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Let \(\mathcal{B}\) be a basis for an infinite-dimensional vector space \(V\) and define, for all \(b \in \mathcal{B}\), the map \(b^{\prime} \in V^{*}\) by \(b^{\prime}(c)=1\) if \(c=b\) and 0 otherwise, for all \(c \in \mathcal{B}\). Does \(\left\\{b^{\prime} \mid b \in \mathcal{B}\right\\}\) form a basis for \(V^{*}\) ? What do you conclude about the concept of a dual basis?

Short Answer

Expert verified
The given set of functionals \(\{b^{\prime} \mid b \in \mathcal{B}\}\) is linearly independent but does not span the dual space V* in an infinite-dimensional vector space. This implies that there may exist functionals in V* not formed by a linear combination of the basis element functionals. Therefore, the concept of a dual basis (i.e., a basis for V* formed from the linear functionals of the basis elements of V) is not sufficient to describe the entire dual space V* for infinite-dimensional vector spaces.

Step by step solution

01

Define the dual space V*

The dual space V* is the set of all linear functionals on V, which are linear maps from V to its base field F (usually the Real numbers R or Complex numbers C). Mathematically, we can express it as: \[ V^{*} = \{ f: V \rightarrow F | f \text{ is linear} \}\]
02

Determine if the given map forms a basis for V*

For each \(b \in \mathcal{B}\), we define a map \(b^{\prime} \in V^{*}\) such that \(b^{\prime}(c) = 1\) if \(c = b\), and 0 otherwise, for all \(c \in \mathcal{B}\). To determine if the given set \(\{b^{\prime} \mid b \in \mathcal{B}\}\) forms a basis for V*, we need to show that this set is linearly independent and spans V*. First, let's show that the set is linearly independent: Assume that we have a linear combination of the elements in the set that equates to the zero functional, i.e., \[\sum_{b \in \mathcal{B}} k_b b^{\prime} = 0\] where \(k_b \in F\). Now, let's apply this functional to any basis element \(c \in \mathcal{B}\): \[\sum_{b \in \mathcal{B}} k_b b^{\prime}(c) = 0\] Due to the definition of \(b^{\prime}\), only one term in the sum will be non-zero, which is the term corresponding to \(c = b\). So, we have \(k_c = 0\), which implies that all coefficients \(k_b\) are zero. Thus, the set \(\{b^{\prime} \mid b \in \mathcal{B}\}\) is linearly independent. Next, showing that the set spans V* is more difficult due to the infinite dimension of V. In fact, it turns out that the given set does not span V*. To see why, consider a functional \(f \in V^{*}\), such that for any finite subset \(S \subseteq \mathcal{B}\), we have: \[f \left( \sum_{b \in S} b \right) = 1\] Clearly, this functional cannot be a linear combination of elements from the set \(\{b^{\prime} \mid b \in \mathcal{B}\}\) (if it were, it would evaluate to 0 for any element b in B that is not in S). Therefore, the given set does not span V*, and thus cannot form a basis for V*.
03

Conclusions about the concept of a dual basis

We have found that the given set of functionals \(\{b^{\prime} \mid b \in \mathcal{B}\}\) is linearly independent but does not span the dual space V*. This implies that for infinite-dimensional vector spaces, there may exist functionals in V* not formed by a linear combination of the basis element functionals. Consequently, the concept of a dual basis (i.e., a basis for V* formed from the linear functionals of the basis elements of V) is not sufficient to describe the entire dual space V* for infinite-dimensional vector spaces.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Linear Functional
In the world of mathematics, a linear functional is a cornerstone concept. A linear functional is a special type of function that takes a vector from a vector space and maps it to the base field, often the real or complex numbers. The defining characteristic of a linear functional is its adherence to two key properties: it must be additive and homogeneous.
To put it succinctly, if we have a linear functional 'f' on a vector space 'V', for any vectors 'u' and 'v' in 'V', and any scalar 'a', these properties can be expressed as:
  • \(f(u + v) = f(u) + f(v)\)
  • \(f(a \times u) = a \times f(u)\)
Understanding linear functionals is vital as they form the building blocks of the dual space, a deeply interconnected concept we'll explore in the next section.
Dual Space
The dual space, denoted as \(V^*\), is a term that sparks curiosity among those studying vector spaces. It refers to the set of all possible linear functionals that can be applied to a vector space 'V'. In essence, if you have a vector space 'V', its dual space \(V^*\) is like a parallel universe where every point is not a vector, but instead, it's a linear functional defined on 'V'.
Imagine each linear functional as a new 'dimension' in this universe - just as vectors have directions in 'V', each linear functional offers a new way to interact with these vectors. The profound nature of dual spaces becomes more intriguing in infinite-dimensional vector spaces, where the relationship between 'V' and \(V^*\) is not as straightforward as it is in finite dimensions.
Infinite-dimensional Vector Spaces
Moving through the abstract halls of mathematics, one might stumble upon the concept of an infinite-dimensional vector space. Unlike its finite-dimensional cousins, wherein we can list a finite set of vectors that span the entire space, an infinite-dimensional vector space defies simple description due to its unending nature.
To grapple with this complexity, we use the notion of a 'basis', which, in finite dimensions, is a set of linearly independent vectors that span the space. However, in the realm of the infinite, the usual rules become murky. A basis exists (assuming the axiom of choice), but it's not enough to capture all possible linear functionals, as the infinite nature of the space precludes a direct translation of our finite intuition. The exercise in question showcases this enigma vividly by demonstrating that a set of basis-induced linear functionals cannot fully characterize the dual space in this boundless context.
Linear Independence
Linear independence is a fundamental concept that speaks to the uniqueness of representation within a vector space. A set of vectors is deemed linearly independent if no vector in the set can be written as a linear combination of the others. Here's a simple way to think about it: if each vector in the set is bringing something 'new' to the table, something that the other vectors don't provide, then they are linearly independent.
Mathematically, a set of vectors \(\{v_1, v_2, \.\.\.\, v_n\}\) in 'V' is linearly independent if the only scalars \(\{a_1, a_2, \.\.\.\, a_n\}\) that satisfy the equation \[a_1v_1 + a_2v_2 + \.\.\. + a_nv_n = 0\]are the trivial solutions \(a_1 = a_2 = \.\.\. = a_n = 0\). This notion directly informs our understanding of a basis, be it in finite or infinite-dimensional spaces.
Spanning Sets
Lastly, let's dive into the concept of spanning sets. To 'span' a vector space means to cover every inch of it, much like a tapestry might cover a wall. A set of vectors spans a vector space if you can combine them in every possible way - through addition and scalar multiplication - to get any vector in the space.
In mathematical terms, a set of vectors \(S\) spans a vector space 'V' if every vector 'v' in 'V' can be expressed as a linear combination of the vectors in \(S\). That is,\[v = a_1v_1 + a_2v_2 + \.\.\. + a_nv_n\]for some vectors \(v_1, v_2, \.\.\.\, v_n\) in \(S\) and scalars \(a_1, a_2, \.\.\.\, a_n\). Spanning sets are integral as they, along with linear independence, help determine the basis of a vector space which is pivotal in solving various mathematical problems, such as those presented in the dual space of an infinite-dimensional vector space.

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