Lebesgue Integration of Simple Functions .... Lindstrom, Lemma 7.4.6 ...

In summary: The notation ##f_n\nearrow## is for a sequence of functions, and means that ##\forall x:\ \forall n:\ f_{n+1}(x) \ge f_n(x)##. So, it is not from below. The notation ##f_n\uparrow## is for a sequence of sets, and means that ##\forall n:\ f_{n+1}\supseteq f_n##. So, it is not from below.We are often so concerned with the pointwise convergence of a sequence of functions that we forget to check whether that sequence is monotonic. When we have monotonic convergence, we can do things like interchange the limit and the integral. That's the case here.
  • #1
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I need help in order to fully understand Lindstrom's proof of Lemma 7.4.6 concerning the Lebesgue integration of an increasing sequence of simple functions ...
I am reading Tom L. Lindstrom's book: Spaces: An Introduction to Real Analysis ... and I am focused on Chapter 7: Measure and Integration ...

I need help with the proof of Lemma 7.4.6 ...

Lemma 7.4.6 and its proof read as follows:
Lindstrom - Lemma  7.4.6 .png


In the above proof by Lindstrom we read the following:

" ... ... Since this holds for any number ##a## less than ##b## and any number ##m## less than ##\mu (B)##, we must have ##\lim_{ n \to \infty } \int_B f_n d \mu \geq b \mu (B)##. ... ... "I need help in order to show, formally and rigorously, that ##\lim_{ n \to \infty } \int_B f_n d \mu \geq b \mu (B)## ... ...My thoughts are that we could assume that ##\lim_{ n \to \infty } \int_B f_n d \mu \lt b \mu (B)## ... ... and proceed to demonstrate a contradiction ... but I'm not sure how to formally proceed ... ...

Help will be much appreciated ...

Peter

=================================================================================================================


Readers of the above post may be assisted by access to Lindstrom's introduction to the integration of simple functions ... so I am providing access to the relevant text ... as follows:
Lindstrom - 1 - Section 7.4 ... Integration of Simple Functions ... Part 1... .png

Lindstrom - 2 - Section 7.4 ... Integration of Simple Functions ... Part 2 ... .png


Hope that helps ...

Peter
 
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  • #2
Note that ##L:= \lim_n \int_B f_n d \mu## always exists (possibly with value ##\infty##), because it is the limit of an increasing sequence.

The claim the proof makes is:

$$\forall m < \mu(B), \forall a < b : am \leq L \implies b\mu(B) \leq L$$

Fix ##a < b##, taking the limit ##m \to \mu(B)-## (limit from the left) of ##am \leq L##, we obtain ##a \mu(B) \leq L##. Since this holds for all ##a < b##, you can take the limit ##a \to b-## (again limit from the left) to obtain ##b \mu(B) \leq L##.

Basically, this boils down to showing that limits preserve (non-strict) inequalities of functions, which you probably already know since you are studying measure theory.
 
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The answer lies in our total freedom to choose both ##a\in(0,b)## and ##m\in(0,\mu(B))##. Let ##a=b-h## and ##m=\mu(B)-h## where we can choose any ##h## such that ##0<h<\min(b,\mu(B))##.
Then

\begin{align*}
\lim_{n\to\infty} \int_B f_n\ d\mu
&\ge \lim_{n\to\infty} am
=am
= (b-h)(\mu(B)-h)
= (b\mu(B)-h\mu(B) -bh +h^2)\\
& =b\mu(B)-h\mu(B) -bh +h^2
> b\mu(B)-h(\mu(B)+b)
\end{align*}

whence
\begin{equation*}
\lim_{n\to\infty} \int_B f_n\ d\mu > b\mu(B)-h(\mu(B)+b)\ \ \ \textrm{(A)}
\end{equation*}
Now suppose the contrary of the theorem's result, that ##\lim_{n\to\infty} \int_B f_n\ d\mu = b\mu(B)-k## for some ##k>0##.

Then choose ##h= \min\left(b/2,\mu(B)/2, \frac k{2(\mu(B)+b)}\right)##, which we noted above that we are free to do. Substitute that expression for ##h## into RHS of (A) and you will get a contradiction:
$$\lim_{n\to\infty} \int_B f_n\ d\mu > b\mu(B)-k/2
> b\mu(B)-k = \lim_{n\to\infty} \int_B f_n\ d\mu$$
 
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  • #4
Thanks to Andrew and Math_QED for most helpful posts ...

Working carefully through your proofs now ...

BUT ... I have another question ...

In the above proof by Lindstrom we read the following:

" ... ... Since ##f_n (x) \uparrow b## for all ##x \in B## ... ... "Unless I am misunderstanding the notation, ##f_n (x) \uparrow b## means ##f_n## tends to ##b## from below ... but ... all we are given is that ##\lim_{n \to \infty } f_n (x) \geq b## which surely is not the same ...

Can someone please clarify this issue ...

Peter
 
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  • #5
Math Amateur said:
Thanks to Andrew and Math_QED for most helpful posts ...

Working carefully through your proofs now ...

BUT ... I have another question ...

In the above proof by Lindstrom we read the following:

" ... ... Since ##f_n (x) \uparrow b## for all ##x \in B## ... ... "Unless I am misunderstanding the notation, ##f_n (x) \uparrow b## means ##f_n## tends to ##b## from below ... but ... all we are given is that ##\lim_{n \to \infty } f_n (x) \geq b## which surely is not the same ...

Can someone please clarify this issue ...

Peter

That notation means that the sequence is non-decreasing, which is a hypothesis.
 
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Oh ... OK ... thanks ...

Peter
 
  • #7
Math Amateur said:
In the above proof by Lindstrom we read the following:

" ... ... Since ##f_n (x) \uparrow b## for all ##x \in B## ... ... "Unless I am misunderstanding the notation, ##f_n (x) \uparrow b## means ##f_n## tends to ##b## from below
It is justified by the theorem stating that ##\{f_n\}## is an "increasing sequence of non-negative simple functions." By "increasing", it means that ##\forall x:\ \forall n:\ f_{n+1}(x) > f_n(x)##.
 

Related to Lebesgue Integration of Simple Functions .... Lindstrom, Lemma 7.4.6 ...

1. What is Lebesgue integration?

Lebesgue integration is a mathematical concept that extends the Riemann integral to a wider class of functions. It allows for the integration of more complicated and discontinuous functions, and is an important tool in many areas of mathematics, including measure theory and probability.

2. What are simple functions?

Simple functions are a type of function that can be written as a finite sum of indicator functions, where each indicator function takes on the value of 1 or 0. They are important in Lebesgue integration because they can be used to approximate more complicated functions.

3. What is Lindstrom's Lemma 7.4.6?

Lindstrom's Lemma 7.4.6 is a theorem that states that for any measurable function, there exists a sequence of simple functions that converges to it in a certain sense. This lemma is often used in the proof of the Lebesgue integral convergence theorem.

4. How is Lebesgue integration of simple functions different from Riemann integration?

Lebesgue integration allows for the integration of more complicated and discontinuous functions, while Riemann integration is limited to continuous functions. Lebesgue integration also uses a different approach to defining the integral, based on the concept of measure, rather than the limit of a sum.

5. What are some applications of Lebesgue integration?

Lebesgue integration is used in many areas of mathematics, including probability, measure theory, and functional analysis. It is also used in other fields, such as physics and engineering, to solve problems involving complex functions and distributions.

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