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Show that \(L^{2}(Q)\) cannot have a finite basis; this is what is meant by saying that \(L^{2}(Q)\) is "infinite-dimensional." Hint: If \(A\) and \(B\) are disjoint measurable subsets of \(Q\) of finite measure, then \(1_{A}\) and \(1_{B}\) are perpendicular in \(L^{2}(Q)\).

Short Answer

Expert verified
\(L^{2}(Q)\) is infinite-dimensional because it has an infinite orthogonal set of characteristic functions.

Step by step solution

01

Understand the Concept of Basis

A basis of a vector space is a set of vectors that are linearly independent and span the entire space. For a space to be finite-dimensional, it must have a finite basis.
02

Define the Characteristic Functions

Consider two disjoint measurable subsets \(A\) and \(B\) within \(Q\), each having finite measure. Define the characteristic functions \(1_A(x)\) and \(1_B(x)\) as functions that take the value 1 if \(x\) is in \(A\) or \(B\), respectively, and 0 otherwise.
03

Proving Orthogonality

To show that \(1_A\) and \(1_B\) are perpendicular or orthogonal in \(L^{2}(Q)\), we need to show that their inner product is zero. The inner product is defined as \(\int_{Q} 1_A(x) 1_B(x) \, dx\). Since \(A\) and \(B\) are disjoint, \(1_A(x) 1_B(x) = 0\) for all \(x\) in \(Q\), hence the integral equals zero, proving orthogonality.
04

Construct Infinite Orthogonal Set

Construct characteristic functions for a countably infinite collection of disjoint measurable subsets \(\{A_n\}\) of \(Q\), each with finite measure. Their characteristic functions \(\{1_{A_n}\}\) are all orthogonal to each other for the same reason as in the previous step.
05

Conclusion on Infinite Dimension

Since you can find an infinite set of orthogonal vectors (characteristic functions) in \(L^2(Q)\), it means that no finite set of vectors could span \(L^2(Q)\), confirming that \(L^2(Q)\) is infinite-dimensional.

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Key Concepts

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

Orthogonality in function spaces
In the realm of function spaces, the concept of orthogonality extends beyond perpendicular vectors. It applies to functions that, when paired together, yield an inner product of zero. This means they are distinct in a special way, having no overlap or similarity in the context of the space they inhabit. Orthogonality in function spaces like \(L^{2}(Q)\) ensures that the functions are as far apart from each other as possible.
Let's consider an example to visualize this concept. Imagine two disjoint measurable subsets \(A\) and \(B\) within the set \(Q\). For these subsets, the characteristic functions \(1_A(x)\) and \(1_B(x)\) can be defined. These functions take the value of 1 if \(x\) is within the subset (\(A\) or \(B\)) and 0 otherwise.
To determine orthogonality between these two functions, calculate their inner product using the formula:
  • \(\int_{Q} 1_A(x) 1_B(x) \, dx\)
Since \(A\) and \(B\) are disjoint, any overlap of function values where both are 1 cannot occur. The result of \(1_A(x) 1_B(x)\) is zero for all \(x\) in \(Q\), leading to the integral value being zero. Hence, these functions are orthogonal. This orthogonality is critical in proving that \(L^{2}(Q)\) is infinite-dimensional.
Characteristic functions in measurable spaces
Characteristic functions serve as a fundamental tool in measurable spaces. They help to bridge intervals to functions by indicating the presence or absence of elements within a particular set. Let's define what a characteristic function is in measurable terms.
For a measurable subset \(A\) within the space \(Q\), the characteristic function \(1_A(x)\) is defined as:
  • \(1_A(x) = 1\) if \(x \in A\)
  • \(1_A(x) = 0\) if \(x otin A\)
This representation highlights how characteristic functions can "light up" the subset space they are defined over. They reflect properties such as measures and integration in the more complex structure of the full space \(Q\).
In measurable spaces, characteristic functions are not merely binary indicators. They help in decomposing the entire space \(Q\) into manageable measurable parts. Each characteristic function acts as a building block, paving the way to measure and analyze larger, more complex functions and subsets.
Basis in vector spaces
A basis in vector spaces is an essential concept that defines the "skeleton" or "framework" of the space. It consists of vectors that are both linearly independent and spanning. This means every vector in the space can be expressed as a linear combination of the basis vectors.
In a finite-dimensional vector space, there's a finite set of such basis vectors. However, in infinite-dimensional spaces like \(L^{2}(Q)\), no finite set of vectors suffices. But why is this so?
To illustrate, consider the set \(L^{2}(Q)\) and construct an infinite orthogonal set of characteristic functions over disjoint subsets of \(Q\). Suppose each subset \(A_n\) is measurable, at finite measure, and disjoint from the others. Then the characteristic functions \(\{1_{A_n}\}\) form a set of orthogonal vectors.
  • Each \(1_{A_n}\) is orthogonal to every other \(1_{A_n}\), meaning none can be written as a linear combination of the others.
  • Since you can keep adding more \(1_{A_n}\) functions indefinitely, the span of these functions expands infinitely.
Conclusively, because essential relations between the basis and orthogonal vectors exhibit infinite possibilities, \(L^{2}(Q)\) cannot have a finite basis, confirming its infinite-dimensional nature.

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Most popular questions from this chapter

Check that the map \(\wedge\) automatically preserves inner products: $$ \left(f_{1}, f_{2}\right)=\int_{Q} f_{1} f_{2}^{*}=\left(\hat{f}_{1}, \hat{f}_{2}\right)=\sum_{n=1}^{\infty} \hat{f}_{1}(n) \hat{f}_{2}^{*}(n) . $$ This fact is the so-called Parseval identity. Hint: \(\left\|f_{1}-f_{2}\right\|=\left\|\left(f_{1}-f_{2}\right)^{\wedge}\right\|=\) \(\left\|f_{1}-f_{2}\right\|\). A little glossary of the chief results of this section may be helpful; \(e_{n}: n \geqslant 1\) is any unit-perpendicular basis of \(L^{2}(Q)\) and \(\hat{f}(n)=\left(f, e_{n}\right)\). Bessel: \(\quad\|f\|^{2}=\int_{Q}|f|^{2} \geqslant\|\hat{f}\|^{2}=\Sigma|\hat{f}|^{2}\). Plancherel: \(\quad\|f\|^{2}=\|\hat{f}\|^{2}\). Parseval \(^{2}: \quad\left(f_{1}, f_{2}\right)=\int_{Q} f_{1} f_{2}^{*}=\left(\hat{f}_{1}, \hat{f}_{2}\right)=\sum \hat{f}_{1} \hat{f}_{2}{ }^{*} .\) Riesz-Fischer: \(\quad \wedge\) is an isomorphism from \(L^{2}(Q)\) onto \(L^{2}\left(Z^{+}\right)\).

\( Check that if \)x \in Q-B\(, then \)f_{n j}(x)\( converges to a finite limit as \)j \uparrow \infty\(. Hint: \)\quad x \in Q-B\( means that \)\left|f_{n_{j+1}}(x)-f_{n j}(x)\right|<2^{-j / 3}\( for all sufficiently large \)j\(. By the Chebyshev inequality [Exercise 1.1.12], $$ m\left(A_{j}\right) \leqslant 2^{2 j / 3}\left\|f_{n_{j+1}}-f_{n_{j}}\right\|^{2} \leqslant 2^{-j / 3}, $$ and since this is the general term of a convergent sum, the Borel-Cantelli lemma, just before Exercise \)1.1 .11\( tells you that $$ m(B)=m\left(A_{j} \quad \text { i.o. }\right)=0 . $$ Therefore, $$ C=\left(x: f_{n j}(x) \text { fails to converge as } j \uparrow \infty\right) \subset B $$ is of measure 0 , too [see Exercise 10], and you can define a measurable function \)f\( by the rule $$ f(x)= \begin{cases}0 & \text { if } x \in C \\ \lim _{j \uparrow \infty} f_{n_{j}}(x) & \text { otherwise }\end{cases} $$ The claim is that \)f_{n}\( converges to this function \)f\( in \)\mathrm{L}^{2}(Q)\(. Because \)m(C)=0\(, \)f_{n}\(, converges to \)f\( pointwise a.e., and by Fatou's lemma $$ \begin{aligned} \left\|f_{n}-f\right\|^{2} &=\int_{Q} \lim _{j \uparrow \infty}\left|f_{n}-f_{n j}\right|^{2} \leqslant \liminf _{j \uparrow \infty} \int_{Q}\left|f_{n}-f_{n j}\right|^{2} \\ &=\liminf _{j \uparrow \infty}\left\|f_{n}-f_{n j}\right\|^{2} \leqslant 2^{-i} \quad \text { if } \quad n \geqslant n_{i} \end{aligned} $$ From this you learn two things: first, that \)f \in \mathrm{L}^{2}(Q)\(, since $$ \|f\| \leqslant\left\|f_{n_{1}}\right\|+\left\|f_{n_{1}}-f\right\| \leqslant\left\|f_{n_{1}}\right\|+2^{-1 / 2}<\infty, $$and second, that \)f_{n}\( approaches \)f\( in \)\mathrm{L}^{2}(Q)\( since \)2^{-i} \downarrow 0\( as \)i \uparrow \infty\(. The proof is finished. Step 4: Verify that \)\mathrm{L}^{2}(Q)\( is separable. This will finish the proof that it is a Hilbert space. The problem is to show that you can come as close as you please to any function \)f \in \mathrm{L}^{2}(Q)\( by functions from a fixed countable family \)K \subset L^{2}(Q)\(. Clearly, it suffices to deal with real functions. For any such function \)f\(, any integral \)i, j\(, and \)k \geqslant 1\(, let $$ \begin{aligned} &f_{1}=\left\\{\begin{array}{lll} f & \text { if } & |x| \leqslant i \\ 0 & \text { if } & |x|>i \end{array}\right. \\ &f_{2}= \begin{cases}f_{1} & \text { if }\left|f_{1}\right| \leqslant j \\ 0 & \text { if }\left|f_{1}\right|>j\end{cases} \end{aligned} $$ and $$ f_{3}=k^{-1}\left[k f_{2}\right], $$ in which \)[f]\( stands for the largest integer \)\leqslant f\(. Each of these functions is measurable and $$ \begin{aligned} \left\|f-f_{1}\right\|^{2} &=\int_{Q \cap(x ;|x|>i)}|f|^{2}, \\ \left\|f_{1}-f_{2}\right\|^{2} &=\int_{Q \cap\left(x:\left|f_{1}\right|>j\right)}\left|f_{1}\right|^{2} \leqslant \int_{Q \cap(x|| f \mid>j)}|f|^{2}, \\ \left\|f_{2}-f_{3}\right\|^{2} & \leqslant \int_{Q \cap(x:|x| \leqslant i)} k^{-2} \leqslant 2 i k^{-2}, \end{aligned} $$ so that you can make $$ \left\|f-f_{3}\right\| \leqslant\left\|f-f_{1}\right\|+\left\|f_{1}-f_{2}\right\|+\left\|f_{2}-f_{3}\right\| $$ as small as you like by taking \)i, j\(, and \)k\( large, in that order. Bring in the family \)K\( of piecewise constant functions \)f\(, which vanish far out, take rational values only, and jump only at a finite number of rational points. \)K\( is a countable subfamily of \)L^{2}(Q)\( and is closed under finite sums with rational coefficients. Now \)f_{3}=l / k\( on the set $$ A=Q \cap(x:|x| \leqslant i) \cap\left(x: l / k \leqslant f_{2}(x)<(l+1) / k\right), $$ so \)f_{3}\( is the sum (with coefficients \)l / k\( ) of indicator functions \)1_{A}\( of sets of measure \)\leqslant 2 i<\infty\(. Therefore, to finish the proof, you only have to check that such an indicator function can be well approximated by a function from \)K\(. By the recipe for Lebesgue measure of Section 1.1, you can cover such a set \)A\( by a countable family of intervals \)I_{k}\(, so closely as to make $$ 0 \leqslant \sum_{k=1}^{\infty} m\left(I_{k}\right)-m(A) $$ as small as you please. Especially, you can find a finite union \)B=\bigcup_{k=1}^{n} I_{k}\( so as to make $$ m(A-B)+m(B-A)=\int_{A-B} 1+\int_{B-A} 1=\left\|1_{A}-1_{B}\right\|^{2} $$ as small as you like, and you can even modify \)B\( to make the endpoints of the intervals rational, at the expense of a small change in \)\left\|1_{A}-1_{B}\right\| .\( But then \)1_{B} \in K$, and the proof is finished.

Check that \(\bigcap_{n \geqslant 1} \bigcup_{k \geqslant n} B_{k}\) is really the set of points \(x\) that belong to \(B_{n}\) for infinitely many values of \(n\). This set is customarily denoted by " \(B_{n}\) i.o."; for \(i . o .\), read infinitely often.

Check that the space \(L^{1}(Q)\) is not a Hilbert space. Hint: In any Hilbert space \(\|\alpha+\beta\|^{2}+\|\alpha-\beta\|^{2}=2\|\alpha\|^{2}+2\|\beta\|^{2}\). Try this out for \(Q=[0,1], \alpha=1\), and \(\beta=x\). What happens? Try again.

Check that if \(f_{n}\) converges to \(f\) in \(\mathrm{L}^{2}(Q)\) and if it also converges to \(f^{0}\) pointwise a.e., then \(f=f^{0}\) a.e.

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