Linear functional clarification (from rudin)

In summary: None of that is required to complete Rudin's argument, though. In his case, what one can do is simply let gamma=sup Gamma(A). All one needs to prove is that gamma is not in the image of A. Assume that it is in the image of A; that is, gamma=Gamma(a) for some a in A.Then gamma is in the image of A, so it is not a solution to A.
  • #1
redrzewski
117
0
In Rudin's Functional Analysis (in theorem 3.4), he says:

"every nonconstant linear functional on X is an open mapping". X is topological vector space.

This seems like a strengthening of the open mapping theorem, which requires X to be an F-Space, and that the linear functional to be continuous.

Indeed, it seems like a discontinuous linear functional from R to R as described in Gelbaum for instance isn't open.

What am I missing?
thanks
 
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  • #2
There is no discontinuous linear functional R to R. Linear is in the sense of real scalars.
 
  • #3
Are you sure? "Counterexamples in Analysis" by Gelbaum p.33 gives an example.
 
  • #4
p. 33 ... compare it carefully to what you said before.
 
  • #5
This isn't homework. Can you be more direct?

As far as I see, the function defined by Gelbaum is a discontinuous linear functional R to R.
 
  • #6
I think the problem is that Gelbaum's construction is using a different definition of linear. It seems Gelbaum's "linear" doesn't also imply f(a*x) = a*f(x) for scalar a. So that resolves some of my confusion. Does this sound accurate?

That still leaves my original question, though:

Is "every nonconstant linear functional on X is an open mapping"? Even assuming that the functional is continuous, do we really not need the stronger condition that X is an F-space?
 
  • #7
We do not need [itex]F[/itex]-space and we do not need continuous. A linear functional [itex]f[/itex] on a TVS [itex]X[/itex] can be discontinuous. But the point of the "hints" I gave before is that when you restrict [itex]f[/itex] to a line (say a line [itex]L = \{tu : t \in \mathbb{R}\}[/itex] through the origin and the point [itex]u[/itex]), that restriction is continuous, in fact it is of the form [itex]f(tu) = tc, t \in \mathbb{R}[/itex], for some constant [itex]c[/itex]. And (more to the point) it maps open intervals in [itex]L[/itex] to open intervals in [itex]\mathbb{R}[/itex].

We do have to assume [itex]f[/itex] is not the zero linear functional!

We claim [itex]f[/itex] is an open map. Let [itex]U \subset X[/itex] be an open set. We must show the image [itex]f(U)[/itex] is open in [itex]\mathbb{R}[/itex]. Let [itex]u \in U[/itex] . We claim that [itex]f(U)[/itex] contains an interval centered at [itex]f(u)[/itex]. This is because of the restriction property mentioned above. OK?

(Strictly speaking, we still have to adjust something when [itex]f(u) = 0[/itex].)
 
  • #8
g_edgar said:
We do not need [itex]F[/itex]-space and we do not need continuous. A linear functional [itex]f[/itex] on a TVS [itex]X[/itex] can be discontinuous. But the point of the "hints" I gave before is that when you restrict [itex]f[/itex] to a line (say a line [itex]L = \{tu : t \in \mathbb{R}\}[/itex] through the origin and the point [itex]u[/itex]), that restriction is continuous, in fact it is of the form [itex]f(tu) = tc, t \in \mathbb{R}[/itex], for some constant [itex]c[/itex]. And (more to the point) it maps open intervals in [itex]L[/itex] to open intervals in [itex]\mathbb{R}[/itex].

We do have to assume [itex]f[/itex] is not the zero linear functional!

We claim [itex]f[/itex] is an open map. Let [itex]U \subset X[/itex] be an open set. We must show the image [itex]f(U)[/itex] is open in [itex]\mathbb{R}[/itex]. Let [itex]u \in U[/itex] . We claim that [itex]f(U)[/itex] contains an interval centered at [itex]f(u)[/itex]. This is because of the restriction property mentioned above. OK?

(Strictly speaking, we still have to adjust something when [itex]f(u) = 0[/itex].)

Your post reminds me of the example of a linear functional from R to R when R is viewed as a vector space over the rationals.

It is easy to construct a discontinuous one from a Q basis.

Problem: Show that such the graph such a discontinuous linear functional is dense in the plane.
 
  • #9
Thanks, g_edgar. That clears things up.

wofsy: that's the same construction that Gelbaum is citing. Am I correct that that construction doesn't have the property that: f(a*x) = a*f(x) for scalar a?
 
  • #10
redrzewski said:
Thanks, g_edgar. That clears things up.

wofsy: that's the same construction that Gelbaum is citing. Am I correct that that construction doesn't have the property that: f(a*x) = a*f(x) for scalar a?

if a is a rational number, then f(a*x) = a*f(x).

The problem has more parts to it. Show that if f is measurable then it is continuous. If f is continuous then it is just multiplication by a scalar.
What does that tell you about the measurability of a basis for the reals over the rationals?
 
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  • #11
I don't see the "adjust something" of g_edgar very clearly (for the case f(u)=0).

None of that is required to complete Rudin's argument, though. In his case, what one can do is simply let gamma=sup Gamma(A). All one needs to prove is that gamma is not in the image of A. Assume that it is in the image of A; that is, gamma=Gamma(a) for some a in A. Then, as A is open, one can find a balanced neighbourhood U of 0 with a+U in A. If u in U, then gamma >= Gamma(a+u)=gamma+Gamma(u), so we conclude that Gamma(u)<=0. But, as U is balanced, this argument should also work for -u, and that gives us a contradiction. The conclusion is that gamma is not in the image of A, and so Gamma(a)<gamma for all a in A.
 

Related to Linear functional clarification (from rudin)

What is linear functional clarification?

Linear functional clarification, also known as linear functional analysis, is a branch of mathematics that studies vector spaces and linear transformations between them. It involves the use of concepts such as norms, duality, and operators to analyze and solve problems in various fields, including physics, engineering, and economics.

What are the main applications of linear functional clarification?

Linear functional clarification has many practical applications, including in optimization, control theory, signal processing, and data analysis. It is also used in the development of mathematical models in various scientific fields, such as quantum mechanics and fluid dynamics.

What are some key concepts in linear functional clarification?

Some key concepts in linear functional clarification include vector spaces, linear transformations, norms, duality, and operators. These concepts are used to describe and analyze the behavior of linear systems and to find solutions to equations and optimization problems.

What are the differences between linear functional clarification and other branches of mathematics?

Linear functional clarification is closely related to other branches of mathematics, such as linear algebra, calculus, and functional analysis. However, it is unique in its focus on vector spaces and linear transformations, and it often uses more abstract and general techniques to solve problems.

What are some recommended resources for learning about linear functional clarification?

Some recommended resources for learning about linear functional clarification include textbooks such as "Principles of Functional Analysis" by Martin Schechter and "Functional Analysis" by Walter Rudin. Online resources such as lecture notes and video lectures from universities can also be helpful for gaining a deeper understanding of the subject.

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