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Check by example that the inclusion of \(L^{2}[0,1]\) in \({ }^{1}[0,1]\) is proper, that is, find a summable function \(f\) with \(\int_{0}^{1}|f|^{2}=\infty\).

Short Answer

Expert verified
Function \(f(x) = \frac{1}{\sqrt{x}}\) is in \(L^1[0,1]\) and not in \(L^2[0,1]\), proving the inclusion is proper.

Step by step solution

01

Understanding the Problem

To prove that the inclusion of \(L^2[0,1]\) in \(L^1[0,1]\) is proper, we need to find a function which is in \(L^1[0,1]\) but not in \(L^2[0,1]\). This means that the function should be summable over the interval \([0, 1]\), but its square should not be integrable over the same interval.
02

Choosing a Function

Consider the function \(f(x) = \frac{1}{\sqrt{x}}\) on the interval \((0,1]\). We will check if this function is in \(L^1[0,1]\) and \(L^2[0,1]\).
03

Calculating \(L^1\) Integrability

To check for \(L^1\) integrability, we evaluate the integral \(\int_0^1 |f(x)| \, dx = \int_0^1 \frac{1}{\sqrt{x}} \, dx\). This integral is calculated as follows: \[ \int_0^1 \frac{1}{\sqrt{x}} \, dx = 2\sqrt{x} \Big|_0^1 = 2(1) - 2(0) = 2, \] which is finite. Thus, \(f(x)\) is in \(L^1[0,1]\).
04

Calculating \(L^2\) Integrability

To check for \(L^2\) integrability, we evaluate the integral \(\int_0^1 |f(x)|^2 \, dx = \int_0^1 \frac{1}{x} \, dx\). This integral is calculated as follows: \[ \int_0^1 \frac{1}{x} \, dx = \lim_{a \to 0^+} \ln(x) \Big|_a^1 = \lim_{a \to 0^+} (0 - \ln(a)) = \infty, \] which is infinite. Thus, \(f(x)\) is not in \(L^2[0,1]\).
05

Conclusion

The function \(f(x) = \frac{1}{\sqrt{x}}\) is an example that is in \(L^1[0,1]\) but not in \(L^2[0,1]\), showing that the inclusion is proper.

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Key Concepts

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

L1 space
The concept of an \(L^1\) space involves functions which are absolutely integrable over a specific interval, meaning that the integral of their absolute value is finite. For a function to belong to the \(L^1[0,1]\) space, specifically over the interval \[0,1\], it must satisfy the condition: \[ \int_0^1 |f(x)| \, dx < \infty. \]In simpler terms, when you compute the area under the curve of its absolute value, the result should be a finite number.
Let's break this down into more tangible examples:
  • If \(f(x) = x\), then \(\int_0^1 |x| \, dx = \frac{1}{2}\), which is finite, showing \(f\) is in \(L^1[0,1]\).
  • For \(f(x) = |x-0.5|\), calculating \(\int_0^1 |x - 0.5| \, dx\) also results in a finite number, implying it's part of \(L^1[0,1]\).
These examples illustrate that belonging to \(L^1\) space is about the finiteness of the integral of a function's absolute value. This is a useful property since it implies the total 'weighted sum' or 'average size' of the function's values are bounded.
integrability
Integrability, in the context of the exercise, refers to whether a function can be appropriately measured using integration, within certain limits. A function is said to be integrable if its integral is finite.
There are different types of integrability associated with different function spaces:
  • \(L^1\) integrability involves checking if the absolute value of a function can produce a finite integral over its domain.
  • \(L^2\) integrability goes a step further, focusing on whether the square of the function can be integrated to yield a finite result.
Therefore, a function can be \(L^1\) integrable but not \(L^2\) integrable if its squared values increase sharply, causing the \(L^2\) integral to diverge.
The function \(f(x) = \frac{1}{\sqrt{x}}\) demonstrates this, being \(L^1\) integrable but not \(L^2\), since the integral of its square results in infinity. This contrast highlights the more restrictive nature of \(L^2\) integrability, emphasizing the conditions under which functions operate within these spaces.
function spaces
Function spaces are collections of functions that are subject to certain conditions, usually involving some form of convergence or integrability. In mathematical analysis, they help in understanding how functions operate under different constraints.
Importantly, function spaces like \(L^1\) and \(L^2\):
  • Facilitate the study of convergence and limits of functions, crucial for solving differential equations or working in signal processing.
  • Offer a framework for comparing functions using norms, which provide a measure of the 'size' or 'length' of a function.
  • Help mathematicians and engineers describe the behavior of functions under transformations, like Fourier transforms.
\(L^1[0,1]\) consists of functions that are integrable, while \(L^2[0,1]\) includes functions whose squares are integrable.
This slight difference means different types of functions qualify for each space, making these concepts vital when working with functional analysis or applied mathematics settings.
Understanding which function space a specific function belongs to impacts how it can be manipulated and used effectively.

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Most popular questions from this chapter

\( Check that if \)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 for any nonnegative measurable function \(\Delta\) on \(Q\), the class \(L^{2}(Q, \Delta(x) d x)\) of measurable functions with $$ \|f\|^{2}=\int_{Q}|f(x)|^{2} \Delta(x) d x<\infty $$ is likewise a Hilbert space.

\(\lim _{n \uparrow \infty} \int f_{n}=\int f\) if \(f_{1} \geqslant f_{2} \geqslant \cdots, \lim _{n \uparrow \infty} f_{n}=f\), and \(\int f_{1}^{+}<\infty\).

Define the angle between \(\alpha \neq 0\) and \(\beta \neq 0\) by the rule \(\cos \theta=\operatorname{Re}(\alpha, \beta) /(\|\alpha\|\|\beta\|) .\) Check the "law of cosines": $$ \|\alpha-\beta\|^{2}=\|\alpha\|^{2}-2\|\alpha\|\|\beta\| \cos \theta+\|\beta\|^{2} . $$ Step 2: For any \(f\) and \(g\) from \(L^{2}(Q)\), the elementary bound $$ 2\left|f g^{*}\right|=2|f||g| \leqslant|f|^{2}+|g|^{2} $$ shows that \(f g^{*}\) is a summable function, so that the inner product $$ (f, g)=\int_{Q} f g^{*} $$ makes sense. The reader will have no difficulty in checking that all the customary rules for inner products hold; after all, from an algebraic standpoint, \(\int f g^{*}\) is just the same as the inner product for \(C^{n}(n<\infty)\), only now you have an integral in place of a finite sum. An automatic consequence is the Schwarz inequality: $$ |(f, g)|^{2}=\left|\int_{Q} f g^{*}\right|^{2} \leqslant\|f\|^{2}\|g\|^{2}=\int_{Q}|f|^{2} \int_{Q}|g|^{2} . $$ \(\alpha\) and \(\beta\) are declared to be perpendicular if \((\alpha, \beta)=0\). In this circumstance, the law of cosines becomes the "Pythagorean rule": $$ \|\alpha+\beta\|^{2}=\|\alpha\|^{2}+\|\beta\|^{2} . $$ Check the extended Pythagorean rule: $$ \left\|\sum_{k=1}^{n} \alpha_{k}\right\|^{2}=\sum_{k=1}^{n}\left\|\alpha_{k}\right\|^{2} \quad \text { if } \quad\left(\alpha_{i}, \alpha_{j}\right)=0 \text { for } i \neq j . $$ So much for elementary geometry. The proof that \(L^{2}(Q)\) is a Hilbert space will now be presented in a series of simple steps with explanatory asides. Step 1: Check that \(L^{2}(Q)\) is closed under multiplication by complex numbers and under addition. The first is self-evident, while the second follows from the elementary bound $$ |f+g|^{2} \leqslant 2|f|^{2}+2|g|^{2} $$ upon integrating over \(Q\).

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 \(L^{2}\left(Z^{+}\right)\).

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