Can You Prove This Measure Theory Problem?

But I don't know, can you get the contradiction from that following argument?A \leq 0 + A < A + \frac{\delta}{2} < A + \epsilon.In summary, the goal is to prove that if f_n converges to f in measure and \mu(\left\{f_n > h\right\}) \leq A, then \mu(\left\{f > h\right\}) \leq A. This is done by assuming the opposite and arriving at a contradiction by using the fact that the limit of a sequence of measurable sets is equal to the measure of their union. The key steps involve finding a suitable epsilon that leads to a contradiction and proving the inclusion of
  • #1
johnson123
17
0
Problem: [tex]f_{n}\rightarrow f [/tex] in measure, [tex]\mu(\left\{f_{n}>h\right\})\leq A[/tex]

Prove that [tex]\mu(\left\{f>h\right\})\leq A[/tex].

My Work:

Suppose not, then [tex]\mu(\left\{f>h\right\}) > A[/tex].

From the triangle inequality for measures we get

[tex]\mu(\left\{f>h\right\}) = \mu(\left\{f-f_{n}+f_{n}>h\right\})

\leq\mu(\left\{f-f_{n}>0\right\}) + \mu(\left\{f_{n}>h\right\}) [/tex].

So [tex] A<\mu(\left\{f-f_{n}>0\right\}) + \mu(\left\{f_{n}>h\right\})

\leq\mu(\left\{f-f_{n}>0\right\}) + A [/tex].

Taking limits on both sides (n->00)yields:

[tex] A < 0 + A \Rightarrow\Leftarrow[/tex].

I do not have much of a background for analysis, so any suggestions are welcome.
 
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  • #2
johnson123 said:
So [tex] A<\mu(\left\{f-f_{n}>0\right\}) + \mu(\left\{f_{n}>h\right\})

\leq\mu(\left\{f-f_{n}>0\right\}) + A [/tex].

Taking limits on both sides (n->00)yields:

[tex] A < 0 + A \Rightarrow\Leftarrow[/tex].

This conclusion doesn't seem right. If you know

[tex]
\lim_{n\to\infty} X_n = X,
[/tex]

[tex]
\lim_{n\to\infty} Y_n = Y,
[/tex]

and

[tex]
X_n < Y_n,\quad \forall\;n\in\mathbb{N},
[/tex]

then it only follows that

[tex]
X \leq Y.
[/tex]

The inequality [tex]X < Y[/tex] is not necessarily true. So in your case, the limit gives you an inequality

[tex]
A \leq 0 + A.
[/tex]
 
  • #3
johnson123 said:
From the triangle inequality for measures we get

[tex]\mu(\left\{f>h\right\}) = \mu(\left\{f-f_{n}+f_{n}>h\right\})

\leq\mu(\left\{f-f_{n}>0\right\}) + \mu(\left\{f_{n}>h\right\}) [/tex].

I don't think the "triangle inequality" was right either.

[tex]
\Big(\;f(x)-f_n(x) > 0\quad\textrm{and}\quad f_n(x)>h\;\Big)\quad\implies\quad f(x)-f_n(x) + f_n(x) > h
[/tex]

so

[tex]
\{x\in X\;|\; f(x)-f_n(x) > 0\}\;\cap\;\{x\in X\;|\; f_n(x)>h\} \;\subset\; \{x\in X\;|\; f(x)-f_n(x) + f_n(x) > h\}.
[/tex]

I don't see how you get that inequality from this. Of course it could be, that you did it some other way... I don't know about it at the moment. Perhaps you could explain in more detail how you thought that should work?
 
  • #4
Here are key steps of my proof.

Assume that there is [tex]\delta > 0[/tex] so that

[tex]
\mu(\{x\in X\;|\; f(x) > h\}) = A + \delta > A.
[/tex]

Prove that there exists [tex]\epsilon > 0[/tex] so that

[tex]
\mu(\{x\in X\;|\; f(x) > h + \epsilon\}) > A + \frac{\delta}{2}.
[/tex]

To accomplish this, to my knowledge the only way is to assume that such epsilon would not exist, and then use the standard result that if there is a sequence of measurable sets

[tex]
X_1\subset X_2\subset X_3\subset\cdots
[/tex]

then

[tex]
\mu(\bigcup_{n=1}^{\infty} X_n) = \lim_{n\to\infty} \mu(X_n),
[/tex]

and arrive at contradiction.

Then prove the inclusion

[tex]
\{x\in X\;|\; f(x) > h+\epsilon\} \subset \{x\in X\;|\; f_n(x) > h\}\;\cup\; \{x\in X\;|\; |f(x) - f_n(x)| \geq \epsilon\}.
[/tex]

This should be close to the goal.
 
Last edited:
  • #5
After proving the inclusion I get [tex] A + \delta/2 < A+\epsilon \Rightarrow \delta/2 < \epsilon[/tex]

From this how can I arrive at a contradiction.
 
  • #6
johnson123 said:
After proving the inclusion I get [tex] A + \delta/2 < A+\epsilon \Rightarrow \delta/2 < \epsilon[/tex]

You don't get precisely that very naturally.

First: How far did you get in proving that the epsilon originally exists? I skipped all details of the proof in homework helping spirit, but it is a highly non-trivial proof.
 
  • #7
I agree it is not a simple fact. I did not thoroughly go through that result, I am more interested in seeing the idea(punchline) in the proof you kindly posted.
 
  • #8
hmhmhm... I'm not sure what I should add to this anymore. It's not allowable to give complete solutions here... and it's so relative that when hints are fine and when there is too much of them... hmhmh... :devil: Considering your earlier mistakes with limits and triangle inequality, IMO you just must put more time into this. You know. Fight with the problem for hours and hours, keep breaks, then come back to the problem and so on, and see how things progress :smile: That's the only way to learn.
 
  • #9
triangle inequality for measures.

Theorem: [tex]\mu ( \left\{ \Sigma f_{n} > \Sigma \epsilon_{n}\right\} ) \leq \Sigma \mu(\left\{ f_{n} > \epsilon_{n} \right\}) [/tex]

Proof: If for all n we have [tex] f_{n} \leq \epsilon_{n}[/tex] then

[tex]\Sigma f_{n}(x) \leq \Sigma \epsilon_{n} [/tex] and so

[tex] \bigcap \left\{f_{n} \leq \epsilon_{n} \right\} \subseteq \left\{ \Sigma f_{n} \leq \Sigma \epsilon_{n} \right\}[/tex]

This Implies [tex] \left\{ \Sigma f_{n} > \Sigma \epsilon_{n} \right\} = \left\{ \Sigma f_{n} \leq \Sigma \epsilon_{n} \right\}^{c} \subseteq ( \bigcap \left\{f_{n} \leq \epsilon_{n}\right\})^{c} = \bigcup \left\{f_{n} > \epsilon_{n} \right\} [/tex].
 
  • #10
Oh, sorry. The triangle inequality was right then. I hadn't seen it before.
 

1. What is Measure Theory?

Measure Theory is a branch of mathematics that deals with the study of measures, which are used to assign a numerical value to sets in a mathematical space. It provides a rigorous framework for understanding the concept of size or volume in a mathematical sense.

2. What are the main applications of Measure Theory?

Measure Theory has many important applications in various fields, such as probability theory, statistics, economics, and physics. It is also used extensively in other areas of mathematics, including real analysis, functional analysis, and topology.

3. What are the essential concepts in Measure Theory?

The main concepts in Measure Theory include measures, measurable sets, and integration. Other important concepts include sigma-algebras, measurable functions, and the Lebesgue measure, which is a specific type of measure used in many applications.

4. How is Measure Theory different from traditional calculus?

While traditional calculus deals with continuous functions and their derivatives, Measure Theory focuses on more general functions and their integrals. It also addresses the issue of measure, which is not typically considered in traditional calculus.

5. What are the challenges in studying Measure Theory?

One of the main challenges in studying Measure Theory is understanding the abstract nature of the subject and its reliance on set theory. It also requires a solid foundation in real analysis and topology. Additionally, some of the concepts and theorems in Measure Theory can be difficult to visualize or grasp intuitively.

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