What makes positive linear functionals always finite?

In summary, Rudin's proof shows that there exists a measure u() that represents A: for every f in Cc(X), u(f) is finite. However, since each A(Vi) is finite, u(K) is finite for any compact K.
  • #1
redrzewski
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I'm strugging with a portion of Rudin's proof.

Quick statement of the bulk of the theorem:

Let X be a locally compact Hausdorff space. Let A be a positive linear functional on Cc(X) (continous functions with compact support). Then (among other things), there exists a measure u() that represents A:

A(f) = Integral(fdu) for every f in Cc(X).

Now assuming that, he shows that u(K) for any compact set K is finite.

He basically shows that u(K) <= A(f) for some f in Cc(X) with 0 <= f <= 1. This far I follow. But then he immediately concludes that therefore u(K) is finite for any compact K.

Is there some basic property of positive linear functionals that makes them always finite? There is a somewhat similar proof in Rudin's Principles of Mathematical Analysis where he shows that the norm of linear functionals on finite dimensional vector spaces is finite. But that proof assumes a finite dimensional space. So I can't see how to apply that proof here.

Any help is appreciated.
thanks
 
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  • #2
I can use the compactness of K to choose an finite open cover {Vi} of K. Then we have:

A(V1) + A(V2) = A(V1+V2), etc by linearity. Showing that this is >= u(K) is straightforward with the countable additivity of the measure. Since there are finite Vi, I get a finite sum very similar to Rudin's proof on the finite vector space in PMA.

However, I'm still assuming that each A(Vi) is finite. But it seems like I could be on the right track. What's the justification that each A(Vi) is finite?

thanks
 
  • #3
redrzewski said:
He basically shows that u(K) <= A(f) for some f in Cc(X) with 0 <= f <= 1. This far I follow. But then he immediately concludes that therefore u(K) is finite for any compact K.

isn't this obvious? A(f) must be finite by the definition of a linear function, so u(K) <= A(f)< infinity.
 
  • #4
Let me make sure I understand.

Since A: X -> R is a function, then for any f in X, A(f) must be finite by the definition of a function since A is well defined on its domain.

Is that right?

Also, Royden has a section about extended real-valued functions where apparently f(x)=infinity for some x is a valid definition. So I just need to assume that we aren't dealing with these extended functions here.

thanks
 
  • #5
redrzewski said:
Since A: X -> R is a function, then for any f in X, A(f) must be finite by the definition of a function since A is well defined on its domain.

Yes, except that it is A:Cc(X)->R and f in Cc(X). The Riesz representation theorem is for real valued linear maps.
Although, u(f) could be infinite if f>=0 doesn't have compact support but then it isn't in the domain of A anyway.
 

1. What is the Riesz representation theorem?

The Riesz representation theorem is a fundamental result in functional analysis that establishes a one-to-one correspondence between certain types of continuous linear functionals and elements of a Hilbert space. It states that every continuous linear functional on a Hilbert space can be represented as an inner product with a unique vector in the space.

2. What is a Hilbert space?

A Hilbert space is a complete vector space equipped with an inner product. It is a generalization of Euclidean space and is commonly used in functional analysis and quantum mechanics.

3. Can you provide an example of the Riesz representation theorem in action?

One example of the Riesz representation theorem is the representation of a continuous linear functional on the space of square-integrable functions on a given interval. In this case, the inner product of the functional with a function in the space gives the integral of the product of the two functions. This allows for the representation of the functional as a function in the space itself.

4. How is the Riesz representation theorem used in real-world applications?

The Riesz representation theorem has numerous applications in mathematics and physics. It is used in the study of partial differential equations, quantum mechanics, and signal processing, among others. It also has applications in data analysis and machine learning, where it can be used to represent data points as vectors in a Hilbert space.

5. Is the Riesz representation theorem limited to Hilbert spaces?

No, the Riesz representation theorem can be extended to other types of topological vector spaces, such as Banach spaces. However, the version of the theorem for Hilbert spaces is the most well-known and commonly used in mathematics and physics.

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