Another proof on self adjointness

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In summary: For instance, the zero map is self-adjoint, but it is certainly not onto R^3.In summary, the conversation discusses the proof that there does not exist a self-adjoint operator T ∈ L(R3) such that T(1, 2, 3) = (0, 0, 0) and T(2, 5, 7) = (2, 5, 7). The proof involves using T's identity as a self-adjoint operator and the properties of the inner product to show that T cannot be self-adjoint. However, it is not clear whether or not such an operator exists.
  • #1
evilpostingmong
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Homework Statement


Prove that there does not exist a self-adjoint operator T ∈ L(R3)
such that T(1, 2, 3) = (0, 0, 0) and T(2, 5, 7) = (2, 5, 7).

Homework Equations





The Attempt at a Solution


I'm having trouble seeing that there is an actual operator, self adjoint or not,
that can do this. I mean, according to this, T maps from R^3 to R^3
but because it maps to R^3 and not to any subspace of R^3 (since it can map (2 5 7 ) to itself), its nullspace must be dim 0. The thing is, since (1 2 3) cannot be in the nullspace
of T, T must be a zero transformation or its matrix might have [1 1 -1] for all three rows. But that can't happen if T(2 5 7)=(2 5 7). I mean, if T cannot exist, then neither can its adjoint.
 
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  • #2
evilpostingmong said:
I mean, according to this, T maps from R^3 to R^3
but because it maps to R^3 and not to any subspace of R^3 (since it can map (2 5 7 ) to itself), its nullspace must be dim 0.


I'm not quite sure what you mean by this. Just because it maps to R3 doesn't mean that it has to map onto R3. Or maybe you mean something else, but still, knowing that one vector gets mapped to itself doesn't mean that no other vector can be mapped to zero (unless I'm missing something).

Still, despite your misgivings, the proof is pretty straight-forward - all it takes is using T's identity as self-adjoint operator in conjunction with the properties of the inner product.
 
  • #3
cipher42 said:
I'm not quite sure what you mean by this. Just because it maps to R3 doesn't mean that it has to map onto R3. Or maybe you mean something else, but still, knowing that one vector gets mapped to itself doesn't mean that no other vector can be mapped to zero (unless I'm missing something).

Still, despite your misgivings, the proof is pretty straight-forward - all it takes is using T's identity as self-adjoint operator in conjunction with the properties of the inner product.

Yeah that's what I meant to say: it maps onto R^3 and not some 2 or 1 or 0 dimensional
subspace of R^3 in R^3 since T(257)=(257). Just started to get some coffee in me,
so my wordings were a bit messed up. I'm working on this right now.
 
  • #4
evilpostingmong said:
Yeah that's what I meant to say: it maps onto R^3 and not some 2 or 1 or 0 dimensional
subspace of R^3 in R^3 since T(257)=(257). Just started to get some coffee in me,
so my wordings were a bit messed up. I'm working on this right now.

Why not? Is your argument that if, for some v, Tv=v, then T must map onto R3? I'm not sure I see that.

In our case, v = (2,5,7) and Tv=v, but all this gives us is that the one-dimensional subspace spanned by v (namely all vectors of the form av, for some scalar a) gets mapped to 0 (and Tv=0 only when a=0). We don't know anything about vectors outside of this subspace. It would not be a contradiction if dim(null(T)) were not zero.
 
  • #5
Ok <T(1 2 3), (2 5 7)>=0. For <(1 2 3), T(2 5 7)> to=<T(1 2 3), (2 5 7)>,
<(1 2 3), T(2 5 7)> must=0. But <(1 2 3), T(2 5 7)>=<(1 2 3), (2 5 7)>.
So T is not self adjoint since <T(1 2 3), (2 5 7)>=/=<(1 2 3 ), T(2 5 7)>.
 
  • #6
evilpostingmong said:
Ok <T(1 2 3), (2 5 7)>=0. For <(1 2 3), T(2 5 7)> to=<T(1 2 3), (2 5 7)>,
<(1 2 3), T(2 5 7)> must=0. But <(1 2 3), T(2 5 7)>=<(1 2 3), (2 5 7)>.
So T is not self adjoint since <T(1 2 3), (2 5 7)>=/=<(1 2 3 ), T(2 5 7)>.

Yup, that's dead-on with the proof (though you might want to throw the word 'positive-definite' in there somewhere for clarity). Though I'm still not sure that no such operator exists :smile:
 
  • #7
cipher42 said:
Yup, that's dead-on with the proof (though you might want to throw the word 'positive-definite' in there somewhere for clarity). Though I'm still not sure that no such operator exists :smile:

Yeah, what I don't understand is when you said "In our case, v = (2,5,7) and Tv=v, but all this gives us is that the one-dimensional subspace spanned by v (namely all vectors of the form av, for some scalar a) gets mapped to 0". T(2 5 7) doesn't get mapped to zero and
a=1.
 
  • #8
I was just trying to make sure I understood your argument and then state what I thought could be drawn from your premises. You say

evilpostingmong said:
Yeah that's what I meant to say: it maps onto R^3 and not some 2 or 1 or 0 dimensional
subspace of R^3 in R^3 since T(257)=(257). Just started to get some coffee in me,
so my wordings were a bit messed up. I'm working on this right now.

So your argument is that because T(2,5,7)=(2,5,7), the map is onto R^3, right?

I'm just saying that all you have shown is that Tv=v for one single v (namely v=(2,5,7)), and I don't see how that is a strong enough premise to conclude that T maps onto R^3. Obviously, with v=(2,5,7) and some scalar a, T(av)=aT(v)=av, but v does not span all of R^3. You have no idea what happens to other vectors that are not multiples of v. It could easily be the case that other vectors - that is to say, those not of the form av for some a - could be mapped to zero, so a large number of vectors would be without preimages under T (slightly awkward terminology for linear algebra, but hopefully you see what I mean).
 

1. What is "Another proof on self adjointness"?

"Another proof on self adjointness" refers to a mathematical concept in linear algebra that deals with linear transformations and their associated matrices. It is a way to determine if a linear transformation is self-adjoint, meaning its matrix representation is equal to its own conjugate transpose.

2. Why is self adjointness important?

Self adjointness is important because it has many applications in physics, engineering, and other fields. It allows us to simplify and solve complex equations, making it an essential tool in mathematical analysis and modeling.

3. What is the difference between self adjointness and symmetry?

Self adjointness and symmetry are related concepts, but they are not the same. A self-adjoint matrix is always symmetric, but a symmetric matrix is not necessarily self-adjoint. In other words, self-adjoint matrices have special properties that make them more useful in mathematical applications.

4. How is self adjointness proven?

There are several ways to prove self adjointness, including using the definition of self adjointness or using properties of eigenvalues and eigenvectors. Another proof on self adjointness is one approach that uses the fact that a self-adjoint matrix is always diagonalizable and its eigenvectors form an orthonormal basis.

5. What are the practical implications of Another proof on self adjointness?

The practical implications of Another proof on self adjointness are that it provides a way to easily and efficiently determine if a linear transformation is self-adjoint. This can be useful in solving differential equations, finding optimal solutions in optimization problems, and other mathematical applications.

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