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The set of linear functionals on is itself a vector space:
Definition 1 The dual space (or algebraic dual) of is the vector space of all linear function on .
Example 1 If then and any linear functional on can be written in this form.
Definition 2 Let be a normed space. The normed dual of is the normed space of all bounded linear functionals with norm .
Since in the sequel we refer almost exclusively to the normed dual, we will abbreviate to “dual” (and ignore the algebraic dual in the process).
Example 2 Let us find the dual of . In this space,
From Example [link] , a linear functional can be written as:
There is a one-to-one mapping between the space and the dual of :
For this reason, is called self-dual (as there is a one-to-one mapping ). Using the Cauchy-Schwartz Inequality, we can show that
with equality if is a scalar multiple of . Therefore, we have that , i.e., the norms of and match through the mapping.
Theorem 1 All normed dual spaces are Banach.
Since we have shown that all dual spaces have a valid norm, it remains to be shown that all Cauchy sequences in in are convergent.
Let be a Cauchy sequence in . Thus, we have that for each there exists such that if then as . We will first show that for an arbitrary input , the sequence is Cauchy. Pick an arbitrary , and obtain the from the definition of Cauchy sequence for . Then for all we have that .
Now, since and are complete, we have that the sequence converges to some scalar . In this way, we can define a new function that collects the limits of all such sequences over all inputs . We conjecture that the sequence : we begin by picking some . Since is convergent to for each , we have that for , . Now, let . Then, for ,
Therefore, we have found an for each that fits the definition of convergence, and so .
Next, we will show that is linear and bounded. To show that is linear, we check that
To show that is bounded, we have that for each there exists (shown above) such that if then
This shows that is linear and bounded and so . Therefore, converges in and, since the Cauchy sequence was arbitrary, we have that is complete and therefore Banach.
Recall the space , with the -norm . The dual of is , with . That is, every linear bounded functional can be represented in terms of , where . Note that if then , and so is self-dual.
Consider the example self-dual space and pick a basis for it. Recall that for each there exists a bounded linear functional given by Thus, one can build a basis for the dual space as .
Since it is easier to conceive a dual space by linking it to elements of a known space (as seen above for self duals), we may ask if there are large classes of spaces who are self-dual.
Theorem 2 (Riesz Representation Theorem) If is a bounded linear functional on a Hilbert space , there exists a unique vector such that for all we have . Furthermore, we have , and every defines a unique bounded linear functional .
Thus, since there is a one-to-one mapping between the dual of the Hilbert space and the Hilbert space ( ), we say that all Hilbert spaces are self-dual. Pick a linear bounded functional and let be the set of all vectors for which . Note that is a closed subspace of , for if is sequence that converges to then, due to the continuity of , and so . We consider two possibilities for the subspace :
We now show uniqueness: let us assume that there exists a second , , for which for all . Then we have that for all ,
This contradicts the original assumption that . Therefore we have that is unique, completing the proof.
When we consider spaces nested inside one another, we can define pairs of functions that match each other at the overlap.
Definition 3 Let be a linear functional on a subspace of a vector space . A linear functional is said to be an extension of to (where is another subspace of that satisfies ) if is defined on and is identical to on .
Here is another reason why we are so interested in bounded linear functionals.
Theorem 3 (Hahn-Banach Theorem) A bounded linear functional on a subspace can be extended to a bounded linear functional on the entire space with
The proof is in page 111 of Luenberger.
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