Prove or disprove about Dual space

In summary, we are asked to prove or disprove that the dual space of a closed subspace of a Banach space is a subset of the dual space of the original Banach space. To do so, we need to show that a bounded linear functional on the original space is also bounded on the closed subspace, or find a contradiction. Using the definition of continuity, we can see that a bounded linear functional on a normed space is discontinuous if it maps an open ball to the set of real numbers. However, we cannot find such a contradiction, suggesting that X* ⊆ Z* is true.
  • #1
TZenith
1
0
1. X : Banach space

Z : closed subspace of X

Prove or disprove that X* ⊆ Z*

where Z* and X* are dual space of Z and X, respectively.

2. X : normed space and f : X → R : linear functional.

Assume that ∃a∈X and r∈(0,1] such that f(B(a,r))=R(Real numbers)

where B(a,r) is open ball. Prove that f is discontinuous

Proof. 2) I think it must suppose f is continuous and find a contradiction with f(B(a,r))=R

But I can't find some contradiction.

1) We think it doesn't satisfy X* ⊆ Z*. But we will show X* ⊆ Z* and find some contradiction. Let f ∈ X*. Thus f : X → R is bounded linear functional. We must to show that f : Z → R is bounded linear functional or find some contradiction.
 
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  • #2
You have that [itex] Z \subseteq X [/itex] right? Linear functionals must be linear maps into [itex] \mathbb R [/itex] for all elements on which they act. A way of thinking about this is that the bigger a space is, the more restrictions are imposed on the dual functions, hence we would suspect that the dual spaces reverse inclusion!

Now check this. As you started, let [itex] f \in X^* [/itex] so that [itex] f^*: X \to \mathbb R [/itex] is linear and bounded. You want to know how it acts on a closed subspace of X. So first of all, could the function fail to be linear? Secondly, could the function fail to be bounded?

For the second question, think about the definition of continuity. The function f is continuous at a if for all [itex] \epsilon >0 [/itex] there exists [itex] \delta >0 [/itex] such that [itex] f(B(a,\delta)) \subseteq B(f(a),\epsilon). [/itex] Could you function be continuous at 'a'? Why or why not?
 

1. What is meant by "Dual space" in mathematics?

The dual space, also known as the algebraic dual or the algebraic conjugate space, is a mathematical concept that refers to the set of all linear functionals on a vector space. It is denoted by V* and is defined as the set of all linear maps from V to its underlying field, which is typically the real or complex numbers.

2. How is the dual space related to the original vector space?

The dual space is closely related to the original vector space, as it is formed by taking the set of all linear functionals on the vector space. This means that every vector in the original space has a corresponding linear functional in the dual space. Additionally, the dimension of the dual space is equal to the dimension of the original space.

3. Can you give an example of a dual space?

One example of a dual space is the space of continuous real-valued functions on a closed interval [a,b] with the inner product defined as the integral of the product of two functions. The dual space of this vector space would be the set of all linear functionals on this space, which can be represented as the set of all integrals of the product of a function and a continuous real-valued function on [a,b].

4. How can we prove or disprove the existence of a dual space for a given vector space?

The existence of a dual space for a given vector space can be proven by showing that the set of all linear functionals on the vector space satisfies the necessary properties, such as being closed under scalar multiplication and addition. On the other hand, to disprove the existence of a dual space, one would need to find a counterexample that violates these properties.

5. How is the dual space used in practical applications?

The dual space has numerous practical applications, particularly in fields such as physics, engineering, and economics. It is used to define and analyze optimization problems, such as linear programming and convex optimization. It is also used in functional analysis to study the properties of function spaces. In physics, the dual space is used to represent the state space of quantum systems, and in economics, it is used to model consumer preferences and production functions.

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