Find an example of a linear functional with some properties

In summary, a linear functional is a specific type of linear map from a vector space to its field of scalars. An example of a linear functional is the function \( f: \mathbb{R}^n \to \mathbb{R} \) defined by \( f(x) = a_1x_1 + a_2x_2 + \ldots + a_nx_n \) where \( a_1, a_2, \ldots, a_n \) are constants. This functional is linear because it satisfies the properties of additivity \( f(x+y) = f(x) + f(y) \) and homogeneity \( f(cx) = cf(x) \) for any scalar \(
  • #1
docnet
Gold Member
799
486
Homework Statement
Let ##X## be a Banach space and ##X^\ast## its dual space. Find an ##f\in X^\ast## such that $$\sup_{||x||\leq 1}||f||=1$$ and ##f(x)\neq 1## whenever ##||x||=1##.
Relevant Equations
.
I considered ##X=\mathbb{R}^n## and quickly realized any linear functional like ##f=a_1x_1+\cdots a_nx_n## would attain a maximum on the boundary. I regret to say that my knowledge of topology is still very limited, and did a lot of experimenting with a pen and paper without fruitful results. And I did some researching to find a possible example.

Would ##X=l^1## and ##f=(0.9, 0.99, 0.999, \cdots ) \in l^\infty## work? It says that no element in ##X## would give ##f(x) = \sum_i f_i x_i = 1##, and ##||f||=1## in the sup norm. My understanding is still hazy and isn't quite clear to me what about makes this all work, and obviously I have a lot of new learning to do.

Thank you (excited to learn another challenging math topic).
 
  • Like
Likes nuuskur
Physics news on Phys.org
  • #2
This is a corollary of Hahn-Banach (cf. Prop 6.6, for example). We can fix a proper closed subspace ##X_0\subseteq X##, then there exists ##x^*## with norm ##1## such that it vanishes on ##X_0##. So if ##S_X\subseteq X_0##, the requirement of ##x^*(x)\neq 1## is met for all ##x\in S_X##.

Your condition should read
[tex]
\sup _{\|x\|\leqslant 1} \|f(x)\| = 1. \quad (\text{or } |f(x)|)
[/tex]
The LHS expression is precisely the norm of ##f## and it suffices to consider the supremum over the unit sphere.
 
Last edited:
  • Like
  • Informative
Likes WWGD and docnet
  • #3
nuuskur said:
This is a corollary of Hahn-Banach (cf. Prop 6.6, for example). We can fix a proper closed subspace ##X_0\subseteq X##, then there exists ##x^*## with norm ##1## such that it vanishes on ##X_0##. So if ##S_X\subseteq X_0##, the requirement of ##x^*(x)\neq 1## is met for all ##x\in S_X##.
This might be an obvious question, but if ##X_0## corresponds to the closed unit ball, and ##x^*## vanishes on ##X_0##, i.e., ##x^*(x)=0## for all ##x\in X_0##, isn't it a contradiction to ##\sup_{||x||\leq 1}x*(x)=1##?
 
  • Like
Likes nuuskur
  • #4
Do you distinguish here between the Continuous dual and the Algebraic dual?
 
  • Like
Likes docnet
  • #5
WWGD said:
Do you distinguish here between the Continuous dual and the Algebraic dual?
I don't know much about the distinction between the two types of duals, but I assume it's the case where ##f## is a linear functional in the continuous dual. If ##f## is allowed to be discontinuous, would it be more trivial to find a discontinuous linear functional to satisfy ##||f||=1## and ##f(x)\neq 1## with ##||x||=1##?
 
  • #6
Might be mechanical thinking on my part - whenever I see Banach spaces and duals mentioned, I switch my brain to "Hahn-Banach mode" and assume we are talking about topological dual (continuous linear functionals).

If it is discontinuous, then you find your subspace where something behaves the way you want and then extend its basis to a basis on the entire space.

docnet said:
This might be an obvious question, but if ##X_0## corresponds to the closed unit ball, and ##x^*## vanishes on ##X_0##, i.e., ##x^*(x)=0## for all ##x\in X_0##, isn't it a contradiction to ##\sup_{||x||\leq 1}x*(x)=1##?
Careful, ##X_0## is a closed vector subspace. In the one dimensional case, there is no proper nonzero example. As soon as ##0\neq a\in X_0## we have ##X_0=\mathbb R##. In multidimensional case, the proper subspaces could correspond to lines and (hyper)planes, for instance.

edit: Whenever a subspace contains the unit ball, it is the entire space ##X##, simply because ##X## is a countable union of scaled unit balls.

I'm not 100% sure right now what happens when ##X_0## only contains the unit sphere. I think it's still the entire space because we can express any ##x\in X## as ##\lambda z\in \lambda S_X## for some ##\lambda##.

What a clever question!
 
Last edited:
  • Informative
Likes docnet
  • #7
Here's another idea. A linear map ##f:X\to Y## between normed spaces is continuous, surjective and has unit norm if ##f## maps the unit sphere to the unit sphere. So, can we have a functional that maps ##S_X## to ##-1## or ##\{ \exp (it)\}\setminus \{1\}##, (depending on whether it's over ##\mathbb R## or ##\mathbb C##)?
 
  • Like
Likes docnet
  • #8
Yes, I guess you're right. It's most likely the continuous dual.
 
  • #9
We'd be hard pressed to find such an example from #7 over ##\mathbb R## due to linearity: if ##f(x)=-1##, then ##f(-x)=-f(x)=1## for any ##x\in S_X##.
 

FAQ: Find an example of a linear functional with some properties

What is a linear functional?

A linear functional is a specific type of linear map that takes a vector from a vector space and returns a scalar. Formally, if V is a vector space over a field F, a linear functional is a function f: V → F that satisfies two properties for all vectors u, v in V and all scalars c in F: f(u + v) = f(u) + f(v) and f(cu) = cf(u).

Can you provide an example of a linear functional?

One simple example of a linear functional is the function f: R² → R defined by f(x, y) = 3x + 4y. This function takes a vector (x, y) in R² and returns a scalar value calculated as 3 times the first component plus 4 times the second component. It satisfies the properties of linearity.

What are the properties of a linear functional?

The main properties of a linear functional are linearity, continuity (if the vector space is finite-dimensional), and boundedness. Specifically, linearity means it adheres to the rules of additivity and homogeneity. Additionally, in finite-dimensional spaces, every linear functional is continuous, which is an important aspect when considering topological vector spaces.

How do you determine if a function is a linear functional?

To determine if a function is a linear functional, you need to check if it satisfies the two linearity properties: additivity (f(u + v) = f(u) + f(v)) and homogeneity (f(cu) = cf(u)) for all vectors u, v in the vector space and all scalars c. If both properties hold, then the function qualifies as a linear functional.

What is the dual space in relation to linear functionals?

The dual space of a vector space V, denoted V*, is the set of all linear functionals defined on V. Each element of the dual space is a linear functional that maps vectors from V to scalars in the underlying field. The dual space plays a crucial role in functional analysis and provides insight into the structure of the original vector space.

Back
Top