Relating to Duality in Vector Spaces

In summary, the author is asking for help understanding a theorem relating to vector spaces, and asks for feedback on whether or not he understands the question.
  • #1
mosenja
13
0
This subject came up in some notes on linear algebra I'm reading and I don't get it. Please help me understand.

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First, the relevant background and notation relating to my question:

Let S be a nonempty set and F be a field. Denote by l(S) the family of all F-valued functions on S and l_c(S) the family of all functions mapping S to F with finite support; that is, those functions on S which are nonzero at only finitely many elements of S.

Now let V be a vector space with (over F) with basis B. Define M : l_{c}(B) \rightarrow V by M(v) = sum_{e \in B}v(e)e.

Note that l(B) (under pointwise operations) is a vector space, l_c(B) is a vector subspace of l(B), and M is an isomorphism.

If we write v for M(v) we see that v = sum_{e \in B}v(e)e.

We will go further and use M to identify V with l_c(B) and write v = sum_{e \in B}v(e)e. That is, in a vector space with basis we will treat a vector as a scalar valued function on its basis.

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And now here is the question: according to the above, what is the value of f(e) when e and f are elements of the basis B? (And most importantly, why?)
 
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  • #2
Writing [tex]\hat{f}[/tex] for [tex]M^{-1}(f)[/tex]...

The relevant equation here is that
[tex]f = \sum_{e \in B} \hat{f}(e) e[/tex]

Knowing that B is a basis, what does that tell you?
 
  • #3
If [tex] f = \sum_{e \in B} \hat{f}(e)e [/tex] then since B is a basis we must have [tex] \hat{f}(e) = 0 [/tex] for all e in B.

Yes? No?
 
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  • #4
mosenja said:
If [tex] f = \sum_{e \in B} \hat{f}(e)e [/tex] then since B is a basis we must have [tex] \hat{f}(e) = 0 [/tex] for all e in B.

Yes? No?

Well, you can check yourself! What happens if you plug that in for [tex]\hat{f}[/tex]?
 
  • #5
Well if [tex] \hat{f}(e) = 0 [/tex] for all [tex] e \in B [/tex], I'm lead to unsatisfactory conclusion that [tex] f = 0 [/tex]. (Unsatisfactory since f is, by assumption, a member of the basis B; 0 is never a member of a linearly independent set.)

Hrmm... If we write the following:

[tex] f = (\sum_{e \in B - \{f\}}\hat{f}(e)e) + \hat{f}(e)f [/tex]

it seems clear that [tex] \hat{f}(e) = 0 [/tex] whenever [tex] e \in B - \{f\} [/tex] (that is [tex]e \neq f [/tex]) and [tex] \hat{f}(e) = 1 [/tex] whenever [tex] e = f [/tex]; more concisely: [tex] \hat{f}(e) = \delta_{ef} [/tex] (Kronecker delta).
 
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  • #6
That looks reasonable. That formula for [tex]\hat{f}(e)[/tex] is a well-defined function of both f and e, and [tex]M(\hat{f}) = f[/tex] as desired.

So, your question is settled, then?
 
  • #7
Incidentally, can you see how an element of lc(B) behaves as if it was a "linear combination in the elements of B"? And how M-1 acts as the function that "writes a vector as a linear combination of basis elements"?
 
  • #8
Hurkyl said:
Incidentally, can you see how an element of lc(B) behaves as if it was a "linear combination in the elements of B"? And how M-1 acts as the function that "writes a vector as a linear combination of basis elements"?

Not quite sure what you mean.

And actually I have another 'exercise' with which I need help; it appears to be related to the above question.

Let v be a nonzero vector in a vector space V and E be a basis for V which contains the vector v. Then there exists a linear functional [tex] \phi \in V^* [/tex] (the dual of V) such that [tex] \phi(v) = 1 [/tex] and [tex] \phi(e) = 0 [/tex] for every [tex] e \in E - \{v\} [/tex].

Now this function [tex] \phi [/tex] seems somewhat similar to the [tex] f, \hat{f} [/tex] about which we spoke of earlier. But I'm having trouble seeing it all fits together; in particular how [tex] l_c(B) [/tex] relates to the dual of V, and also how to define [tex] \phi [/tex].

By the way, in case you were wondering, the 'goal' of the section of the notes from which I'm drawing these questions is to prove what the author is calling the "Riesz-Fréchet theorem for vector spaces".
 

1. What is a vector space?

A vector space is a mathematical structure that consists of a set of vectors and two operations, vector addition and scalar multiplication. It is used to represent physical quantities that have both magnitude and direction, such as velocity and force.

2. What does duality mean in the context of vector spaces?

In the context of vector spaces, duality refers to the relationship between a vector space and its dual space. The dual space consists of all linear functionals on the original vector space, which map each vector to a scalar. This duality allows for the concept of vectors and linear functionals to be interchangeable.

3. How is duality related to linear transformations?

Duality is closely related to linear transformations as linear functionals can be viewed as a special type of linear transformation. In fact, the dual space of a vector space is isomorphic to the space of linear transformations from the vector space to its underlying field.

4. What is the significance of duality in mathematics?

Duality plays a significant role in mathematics as it allows for the simplification and generalization of concepts. It also provides a powerful tool for solving problems and proving theorems. Additionally, many mathematical structures and theories, such as linear algebra and functional analysis, rely heavily on the concept of duality.

5. How do you apply duality in real-world situations?

Duality has many practical applications in fields such as physics, engineering, and economics. For example, in physics, duality is used to describe the wave-particle duality of quantum mechanics. In engineering, it is used in signal processing and control systems. In economics, duality is used in optimization problems and game theory.

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