Adjoint of Inner Product Space Question

In summary, the student is trying to solve a homework problem, but isn't getting anywhere because they don't understand what a pre-Hilbert space is or how to prove a theorem.
  • #1
PuzzledMath
2
0

Homework Statement



Suppose T is in the set of linear transformations on an inner product space V and lamba is in F, the scalar field of V. Prove that lamda is an eigenvalue of T iff lamda_bar (the conjugate) is an eigenvalue of T*, the adjoint of T.

Homework Equations



Using [] brackets to denote an inner product:
The definition of the adjoint is
[Tv, w] = [v, T*w ] for all v, w in V
Some Properties of the inner product:
[v, v] >/= 0 for all v in V
[v, v] = 0 iff v = O
[v + u, w] = [v, w] + [u, w]
[av, w] = a [v, w]
[v, aw] = a_bar [v, w]
Some Properties of the Adjoint:
(T*)* = T
(aT)* = a_bar T*

The Attempt at a Solution


I've been working on the first direction of the iff statement.

We know Tx = lamda x for some x in V
Then
[Tx, w]
= [ x, T*w]
= [ lamda x, w]
= [ x, lamda_bar w] using conjugate linearity property
This is for any w in V.

Then [ x, T*w - lamda_bar w ] = 0 for any w in V.

But how can I get the conclusion that T*v = lamda_bar v for some v in V?
I've tried letting w = x, but it doesn't seem to help.
I've also tried starting with something like
[Tw, lamda x]
= [w, lamda_bar T*x]
= [Tw, Tx ]
= [w, T*(Tx)]
but I don't know what T*(Tx) is, so that doesn't help me.

My professor isn't answering email over spring break, and this is frustrating me. I keep trying and don't get anywhere. Any advice would be greatly appreciated! Thanks.
 
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  • #2
Allright, have you seen the following theorem:

If X and Y are pre-Hilbert spaces and if [tex]T:X\rightarrow Y[/tex] is a bounded linear operator. If [tex]<T(x),y>=0[/tex] for all x in X and y in Y, then T=0.

Try to prove this first. Then you can apply this theorem to the space

[tex]X=Y=span\{x\}[/tex]

where x is your eigenvector.
 
  • #3
I don't have any theorem like that - I don't even know what a pre-Hilbert space is. My professor said it was a very simple, short proof with applications of definition and properties we have, but I can't figure it out. I've listed most of the information I have to work with, but I can't make it do what it's supposed to : (
 

What is an inner product space?

An inner product space is a mathematical concept that refers to a vector space with an additional operation called an inner product. This operation takes in two vectors and returns a scalar value, satisfying certain properties such as linearity, symmetry, and positive definiteness.

What is the adjoint of an inner product space?

The adjoint of an inner product space is a linear map that takes in one vector and returns another vector in the same space. It is defined by the inner product, and it has the property that the inner product of the adjoint of a vector with another vector is equal to the inner product of the two vectors. In other words, it preserves the inner product.

What is the significance of the adjoint in an inner product space?

The adjoint is significant in an inner product space because it allows us to generalize concepts such as orthogonality and distance in vector spaces. It also helps us solve problems involving projections and least squares approximations.

How is the adjoint of an inner product space calculated?

The adjoint of an inner product space is calculated by taking the transpose of the matrix representation of the original linear map and then taking the conjugate of each element in the matrix. This results in a new matrix that represents the adjoint of the original map.

What are some applications of the adjoint in physics and engineering?

The adjoint of an inner product space has various applications in physics and engineering, such as in quantum mechanics, signal processing, and control theory. It is used to solve problems in quantum mechanics involving wave functions and operators. In signal processing, it is used for filtering and spectral analysis. In control theory, it is used for system identification and model reduction.

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