Proving Finite Lebesgue Measure of A When B Is Contained Within

In summary, the author is trying to prove that if an integrable function has a finite limit, then its integral must have a finite limit as well. He is using the idea of two unions being equal if their measures are equal to each other. If he can find any flaws in his logic, or find any other contributions to the problem, he would be more than welcome.
  • #1
mikesmith00
3
0
Is there anyway to prove that if I have (m is the lebesgue measure) m(A\B) is finite, then m(A) is finite? It seems intuitive to me, but I'm having trouble coming up with rigorous mathematical reasoning for it. B is completely contained within A. If there's anything else that might clarify this, let me know, because this is bugging me. Thanks for any help that you can offer.
 
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  • #2
The reason you're having trouble proving it is that it's not true (at least when you consider the Lebesgue measure on the whole real line). Consider the trivial example of A=all reals and [tex]B = (-\infty,0)\cup(0,\infty)[/tex]. For which m(A\B)=m({0})=0, but A has infinite measure. For a somewhat less trivial example take A=all reals and B=all irrationals. It is very important to note that although the "identity" m(A\B)=m(A)-m(B) seems plausible and intuitive, it fails in many infinite measure spaces.
 
  • #3
Thank you. I'm working on getting ready for my real analysis comps, and trying to go through all of the books that were listed on the syllabus for the exam. This was part of a question in one of the books that I'm working on (Stein and Shakarachi vol 3) and I'm trying to show that if f is integrable, then [tex]\sum m(E_{2^{k}}) < \infty[/tex] summed from [tex] k= -\infty[/tex] to [tex]\infty[/tex] with [tex] E_{2^{k}}=\left\{x:f(x)>2^{k}\right\} [/tex]. My first attempt was that I was able to show that if I found disjoint sets [tex] F_{k} = E_{2^{k}}{\setminus}E_{2^{k+1}} [/tex] then [tex]\sum m(F_{k}) < \infty[/tex] summed from [tex] k= -\infty[/tex] to [tex]\infty[/tex]. I was trying to use this to show that if we know that sum F is finite, then sum E is finite. Looks like I might have to start on a different approach. Thanks for your help again.
 
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  • #4
I believe I have it solved. If we're taking the measure of the union, and I have two unions that are equal to each other, then the measures are equal, so then if the measure of one is finite, then the other must be finite as well. If you see any flaws in this logic, any contributions to this are more than welcome.
 

1. What is the definition of Finite Lebesgue Measure?

The Finite Lebesgue Measure is a mathematical concept that measures the size or volume of a set in terms of the length, area, or volume of its elements. It is used to quantify the "size" of a set in a way that is consistent across all dimensions.

2. How is Finite Lebesgue Measure different from other measures?

Finite Lebesgue Measure is different from other measures because it takes into account the geometric structure of the set being measured. It is also a more general measure that can be applied to any set in any dimension, unlike other measures that are limited to specific sets or dimensions.

3. How is the Finite Lebesgue Measure of a set A determined when another set B is contained within it?

The Finite Lebesgue Measure of set A when B is contained within it is determined by subtracting the measure of set B from the measure of set A. This is because the measure of set A includes the elements of set B, so those elements must be subtracted in order to get the measure of just set A.

4. Can the Finite Lebesgue Measure of a set be negative?

No, the Finite Lebesgue Measure of a set cannot be negative. It is always a non-negative real number, meaning it can be zero or a positive value. This is because the measure of a set cannot be less than nothing.

5. What is the significance of proving Finite Lebesgue Measure of A when B is contained within?

Proving the Finite Lebesgue Measure of A when B is contained within is important because it allows us to accurately measure the size of a set even when another set is contained within it. This is useful in a variety of mathematical and scientific fields, including probability, analysis, and geometry.

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