How can the adjoint of a linear transformation be related to inner products?

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In summary, the conversation discusses the concept of an adjoint operator and its definition in the context of linear algebra. It also touches on the use of inner products and Hilbert spaces. The main point is that the adjoint of a linear operator maps from the scalar field F to the original vector space V. The conversation also addresses some confusion about the notation and the use of inner products for scalars. The conversation ends with a suggestion to check the definition of inner product for the space F over itself.
  • #1
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Homework Statement



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The Attempt at a Solution



So I know that ##T^*## will be from F to V and we have a fixed scalar a in F. So :

##<u, T^*(a)> \space = \space <T(u), a> \space = \space <<u,v>,a>##

Now, I am unsure how to continue this. What should I do with <u,v>? I know that <u,v> is in F, but that doesn't seem to help me much?
 

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  • #2
Sorry for the double, but I stared at this for a second and thought of something.

##<<u,v>, a> \\
= < u_1 \overline{v_1} + ... + u_n \overline{v_n}, a> \\
= < u_1 \overline{v_1}, a> + ... + < u_n \overline{v_n}, a>##

I'm not quite sure this helps either though.
 
  • #3
You know that ##<u,v>## and ##a## are scalars. So what is ##<<u,v>,a>##? What is the inner product in ##\mathbb{R}##?
 
  • #4
micromass said:
You know that ##<u,v>## and ##a## are scalars. So what is ##<<u,v>,a>##? What is the inner product in ##\mathbb{R}##?

Hmm... ##<<u,v>,a> = a<<u,v>,1>\space##

its just a real number when we work over ##ℝ## I believe.
 
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  • #5
I don't get this whole question. V is a vector space. It doesn't say that it is F or R. If T maps V to F. Then T* doesn't map F to V. It maps the dual space V* to F. It doesn't act on scalars.

I'm confused. Or is there some interpretation of adjoint I don't know.
 
  • #6
Dick said:
I don't get this whole question. V is a vector space. It doesn't say that it is F or R. If T maps V to F. Then T* doesn't map F to V. It maps the dual space V* to F. It doesn't act on scalars.

I'm confused. Or is there some interpretation of adjoint I don't know.
Suppose that H and K are Hilbert spaces over F, where F is either ℝ or ℂ. I will use the convention that when F=ℂ, the inner product is linear in the second variable. Suppose that ##T:H\to K## is linear and bounded. Let ##x\in K## be arbitrary. Define ##\phi_x:H\to F## by ##\phi_x(y)=\langle x,Ty\rangle_K## for all ##y\in H##. This map is linear and bounded, so ##\phi_x\in H^*##. The Riesz representation theorem for Hilbert spaces tells us that this implies that there's a unique ##z\in H## such that ##\phi_x=\langle z,\cdot\rangle_H##. (The right-hand side denotes the map that takes an arbitrary ##w\in H## to ##\langle z,w\rangle_H##). The map ##x\mapsto z## is denoted by ##T^*## and is called the adjoint of T. Note that ##x\in K## and ##z\in H##, so ##T^*:K\to H##.

Since ##\phi_x=\langle T^*x,\cdot\rangle_H##, we have
$$\langle T^*x,y\rangle_H =\phi_x(y) =\langle x,Ty\rangle_K$$ for all ##y\in H##.

The field of scalars F can be interpreted as a Hilbert space over F, with addition being the field's addition operation, and both scalar multiplication and the inner product being the field's multiplication operation. So the definition of "adjoint" above implies that if ##T:V\to F##, then ##T^*:F\to V##.
 
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  • #7
Dick said:
I don't get this whole question. V is a vector space. It doesn't say that it is F or R. If T maps V to F. Then T* doesn't map F to V. It maps the dual space V* to F. It doesn't act on scalars.

I'm confused. Or is there some interpretation of adjoint I don't know.

This is from the book linear algebra done right. So throughout the course of the chapter, V denotes a finite dimensional inner product space over F.

I was confused about it acting on scalars as well.
 
  • #8
Fredrik said:
The field of scalars F can be interpreted as a Hilbert space over F, with addition being the field's addition operation, and both scalar multiplication and the inner product being the field's multiplication operation. So the definition of "adjoint" above implies that if ##T:V\to F##, then ##T^*:F\to V##.

Certainly. The only problem I have with that is that the problem doesn't say that V is F regarded as a vector space over itself. Unless this is in the context of other problems which say it is. And it's pretty sloppy to say ##T^*:F\to V## in that case. V and F have the same elements but V is F regarded as a vector space and F is a field. To say ##T^*:F\to F## is less sloppy. I don't see why you would want to reverse the symbols F and V.
 
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  • #9
Dick said:
Certainly. The only problem I have with that is that the problem doesn't say that V is F regarded as a vector space over itself. Unless this is in the context of other problems which say it is. And it's pretty sloppy to say ##T^*:F\to V## in that case. V and F have the same elements but V is F regarded as a vector space and F is a field. To say ##T^*:F\to F## is less sloppy. I don't see why you would want to reverse the symbols F and V.
V can be any Hilbert space over F in this problem. It doesn't have to be equal to F.
 
  • #10
Fredrik said:
V can be any Hilbert space over F in this problem. It doesn't have to be equal to F.

I don't believe we've covered Hilbert Spaces yet, so I don't think I can use anything pertaining to it.

V is an inner product space over a field F, but I don't think I've ever seen an inner product for scalars only which is why I'm having so much trouble manipulating the equation in my first post.
 
  • #11
Zondrina said:
I don't believe we've covered Hilbert Spaces yet, so I don't think I can use anything pertaining to it.

V is an inner product space over a field F, but I don't think I've ever seen an inner product for scalars only which is why I'm having so much trouble manipulating the equation in my first post.

The space ##\mathbb{C}## is always an inner-product space over itself. The inner product is given by ##<x,y> = \overline{x}y##. You should check that this is indeed an inner product.
What will the inner product be of the space ##\mathbb{R}## over itself?
 
  • #12
micromass said:
The space ##\mathbb{C}## is always an inner-product space over itself. The inner product is given by ##<x,y> = \overline{x}y##. You should check that this is indeed an inner product.
What will the inner product be of the space ##\mathbb{R}## over itself?

It's the same as the complex one, except complex conjugation is not needed.

<x,y> = xy
 
  • #13
Right! So the product in ##\mathbb{R}## is a special case of the inproduct in an inner-product space! It's good to keep this in mind!

Can you solve the exercise now?
 
  • #14
##<u, T^*(a)> \space = \space <T(u), a> \space = \space <<u,v>,a> = \space <u \overline{v},a> = u \overline{va}##

Still can't see where this is going.
 
  • #15
No! You can't say ##<u,v> = \overline{u}v##! That is only true in the special case ##V=\mathbb{C}##. But your ##u## and ##v## are in a general space, so you can't use that expression.

The only thing you can conclude is

[tex]<u,T*(a)> = \overline{a}<u,v>[/tex]

Now, can you use this to write ##T*(a)## in another form?? That is, do you know some other vector ##x## such that

[tex]<u,x> = \overline{a} <u,v>[/tex]
 
  • #16
Zondrina said:
I don't believe we've covered Hilbert Spaces yet, so I don't think I can use anything pertaining to it.
Every finite-dimensional inner product space is a Hilbert space, so everything I said holds for this V as well. One thing is easier when we're dealing with a finite-dimensional space: We don't have to use the Riesz representation theorem.

Note however that what I wrote was an explanation meant for Dick. It may be useful for you too, but I was just talking about the definition of the adjoint in general. I haven't said anything about this specific problem.
 
  • #17
I just noticed my first post was wrong by a bit. I'll start over here and see what happens.

Okay so ##T : V → F \Rightarrow T^*: F → V##. Fix a scalar ##a \in F## and we know ##v \in V##. Also ##T(u) = <u,v>##.

##<v, T^*(a)> \space = \space <T(v), a> \space = \space <<v,v>,a> \space##

That's better. I don't know if V is ℝ or ℂ in particular, we're supposed to assume it can be ℂ unless stated otherwise I believe.

So ##<<v,v>,a> = \overline{a}<<v,v>,1> = \overline{a}<v,v> = <v, av> = <v, T^*(a)>##

##∴T^*(a) = av##

EDIT : If anyone sees this, I believe this is a Linear functional?
 
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  • #18
<v,T*a>=<v,av> doesn't imply that T*a=av. Can you think of something you can do just a little bit differently that will allow you to conclude that T*a=av at the end of the calculation? (That is the correct formula for T*a).

A linear functional is a linear transformation into the field of scalars. Since the codomain of T* is V, not F, it's not a linear functional. It's just a linear transformation.
 
  • #19
Fredrik said:
<v,T*a>=<v,av> doesn't imply that T*a=av. Can you think of something you can do just a little bit differently that will allow you to conclude that T*a=av at the end of the calculation? (That is the correct formula for T*a).

This confused me, how does that not imply it? What could I have possibly done differently to arrive at the general formula for ##T^*(a)##?
 
  • #20
Take the vector space ##\mathbb R^2## for example. We have $$\langle (1,0),(2,0)\rangle=2 =\langle (1,0),(2,5)\rangle$$ but ##(2,0)\neq (2,5)##. So it's not always true that if ##\langle x,y\rangle=\langle x,z\rangle##, then ##y=z##.

Also note that for all w such that <w,v>=0, we have <v,T*a+w>=<v,T*a>=<v,av>. This observation should be enough to cast some doubt on your derivation of T*a=av.
 
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  • #21
Fredrik said:
Take the vector space ##\mathbb R^2## for example. We have $$\langle (1,0),(2,0)\rangle=2 =\langle (1,0),(2,5)\rangle$$ but ##(2,0)\neq (2,5)##. So it's not always true that if ##\langle x,y\rangle=\langle x,z\rangle##, then ##y=z##.

Also note that for all w such that <w,v>=0, we have <v,T*a+w>=<v,T*a>=<v,av>. This observation should be enough to cast some doubt on your derivation of T*a=av.

I understand what you're saying, what I still don't seem to understand is why after the manipulation I can't conclude that T*(a) = av? I understand there are counterexamples... does that mean I have to restrict something?
 
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  • #22
Since <v,av> is equal to both <v,T*a> and <v,T*a+w>, why should the conclusion be T*a=av and not T*a=av+w with w≠0?

You need to go back to the beginning. ##v## is some specific member of ##V##, which is used to define ##T##. ##T:V\to F## is defined by ##Tu=\langle u,v\rangle_V## for all ##u\in V##. This map is linear because you're using the convention that if V is an inner product space over ℂ, then the inner product ##\langle,\rangle_V## is linear in the first variable. (Physicists like to make it linear in the second variable instead).

It's true that this implies that for all ##a\in F##, we have
$$\langle v,T^*a\rangle_V =\langle Tv,a\rangle_F =(Tv)a^* =\langle v,v\rangle_V a^* =\langle v,av\rangle_V.$$ But is this really all that the assumptions imply? Did you really have to insert the specific vector ##v## in the first slot of the inner product of the left? What if you insert something else there?
 
  • #23
Fredrik said:
Since <v,av> is equal to both <v,T*a> and <v,T*a+w>, why should the conclusion be T*a=av and not T*a=av+w with w≠0?

You need to go back to the beginning. ##v## is some specific member of ##V##, which is used to define ##T##. ##T:V\to F## is defined by ##Tu=\langle u,v\rangle_V## for all ##u\in V##. This map is linear because you're using the convention that if V is an inner product space over ℂ, then the inner product ##\langle,\rangle_V## is linear in the first variable. (Physicists like to make it linear in the second variable instead).

It's true that this implies that for all ##a\in F##, we have
$$\langle v,T^*a\rangle_V =\langle Tv,a\rangle_F =(Tv)a^* =\langle v,v\rangle_V a^* =\langle v,av\rangle_V.$$ But is this really all that the assumptions imply? Did you really have to insert the specific vector ##v## in the first slot of the inner product of the left? What if you insert something else there?

Well... if i insert ##u + w## then :

$$\langle u+w,T^*(a) \rangle_V =\langle T(u+w),a\rangle_F =(T(u) + T(w))a^* =(\langle u,v\rangle + \langle w,v \rangle)_V a^* =(\langle u,av\rangle + \langle w,av\rangle)_V.$$
 
  • #24
Right. The right-hand side is equal to ##\langle u+w,av\rangle_V##, and you didn't make any assumptions about u or w. So it seems that no matter what vector you put in the first slot of the inner product that you start with, you still get a similar result. Is there any way you can use that to your advantage?
 

1. What is an inner product in mathematics?

An inner product is a mathematical operation that takes two vectors and produces a scalar value. It is a generalization of the familiar dot product in three-dimensional space. The inner product is used to define the geometric concept of angle and to measure the length of a vector in a vector space.

2. What is the importance of inner products in mathematics?

Inner products are important in mathematics because they allow us to define notions of length and angle in more abstract vector spaces. They also provide a way to generalize the Pythagorean theorem and the concept of orthogonality to higher dimensions and more abstract settings.

3. How are inner products related to adjoints?

The adjoint of a linear operator is a generalization of the transpose of a matrix. It is defined in terms of the inner product and allows us to define notions of orthogonality and perpendicularity in more abstract spaces. The adjoint also plays an important role in many applications, such as in quantum mechanics and signal processing.

4. What is the difference between an inner product and a norm?

An inner product is a mathematical operation that takes two vectors and produces a scalar, while a norm is a function that assigns a non-negative value to a vector. Inner products are used to define norms, as the norm of a vector is the square root of its inner product with itself. However, norms can also be defined without using inner products.

5. How are inner products and adjoints used in practical applications?

Inner products and adjoints are used in many practical applications, such as in physics, engineering, and data analysis. In physics, inner products are used to calculate the probability of finding a quantum particle in a certain state, while adjoints are used to describe the time evolution of quantum systems. In engineering, inner products and adjoints are used in signal processing and control systems. In data analysis, they are used in machine learning algorithms and data compression techniques.

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