Half of the real numbers, homogenously

In summary: I think it's because the outer measure is not additive. You can't assume that m^*(X_{10^{10^{100000000}}}) is the sum of those intervals.But can't you use some kind of an induction?First you can find a set that has the property approximately with arbitrary preciseness, for example by choosing the first n intervals, n large enough, and then picking the first n+1 intervals, and so on. Then you could use such a set as a base for an induction, and then you could use the inductive hypothesis for example in this way:The set X_n \cap [0,t] has the property m^*(X_n \cap [0,t]) = \
  • #1
jostpuur
2,116
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Does there exist a set [tex]X\subset\mathbb{R}[/tex] that has a property

[tex]
m^*(X\cap [0,x]) = \frac{x}{2},\quad\quad\forall x>0,
[/tex]

where [tex]m^*[/tex] is the Lebesgue outer measure?

My own guess is that this kind of X does not exist, but I don't know why. Anybody knowing proof for the impossibility of this X?
 
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  • #2
Loosely speaking most sets of positive measure are very close to being unions of intervals and in some sense if [tex] m(X) > 0[/tex] then [tex] m(X\cap[x-\epsilon,x+\epsilon]) \approx 2\epsilon[/tex] for "most" [tex] x\in X[/tex].

There clearly does not exist a X which is lebesgue measurable.
Assume the existence of such a X.
Since we have assumed X to be measurable, we can work with the lebesgue measure [tex]m()[/tex] instead of outer measures.

Now,
Choose any [tex] 0 \leq a < b [/tex]. Then [tex] m (X \cap [0,b]) = m(X \cap [0,a]) +m(X \cap [a,b]) - m(X \cap \{a\})[/tex]
hence,
[tex] m(X \cap [a,b]) = \frac{b-a}{2} [/tex]

and since points have measure 0, it follows that
[tex] m(X \cap [a,b]) =m(X \cap (a,b])=m(X \cap (a,b))=m(X \cap [a,b)) =\frac{b-a}{2} (1) [/tex]

The proof hinges on the following: Given any measurable set E and an arbitary [tex] \epsilon > 0[/tex], there exists an open set O such that
[tex]E \subseteq O[/tex] and [tex] m(O) \leq m(E) + \epsilon [/tex] (note: corrected after jostpuur pointed out a typo)

So choose such an O for X.

O being open can be written as [tex] O=\cup_{i\in A}^{\infty} O_i [/tex] where [tex] O_i [/tex] are disjoint open intervals in [tex][0,\infty)[/tex] and A is a finite or countable index set, clearly [tex] m(O)= \sum_{i\in A} m(O_i)[/tex].

From (1) we have for all i, [tex] m(X \cap O_i) = m(O_i)/2 [/tex]

Clearly, [tex] X = X \cap O = \cup_{i\in A} X\cap O_i[/tex]
and hence,
[tex] m(X) = \sum_{i\in A} m(X\cap O_i) = \sum_{i\in A} m(O_i)/2 = m(O)/2 \leq (m(X)+\epsilon)/2[/tex]

and we get
[tex] m(X) \leq \epsilon [/tex]

Since [tex]\epsilon > 0[/tex] was arbitary we must have [tex] m(X) = 0 [/tex]

In which case we also must have [tex] m(X\cap[0,x]) = 0 [/tex] for all x.

A contradiction. So no such X exists.

I won't be surprised if the result holds for arbitrary X.
 
  • #3
I see. Isn't it also true, that for arbitrary set [tex]X\subset\mathbb{R}[/tex], there exists a measurable set [tex]\overline{X}[/tex] such that [tex]X\subset\overline{X}[/tex] and [tex]m^*(X)=m(\overline{X})[/tex], so that that deals with the non-measurable case?

edit: Or it could be that this idea starts to lead towards unnecessarily complicated proof. Perhaps one could start dealing with the open covers, used in the definition of the outer measure, directly?

I suppose it would be safest to replace the original [tex]X[/tex] with for example [tex]X\cap [0,1][/tex], so that its outer measure becomes finite. It is sufficient to hypothesize the property [tex]m^*(X\cap[0,t])=t/2[/tex] for 0<t<1.
 
Last edited:
  • #4
I noticed a subtle mistake in my proof which can be corrected, jostpuur perhaps you can figure out how to correct it.

[tex]
m(X) \leq (m(X)+\epsilon)/2
[/tex]

implies, [tex] m(X) \leq \epsilon [/tex] if [tex] m(X) < \infty [/tex]

So the proof only holds for measurable sets of finite measure and clearly measure of X cannot be finite.
 
  • #5
Yeah, I just managed to mention about that. It is not a problem, because we can carry out the proof "locally". If the X, that was described in the first post, existed, we could just take its intersection with some finite interval, and it would still have the same strange behavior locally there.


Then there was a typo here:

acarchau said:
The proof hinges on the following: Given any measurable set E and an arbitary [tex] \epsilon > 0[/tex], there exists an open set O such that
[tex]E \subseteq O[/tex] and [tex] m(E) \leq m(O) + \epsilon [/tex]

The intented inequality must have been

[tex]
m(O) \leq m(E) + \epsilon
[/tex]
 
  • #6
jostpuur said:
Yeah, I just managed to mention about that. It is not a problem, because we can carry out the proof "locally". If the X, that was described in the first post, existed, we could just take its intersection with some finite interval, and it would still have the same strange behavior locally there.


Then there was a typo here:



The intented inequality must have been

[tex]
m(O) \leq m(E) + \epsilon
[/tex]

You are correct, thanks!
 
  • #7
acarchau said:
You are correct, thanks!

Hehe. Thanks to you for the proof :smile:
 
  • #8
I think I managed modifying the proof so that it can be carried out with outer measures. This equation

acarchau said:
[tex] m(X) = \sum_{i\in A} m(X\cap O_i)[/tex]

would require measure, but outer measure has the property

[tex]
m^*(\bigcup_{n=1}^{\infty} A_n) \leq \sum_{n=1}^{\infty} m^*(A_n)
[/tex]

which is enough for the proof.

So if there is [tex]X\subset [0,1][/tex] with the property [tex]m^*(X\cap [0,t])=t/2[/tex] for all [tex]0<t<1[/tex], then let [tex]\epsilon >0[/tex] be arbitrary. By definition of the outer measure, and by the fact that if two open intervals are not disjoint, then they together are one interval, there exists a sequence of open intervals [tex]]a_k,b_k[[/tex], [tex]k=1,2,3,\ldots[/tex], so that

[tex]
X\subset \bigcup_{k=1}^{\infty}\; ]b_k,a_k[
[/tex]

and

[tex]
\sum_{k=1}^{\infty} (b_k-a_k) \leq m^*(X) + \epsilon.
[/tex]

Then

[tex]
X = \bigcup_{k=1}^{\infty} \big(X\cap \;]b_k,a_k[\big)
[/tex]

so that by the property mentioned earlier we get

[tex]
m^*(X) \leq \sum_{k=1}^{\infty} m^*(X\cap\; ]b_k,a_k[) = \sum_{k=1}^{\infty} \frac{b_k-a_k}{2} \leq \frac{1}{2}(m^*(X) \;+\; \epsilon).
[/tex]

Isn't this right now too?

hmhmh... I see some little difficulties. The equation

[tex]
m^*(X\cap \;]b_k,a_k[) = \frac{1}{2}(b_k-a_k)
[/tex]

requires some explanation, and it could happen that some of these intervals go outside the [tex][0,1][/tex]... but these don't look fatal problems.
 
Last edited:
  • #9
the approach appears sound
 
  • #10
I'm still puzzled by this all. If I define following sequence of sets:

[tex]
X_1 = [0,\frac{1}{2}[
[/tex]

[tex]
X_2 = [0,\frac{1}{4}[\;\cup\;[\frac{1}{2},\frac{3}{4}[
[/tex]

[tex]
X_3 = [0,\frac{1}{8}[\;\cup\;[\frac{1}{4},\frac{3}{8}[\;\cup\;[\frac{1}{2},\frac{5}{8}[\;\cup\;[\frac{3}{4},\frac{7}{8}[
[/tex]

...

Then for example [tex]m^*(X_{10^{10^{100000000}}}\cap [0,t])\approx \frac{t}{2}[/tex] would be true to great accuracy. One might think, that if you can device a set that has that property approximately, but with arbitrary preciseness, then we could also device, as some kind of limit, a set that has this property precisely :confused:

What's happening there?
 
  • #11
Is [tex]X_n[/tex] the set of all x in [0,1] whose nth bit, after the "decimal" point, is 0 in the binary representation?
 
  • #12
jostpuur said:
What's happening there?
Whatever notion of limit you come up with, at least one of the following will be true:

1. The limit isn't a set, but instead some generalized object
2. Lesbegue outer measure won't be continuous
3. The limit won't exist
 
  • #13
My take is this:

1. [tex]X_n[/tex] is constructed by dividing [0,1] into [tex]2^n[/tex] equal intervals and dropping alternate ones, the idea being that only half of any "good" set lies in [tex]X_n[/tex], upto a resolution. This makes [tex] X_n [/tex] to be the set of points having their nth bit 0, it is a disjoint union of [tex]2^{n-1}[/tex] intervals having dyadic rationals as their endpoints.

2. Limit of [tex]X_n[/tex] does not exist, for instance 0.1010101010... lies in [tex]X_2, X_4 \ldots [/tex] but not in [tex]X_1, X_3 \ldots[/tex]

3. [tex] \sup_{t \in [0,1]} | m(X_n \cap [0,t]) -t/2 | \leq \frac{1}{2^n} [/tex], hence [tex] \lim_{n \to \infty} \sup_{t \in [0,1]} | m(X_n \cap [0,t]) -t/2 | = 0 [/tex]
 
  • #14
jostpuur said:
By definition of the outer measure, and by the fact that if two open intervals are not disjoint, then they together are one interval, there exists a sequence of open intervals [tex]]a_k,b_k[[/tex], [tex]k=1,2,3,\ldots[/tex], so that

The remark with disjointness was useless, btw. It stayed there from the previous proof that did not use outer measures.

Hurkyl said:
Whatever notion of limit you come up with, at least one of the following will be true:

1. The limit isn't a set, but instead some generalized object
2. Lesbegue outer measure won't be continuous
3. The limit won't exist

Very clear thinking.
 

1. What does it mean for half of the real numbers to be homogenously distributed?

Homogeneous distribution refers to a group of numbers being evenly spread out, with no particular pattern or clustering. Therefore, half of the real numbers being homogeneously distributed means that the numbers are randomly distributed and not skewed towards any specific value or range.

2. How is homogeneity determined in a set of real numbers?

Homogeneity can be determined by calculating the standard deviation of the numbers. A low standard deviation indicates a homogeneous distribution, while a high standard deviation suggests a non-homogeneous distribution.

3. Can half of the real numbers ever be perfectly homogeneously distributed?

No, it is highly unlikely for half of the real numbers to be perfectly homogeneously distributed. This is because true randomness is not achievable in a finite set of numbers. There will always be some degree of variation or clustering present.

4. What are the practical applications of studying homogeneity in real numbers?

Studying homogeneity in real numbers can be useful in various fields such as statistics, economics, and data analysis. It can help identify any biases or trends in a dataset and ensure that statistical analyses are accurate and valid.

5. How does homogeneity in real numbers affect data analysis?

In data analysis, homogeneity ensures that any conclusions or predictions made from the data are representative of the entire dataset. If the data is not homogeneous, it can lead to skewed or inaccurate results. Therefore, understanding and accounting for homogeneity is crucial in data analysis.

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