1 or -1. Is this correct?
I'm a little confused in this part, part a just says that the eigenvalues can only have values 1 or -1 right? So if the M's are not odd dimensional, what if the eigenvalues are all 1's, then it would not add up to 0.
 
  • #15
Actually you don't need part a) to work out part b). In b) you found that the trace must vanish, thus the eigenvalues cannot be all 1 nor all -1.
 
  • #16
blue_leaf77 said:
Actually you don't need part a) to work out part b). In b) you found that the trace must vanish, thus the eigenvalues cannot be all 1 nor all -1.
So how will I argue with part c)?
 
  • #17
From part a) and b), you can write ##\textrm{Tr} (M) = \sum_{k=1}^N \lambda_k = 0## with ##\lambda_k## being either -1 or 1. Think of how ##N## will be if you were to satisfy that equation.
 
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Related to Properties of hermitian matrices

What is a Hermitian matrix?

A Hermitian matrix is a square matrix that is equal to its own conjugate transpose. This means that the entries of the matrix are mirrored across the diagonal and the complex numbers are also conjugated.

What are the properties of a Hermitian matrix?

Some properties of a Hermitian matrix include:

  • Every eigenvalue of a Hermitian matrix is real.
  • A Hermitian matrix is always diagonalizable.
  • The diagonal entries of a Hermitian matrix are real numbers.
  • The sum of two Hermitian matrices is also Hermitian.
  • The product of two Hermitian matrices is Hermitian if and only if the two matrices commute.

How can you determine if a matrix is Hermitian?

A matrix is Hermitian if it is equal to its own conjugate transpose. This means that the matrix must be square and the entries must be equal to their complex conjugates. Another way to check is to see if all the eigenvalues are real.

What is the relationship between Hermitian matrices and symmetric matrices?

A Hermitian matrix is a special case of a symmetric matrix, where the entries are real numbers. In a Hermitian matrix, the entries can also be complex numbers, but they must be equal to their conjugates. A symmetric matrix is still considered to be Hermitian, but a Hermitian matrix is not necessarily symmetric unless the entries are all real numbers.

How are Hermitian matrices used in science?

Hermitian matrices are commonly used in quantum mechanics, as they represent observables in quantum systems. They are also used in statistics and data analysis, as they have properties that make them useful for data transformation and analysis. Additionally, Hermitian matrices have applications in engineering and signal processing, such as in the design of filters and control systems.

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