Hermitian matrix with negative eigenvalue

In summary: T)?In summary, the problem is trying to find a vector v such that (v^T)(A)(v bar) <=0. The Attempt at a Solution states that let w be an eigenvetor and we have the following equalities which are equivalent:Aw=\lambdaw A(w bar) = \lambda (w bar) (i am not sure about this equality)(w^t)(A)(w bar) = (w^t)\lambda (w bar)[(w^t)(A)(w bar)]^T = [(w^t)\lambda (w bar)]^T(w bar)^T*(A)
  • #1
snakebite
16
0

Homework Statement


Hello,
I have the following problem:
Suppose A is a hermitian matrix and it has eigenvalue [tex]\lambda[/tex] <=0. Show that A is not positive definite i.e there exists vector v such that (v^T)(A)(v bar) <=0

The Attempt at a Solution


Let w be an eigenvetor we have the following equalities which are equivalent:
Aw=[tex]\lambda[/tex]w
A(w bar) = [tex]\lambda[/tex] (w bar) (i am not sure about this equality)
(w^t)(A)(w bar) = (w^t)[tex]\lambda[/tex] (w bar)
[(w^t)(A)(w bar)]^T = [(w^t)[tex]\lambda[/tex] (w bar)]^T
(w bar)^T*(A)^T*w = (w bar)^T*[tex]\lambda[/tex]*w
[(w bar)^T*(A)^T*w]bar = [(w bar)^T*[tex]\lambda[/tex]*w]bar
(w^T)(A bar)^T(w bar) = (w^T)[tex]\lambda[/tex](w bar)
(w^T)A(w bar) = (w^T)[tex]\lambda[/tex](w bar)

but i cannot prove why (w^T)[tex]\lambda[/tex](w bar) is negative, assuming taht the 2ndd equality is true.

Thank you
 
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  • #2
Well, (w^T)(w bar) is positive, isn't it? It's sum of w_i*(w_i bar), right?
 
  • #3
I'm not to sure what you mean by w_i?

Also, I'm still not completely convinced that
Aw=[tex]\lambda[/tex]w is equivalent to A(w bar) = [tex]\lambda[/tex](w bar).
Is it generally true that the conjugate is also an eigenvector?

Thanks
 
  • #4
snakebite said:
I'm not to sure what you mean by w_i?

Also, I'm still not completely convinced that
Aw=[tex]\lambda[/tex]w is equivalent to A(w bar) = [tex]\lambda[/tex](w bar).
Is it generally true that the conjugate is also an eigenvector?

Thanks

w_i is the i component of the vector w. No, you can't go from Aw=lambda*w to A(w bar)=lambda (w bar). What you can do is take conjugate transpose of both sides. (w bar)^T (A bar)^T=((lambda bar) (w bar)^T). Do you see how to get what you want from there?
 
  • #5
yea if we start off with (w bar)^T(A bar)^T then this is equivalent to the following(I am only looking at the left side of the equation)
(w bar)^T(A bar)^T(w bar)
[(w bar)^T(A bar)^T(w bar)]^T since it is a 1x1 matrix
(w bar)^T(A bar)(w)
[(w bar)^T(A bar)(w)]bar
(w^T)(A)(w bar)

and on the left side we're left with [tex]\lambda[/tex](w^T)(w bar) which is negative, hence the left side is negative and therefore A is not positive definite
Correct?
 
  • #6
snakebite said:
yea if we start off with (w bar)^T(A bar)^T then this is equivalent to the following(I am only looking at the left side of the equation)
(w bar)^T(A bar)^T(w bar)
[(w bar)^T(A bar)^T(w bar)]^T since it is a 1x1 matrix
(w bar)^T(A bar)(w)
[(w bar)^T(A bar)(w)]bar
(w^T)(A)(w bar)

and on the left side we're left with [tex]\lambda[/tex](w^T)(w bar) which is negative, hence the left side is negative and therefore A is not positive definite
Correct?

No, not correct. I really don't know how you are justifying some of those steps. Can you show what happens to the right side as well? And you have to use that A is hermitian, otherwise the statement isn't true. What does it mean for A to be Hermitian?
 
  • #7
So, far we have Aw=[tex]\lambda[/tex]w
so by taking the transpose and getting the conjugate we get
(w bar)^T(A bar)^T = (w bar)^T ([tex]\lambda[/tex] bar)^T but A is hermitian and lamba is a real number therefore we get

(w bar)^T(A) = [tex]\lambda[/tex] (w bar)^T

now if I want to arrive at w^TA(w bar) on the right side i will have to either add a w factor in which case i can:
-get the transpose of the whole equation, at which point i will get an extra ^T on the A. (w^T)(A)^T(w bar)
-or i can but a bar over the whole thing where i would be left with an extra bar on the A.(w^T)(A bar)(w bar)
I cannot quite fix the A in order to get the correct equation. Is there some kind of property that I am missing here?

Thanks again
 
  • #8
snakebite said:
So, far we have Aw=[tex]\lambda[/tex]w
so by taking the transpose and getting the conjugate we get
(w bar)^T(A bar)^T = (w bar)^T ([tex]\lambda[/tex] bar)^T but A is hermitian and lamba is a real number therefore we get

(w bar)^T(A) = [tex]\lambda[/tex] (w bar)^T

now if I want to arrive at w^TA(w bar) on the right side i will have to either add a w factor in which case i can:
-get the transpose of the whole equation, at which point i will get an extra ^T on the A. (w^T)(A)^T(w bar)
-or i can but a bar over the whole thing where i would be left with an extra bar on the A.(w^T)(A bar)(w bar)
I cannot quite fix the A in order to get the correct equation. Is there some kind of property that I am missing here?

Thanks again

You just need 'some vector' v. v doesn't have to be the same as w. How about picking v=(w bar)?
 
  • #9
Ah yes I didn't think of that
so we have (w bar)^T A = (lambda)(w bar)^T
replacing w bar by v and then multiplying both sides by (v bar) we get
(v^T)(A)(v bar) = (lambda)(v^T)(v bar) but (v^T)(v bar)= ((z1(z1 bar) + ... + zn(zn bar)) which is positive hence (lambda)(v^T)(v bar) is negative which is what we wanted to show.

right?
 
  • #10
snakebite said:
Ah yes I didn't think of that
so we have (w bar)^T A = (lambda)(w bar)^T
replacing w bar by v and then multiplying both sides by (v bar) we get
(v^T)(A)(v bar) = (lambda)(v^T)(v bar) but (v^T)(v bar)= ((z1(z1 bar) + ... + zn(zn bar)) which is positive hence (lambda)(v^T)(v bar) is negative which is what we wanted to show.

right?

Right.
 

Related to Hermitian matrix with negative eigenvalue

What is a Hermitian matrix with negative eigenvalue?

A Hermitian matrix is a square matrix that is equal to its own conjugate transpose. A negative eigenvalue is a value that, when multiplied by its corresponding eigenvector, produces a negative scalar multiple of the eigenvector. Therefore, a Hermitian matrix with a negative eigenvalue is a square matrix that satisfies both of these conditions.

What is the significance of a Hermitian matrix with negative eigenvalue?

A Hermitian matrix with negative eigenvalue has important implications in the field of quantum mechanics. In this context, the eigenvalues of a Hermitian matrix represent the possible observable values of a quantum system. A negative eigenvalue indicates that the corresponding eigenvector is in an unstable state, meaning that the system is unstable and will eventually decay.

How can a Hermitian matrix have a negative eigenvalue if it is equal to its own conjugate transpose?

It is important to note that the eigenvalues of a Hermitian matrix do not necessarily have to be real numbers. In fact, they can be complex numbers. The fact that a Hermitian matrix is equal to its own conjugate transpose ensures that the complex eigenvalues occur in conjugate pairs. Therefore, a negative eigenvalue could be paired with a positive eigenvalue, resulting in a complex conjugate pair.

What types of matrices can have a negative eigenvalue?

Any square matrix can have a negative eigenvalue, as long as it satisfies the condition of being equal to its own conjugate transpose. However, Hermitian matrices are the only type of matrices that are guaranteed to have real eigenvalues, making them particularly useful in quantum mechanics.

What are some applications of Hermitian matrices with negative eigenvalues?

Hermitian matrices with negative eigenvalues have a wide range of applications in physics, particularly in quantum mechanics and quantum field theory. They are used to model unstable particles and decay processes, as well as to predict the behavior of quantum systems. They are also used in other fields such as signal processing and image compression.

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