Proving Measurable Functions Convergence in Finite Measure Sets

In summary, the following sequence of measurable sets exists:E_{1}, E_{2}, E_{3}, E_{4}, E_{5}, E_{6}, E_{7}, E_{8}, E_{9}, E_{10}
  • #1
SqueeSpleen
141
5
Let [itex]E[/itex] be of finite measure and let [itex]\{ f_{n} \} _{n \geq 1} : E \rightarrow \overline{\mathbb{R}}[/itex] measurable functions, finites almost everywhere in [itex]E[/itex] such that [itex]f_{n} \rightarrow_{n \to \infty}[/itex] f almost everywhere in [itex]E[/itex]. Prove that exists a sequence [itex](E_{i})_{i \geq 1}[/itex] of measurable sets of [itex]E[/itex] such that:
1) [itex]| E- \displaystyle \bigcup_{i=1}^{\infty} E_{i} | = 0[/itex]
2) For every [itex]i \geq 1[/itex], [itex]f \rightrightarrows_{n \to \infty}[/itex] in [itex]E_{i}[/itex]

Notation:
[itex]f_{n} \rightarrow_{n \to \infty} f[/itex] means "[itex]f_{n}[/itex] converges to [itex]f[/itex] as [itex]n \to \infty[/itex]"
[itex]f \rightrightarrows_{n \to \infty}[/itex] means "[itex]f_{n}[/itex] converges uniformly to [itex]f[/itex] as [itex]n \to \infty[/itex]"
I don't know if there are multiple common definitions, but here is the mine:
A function [itex]f : \mathbb{R}^{n} \rightarrow \mathbb{R}[/itex] is measurable if for all [itex]a \in \mathbb{R}[/itex]:
[itex]\{ f > a \} = \{ x \in \mathbb{R}^{n} / f(x) > a \}[/itex] is measurable in the sense of Lebesgue.
[itex]E \in R^{n}[/itex] is measurable in the Lebesgue sense if for every [itex]\varepsilon > 0 \exists U \in \mathcal{U} / E \in U \wedge |U-E|_{e} < \varepsilon[/itex]
Where [itex]|[/itex] [itex]|_{e}[/itex] is the outer measure.Attemp/idea:
I tried to divide [itex]E[/itex] in [itex]E_{k,n} = \{ x \in E / | f(x)-f_{n} | < \frac{1}{k} \}[/itex] but it will not help at all because something may "converge" similar in the first m terms but converge otherwise later.
 
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  • #2
SqueeSpleen said:
medible functions

Sorry, did you mistype this? I don't know what it means.
 
  • #3
This was a mistake, I meant measurable.
 
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  • #4
In a previous exercise (I didn't realize it could be useful here, so I didn't post it) I had the following result:
Let [itex]E[/itex] be a set of finite measure and [itex]\{ f_{k} \}_{k \in \mathbb{N}} : E \rightarrow \mathbb{R}[/itex] a sequence of measurable functions such that for every [itex]x \in E[/itex], exists [itex]M_{x} \in \mathbb{R}_{>0}[/itex]:
[itex]| f_{k} (x) | \leq M_{x}, \forall k \in \mathbb{N}[/itex]
Then for every [itex]\varepsilon > 0[/itex] exists [itex]F \subset E[/itex] closed and [itex]M \in \mathbb{R}_{>0}[/itex] such that:
[itex]| E - F | < \varepsilon[/itex] and [itex]| f_{k} (x) | \leq M, \forall k \in \mathbb{N}, \forall x \in F[/itex]

If there's a set where [itex]\forall M \in \mathbb{R}_{>0} \exists k \in \mathbb{N} / | f - f_{k} | > M[/itex], then this set has to have null measure, because either the functions doesn't converge here or they're not finite.

Let's be [itex]E = H \cup Z[/itex] where [itex]\{ f_{k} \}_{k \in \mathbb{N}}[/itex] are finite and converges to [itex]f[/itex] in [itex]H[/itex] and [itex]| Z | = 0[/itex] ([itex]E-Z=H[/itex]) (They may be not finite in differents sets, but all are of zero measure and the countable union of sets of null measure has null measure).

As [itex] H [/itex] is measurable, for every [itex] \varepsilon > 0[/itex] exists [itex] C [/itex] closed such that [itex] C \in H [/itex] and [itex] | H - C | < \varepsilon [/itex].

I'll try with [itex]E_{k} = \{ x \in H / f_{n} (x) < k \forall n \in \mathbb{N}[/itex]

Edit: I had a non-trivial mistake so I was trying to fix it but I failed and I have a lecture in 6 hours, so I'd better to sleep now.
 
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  • #5
Getting late here -- will look at this tomorrow if someone doesn't help you sooner.
 

1. What is the definition of measurable functions convergence in finite measure sets?

Measurable functions convergence in finite measure sets refers to the idea that as we take the limit of a sequence of measurable functions, the values of the functions approach each other in a measurable way. In other words, the functions converge to a common limit, which can be measured in terms of the measure of the set on which the functions are defined.

2. How is convergence of measurable functions in finite measure sets different from pointwise convergence?

Convergence of measurable functions in finite measure sets is different from pointwise convergence in that it considers the convergence of the functions as a whole, rather than at individual points. Pointwise convergence only looks at the values of the functions at each point, whereas measurable functions convergence takes into account the measure of the set on which the functions are defined.

3. What are some common examples of measurable functions convergence in finite measure sets?

Some common examples of measurable functions convergence in finite measure sets include the convergence of Riemann integrable functions, Lebesgue integrable functions, and Borel measurable functions. These types of functions are frequently used in mathematical analysis and probability theory.

4. How is the concept of measurable functions convergence in finite measure sets applied in real-life situations?

Measurable functions convergence in finite measure sets has many applications in real-life situations. For example, it is used in statistics to analyze the convergence of sample means and in economics to study the convergence of economic variables over time. It is also used in physics to describe the convergence of physical phenomena and in engineering to analyze the convergence of numerical methods.

5. What are some important theorems related to proving measurable functions convergence in finite measure sets?

Some important theorems related to proving measurable functions convergence in finite measure sets include the Dominated Convergence Theorem, the Monotone Convergence Theorem, and the Fatou's Lemma. These theorems provide conditions under which we can prove the convergence of measurable functions in finite measure sets and are essential tools in mathematical analysis and measure theory.

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