Proving Null Sets: Lebesgue Measure and Lipschitz Functions

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In summary, the two questions are:1. If E is a set such that m^*(E)=0, prove that m^*(E^2)=0.2. If f is a K-Lipschitz function, show that m^*(E^2)≤Km^*(E) for all E\subset\mathbb{R}.
  • #1
TheBigBadBen
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I have a final coming up, so I thought I'd post some of my review questions as a way of checking my work. I think I have a working answer for this one, but I'm not sure it's totally right. I'll post it upon request.

At any rate, two related questions:

(1)
Suppose that \(\displaystyle E \subset \mathbb{R}\) is a set such that \(\displaystyle m^*(E)=0\). Prove that \(\displaystyle m^*(E^2)=0\), where \(\displaystyle E^2 = \{x^2|x\in E\}\)

(2)
Suppose that \(\displaystyle f:\mathbb{R}\rightarrow\mathbb{R}\) is a K-Lipschitz function. Show that \(\displaystyle m^*(E^2)≤Km^*(E)\) for all \(\displaystyle E\subset\mathbb{R}\)

Note that \(\displaystyle m^*\) refers to the Lebesgue outer-measure.
 
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  • #2
I think it would be better if you post your work, and then our helpers can give you a critique.

You are more likely to get help this way rather than to request proofs be given to which you can compare your work. :D
 
  • #3
MarkFL said:
I think it would be better if you post your work, and then our helpers can give you a critique.

You are more likely to get help this way rather than to request proofs be given to which you can compare your work. :D

Fair enough. My proof for the first:

We define the outer measure by

\(\displaystyle m^*(E)=inf \left\{ \sum_{n=1}^{∞}\ell(I_n):E\subset \cup_{n=1}^{∞} I_n \right\}\)

Where each \(\displaystyle I_n\) is an open interval. Since the outer measure of E is 0, we can state that for any \(\displaystyle \epsilon >0\), we can equivalently find a collection of sets {I_n} so that the sum of their lengths is less than \(\displaystyle \epsilon\) and such that E is contained in their union.

We begin with the case that \(\displaystyle E\subset [0,\infty)\)

Consider any \(\displaystyle \epsilon>0\). There exists a collection of sets {I_n} so that the sum of their lengths is less than \(\displaystyle \sqrt{\epsilon}\) and such that E is contained in their union. We note that for any \(\displaystyle x\in I_n\), we have \(\displaystyle x^2 \in I_n^2\). It follows that \(\displaystyle E^2\subset \cup_{n=1}^{∞} I_n^2\).

Now, for \(\displaystyle I_n=(a_n,b_n)\), we note that \(\displaystyle I_n^2=(a_n^2,b_n^2)\), from which it follows that
\(\displaystyle \ell(I_n^2)=b^2-a^2 = (b - a)(b + a) \geq (b - a)^2 = \ell(I_n)^2\)
^^^^
Not as useful as I thought...It follows that\(\displaystyle \sum_{n=1}^{\infty}\ell(I_n^2)≤\)
\(\displaystyle \sum_{n=1}^{\infty}\ell(I_n)^2≤\) <--- this is wrong
\(\displaystyle \left[ \sum_{n=1}^{\infty}\ell(I_n) \right]^2<\)
\(\displaystyle \left[ \sqrt{\epsilon} \right]^2=\epsilon\)We conclude that for \(\displaystyle E\subset [0,\infty)\), \(\displaystyle m^*(E^2)=0\).

Takes a while to type up...
Any critique so-far? Nit-picking is welcome here!
I think I should be able to generalize this relatively easily to the entire real line, but if anybody has a cleverer method I readily welcome that.

**So I found a flaw in my own proof, where I try to establish the inequality. Ends up, I have it going the wrong way. I need to find a way to fix that.**
 
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  • #4
My idea for the proof for the other one is similar. The idea is that if f is Lipschitz, then for any interval I, we should have
\(\displaystyle m^*(f(I))≤K\ell(I)\)
Still not sure how to put it all together.
 
  • #5
Figured out the solution to my problem. I had tried to come up with some sort of inequality, and wrote:

TheBigBadBen said:
We begin with the case that \(\displaystyle E\subset [0,\infty)\)...

Now, for \(\displaystyle I_n=(a_n,b_n)\), we note that \(\displaystyle I_n^2=(a_n^2,b_n^2)\), from which it follows that
\(\displaystyle \ell(I_n^2)=b^2-a^2 = (b - a)(b + a) \geq (b - a)^2 = \ell(I_n)^2\)

Which, as you can see later, is the wrong sort of inequality for what I wanted to do. As it ends up, the easier thing to do is to start by looking at bounded intervals, eventually noting that the arbitrary union of null sets is a null set. So, I'd have something like:

Define \(\displaystyle E_N = E \cap [0,N], N\in \mathbb{N}\). We note that
\(\displaystyle I_n^2=(a^2,b^2)\), and
\(\displaystyle \ell(I_n^2)=b^2-a^2 = (b - a)(b + a) \leq 2N(b-a) = 2N\ell(I_n)\)

From there, it's easy enough to use cascading inequalities to show that an interval covering \(\displaystyle \{I_n\}_{n\geq 1}^2\) of \(\displaystyle E_N^2\) can be made arbitrarily small. Following a similar logic, we can do this for an integer \(\displaystyle -N\), defining

\(\displaystyle E_{-N} = E \cap [-N,0], N\in \mathbb{N}\)

And producing the same result. Since \(\displaystyle E^2\) is the union of all sets \(\displaystyle E_N^2\), we deduce that \(\displaystyle E^2\) is the countable union of null sets, and hence is itself a null set.

I'm pretty happy with this proof, but if someone can offer something better, I'm listening.
 

FAQ: Proving Null Sets: Lebesgue Measure and Lipschitz Functions

What is Lebesgue Measure?

Lebesgue Measure is a mathematical concept used to measure the size or volume of a set in n-dimensional Euclidean space. It was developed by French mathematician Henri Lebesgue in the early 20th century as a way to generalize the concept of length and area to higher dimensions.

What sets can be measured using Lebesgue Measure?

Lebesgue Measure can be used to measure any subset of n-dimensional Euclidean space, as long as the set is measurable. This means that it must satisfy certain mathematical properties, such as being countable and having a well-defined boundary.

How is Lebesgue Measure different from other measures?

Lebesgue Measure differs from other measures, such as the Riemann Measure, in that it takes into account the entire space rather than just the individual points. This allows it to measure more complicated sets, such as fractals and non-continuous functions.

What is the importance of Lebesgue Measure in mathematics?

Lebesgue Measure is an important tool in mathematical analysis and is used in various fields such as probability, differential equations, and functional analysis. It provides a more general and powerful way to measure sets and has many applications in real-world problems.

How is Lebesgue Measure calculated?

Lebesgue Measure is calculated by partitioning the set into smaller measurable subsets and summing their individual measures. This process is known as integration, and it involves taking the limit of the sum as the size of the partitions approaches zero. This allows for a more precise and accurate measurement of the set's size.

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