Self-Adjoint Operators problem

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In summary, if T is a self-adjoint linear operator on an inner product space V and W is a T-invariant subspace of V, then the restriction of T to W, Tw, is also self-adjoint. This can be seen by observing that if T is self-adjoint, then <v,T(w)>=<T(v),w> for all vectors v,w in V, and since W is contained in V, this relation also holds for vectors in W.
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Homework Statement


T a linear operator on inner product space V and W a T-invariant subspace of V. Then if T is self-adjoint then Tw is self-adjoint.


Homework Equations


Thm: T is self-adjoint iff [tex]\exists[/tex] an orthonormal basis for V consisting of e-vectors of T.


The Attempt at a Solution


Let [tex]\beta[/tex]1 be a basis for Tw and by thm can extend to a basis [tex]\beta[/tex] for V, s.t. [tex]\beta[/tex]1[tex]\subseteq[/tex][tex]\beta[/tex]. But by above thm, [tex]\beta[/tex] is ON and consists of e-vectors of T, so then [tex]\beta[/tex]1 is also ON and consists of e-vectors of T, and Tw is self-adjoint.

Does my proof make any sense?? Thanks everyone!
 
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  • #2
Tw is the restriction of T to W, right? Which makes sense as an operator on W since W is invariant? If so, then the proof is immediate using the definition of 'adjoint'. As for your proof... If you start with a basis for W and extend to V, how can you know the basis you started with can be selected to have orthonormal eigenvectors if you haven't proved Tw is self-adjoint yet?
 
  • #3
Dick said:
Tw is the restriction of T to W, right? Which makes sense as an operator on W since W is invariant? If so, then the proof is immediate using the definition of 'adjoint'.
Would it be possible to elaborate a bit more? <T(v),w> = <v,T*(w)> for v,w in W is the def from what I remember but how is that immediate?

Dick said:
As for your proof... If you start with a basis for W and extend to V, how can you know the basis you started with can be selected to have orthonormal eigenvectors if you haven't proved Tw is self-adjoint yet?
I was kind of thinking the same thing but don't the basis vectors for Tw have to come from the basis for T?

Thanks
 
  • #4
genjuro911 said:
Would it be possible to elaborate a bit more? <T(v),w> = <v,T*(w)> for v,w in W is the def from what I remember but how is that immediate?


I was kind of thinking the same thing but don't the basis vectors for Tw have to come from the basis for T?

Thanks

Nooo. Take V=R^2. Take v=(1/sqrt(2),1/sqrt(2)). Take W to be the subspace t*v for real t. One orthonormal basis for V is e1=(0,1) and e2=(1,0). v isn't in that basis. A basis of a subspace isn't automatically a part of the basis of the containing space. You have to arrange it to be so. You are having a hard time seeing the obvious solution because it's, uh, obvious. That does make things hard to see.

T is self adjoint in V. So T=T* in V. Let Tw be the restriction of T to W. Which makes sense because T(W) is contained in W. So Tw:W->W. But W is contained in V. So if <v,T(w)>=<T(v),w> (T is self adjoint) for v,w in V, then <v,T(w)>=<T(v),w> for v,w in W. What does this tell you about the relation between Tw and (Tw)*?
 

What is a self-adjoint operator?

A self-adjoint operator is a mathematical concept in linear algebra that represents a special type of linear transformation on a vector space. It is a linear operator that is equal to its own adjoint, meaning that its matrix representation is equal to its transpose conjugate. This property has important implications in quantum mechanics and other areas of physics.

What is the significance of self-adjoint operators in quantum mechanics?

In quantum mechanics, self-adjoint operators play a crucial role in representing observable quantities such as position, momentum, and energy. They have real eigenvalues and orthogonal eigenvectors, making them useful for solving the Schrödinger equation and predicting the behavior of quantum systems.

How do you determine if an operator is self-adjoint?

To determine if an operator is self-adjoint, you can use the Hermitian conjugate or adjoint operation to calculate its adjoint. If the resulting matrix is equal to the original operator's matrix transpose, then it is self-adjoint. Additionally, self-adjoint operators must have real eigenvalues and orthogonal eigenvectors.

What are the properties of self-adjoint operators?

Aside from being equal to their own adjoint, self-adjoint operators have several important properties. They are Hermitian, meaning that their matrix representation is equal to its complex conjugate transpose. They also have real eigenvalues and orthogonal eigenvectors, as mentioned before. In addition, self-adjoint operators are unitary, meaning that their inverse is equal to their adjoint.

What are some real-world applications of self-adjoint operators?

Self-adjoint operators have applications in a variety of fields, including quantum mechanics, signal processing, and image processing. In quantum mechanics, they are used to represent observable quantities and predict the behavior of quantum systems. In signal and image processing, they are used for noise reduction and feature extraction. They also have applications in differential equations, optimization problems, and other areas of mathematics.

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