Lipschitz Continuity and measure theory

In summary, all schools are closed for the winter break, but Hurkyl is currently working on a problem and is not sure how to begin. There is a positive M such that |F(x)-F(y)| <= M|x-y|, and there exist a constant C such that m(F(E)) <= C(m(E)) for every Lebesgue measurable set E of [0,00].
  • #1
bham10246
62
0
Hi, this is not a homework problem because as you can see, all schools are closed for the winter break. But I'm currently working on a problem and I'm not sure how to begin to attack it. Here's the entire problem:

Let f be bounded and measurable function on [0,00). For x greater than or equal to 0, define F(x)= \int_{0,...,x} f(t)dt.

Part i. Show that there is some positive M such that |F(x)-F(y)| <= M|x-y|.

Part ii. Prove that there exist a constant C such that m(F(E)) <= C(m(E)) for every Lebesgue measurable set E of [0,00). Note that m(E) is the Lebesgue measure of the set E and F(E) := {F(x) : x is in E}.


I know how to do Part i. But as for Part ii., I'm not sure how to begin working on it. Can I rewrite E as an infinite disjoint union of open/closed sets??


Note that 00 means infinity and <= means less than or equal to.

Thank you.
 
Physics news on Phys.org
  • #2
Can I rewrite E as an infinite disjoint union of open/closed sets??
Yes, but not in a useful way. For example, the Cantor set requires an uncountable union.


I don't see the proof immediately, but I can see how to do a simpler case. What if f is continuous, and E sufficiently nice? (And, thus, F is differentiable) Remember that [itex]m(S) = \int_S 1 \, dx[/itex].
 
Last edited:
  • #3
Thanks Hurkyl. I forgot about the Cantor set. I'll think about it some more but I still don't see it.

We have to find C so that the above inequality works, and I think we're supposed to use Part i.

Hmm... maybe if we begin by assuming that E is an open set, then |F(x)-F(y)| <= m(F(E))? At this moment I'm not sure how m(E) fits in with m(F(E)).

Also, whether f is continuous or not, F is absolutely continuous. So F is always differentiable, isn't it?
 
  • #4
F is almost everywhere differentiable, IIRC. (but not necessarily everywhere differentiable)


The starting point that I'm imagining is that

[tex]\int_{F(E)} 1 \, dx = \int_E f(x) \, dx.[/tex]

But that doesn't work in the most general case.

I think you see something similar; did you mean to say that you're assuming E to be the interval (y, x)? In that case, m(E) = |x - y|, which is how you can get the desired relationship.
 
  • #5
Start by proving it for an open interval, then an open set, then an arbitrary measurable set.
 
  • #6
If it holds for E an open interval, then it holds for E a countable union of disjoint open intervals by subadditivity:

[tex]m(F(\cup E_i)) = m(\cup F(E_i)) \leq \sum mF(E_i) \leq \sum Cm(E_i) = C\sum m(E_i) = cm(E)[/tex]

Thus it holds for any open set. Thus it holds for any countable intersection of nested open sets, since:

[tex]m(F(\cap U_i)) \leq m(\cap F(U_i)) = \mbox{inf} mF(U_i) \leq \mbox{inf} Cm(U_i) = C\mbox{inf} m(U_i) = Cm(\cap U_i)[/tex]

Thus it holds for any [itex]G_{\delta}[/itex] set. But every measurable set is just a [itex]G_{\delta}[/itex] set unioned with a zero set, so it should hold for any measurable set.

To get the result to hold on an open interval, separate f into its positive and negative parts, and you should get:

[tex]m(F((x,y)) \leq \int _x^yf^+(t)dt + \int _x^yf^-(t)dt = |F^+(y) - F^+(x)| + |F^-(y) - F^-(x)| \leq M^+|x-y| + M^-|x-y| = (M^+ + M^-)m((x,y))[/tex]
 
  • #7
To clarify, the positive part of f is f+, which is defined as follows: f+(x) = f(x) when f(x) is positive, and f+(x) = 0 when f(x) is negative. [itex]F^+(x) = \int _0 ^x f^+(t)dt[/itex]. Clearly, the positive and negative parts of f are bounded and measurable, so part i applies to them, and it is by applying part i to these to functions that we get M+ and M- (you might have been wondering where those numbers came from).

Note, the first inequality in the last line might need some explanation, but you should be able to get it for yourself. Unfortunately, I don't have time now to elaborate.
 
  • #8
Thanks so much! I think I can go from here. Actually, I thought about it on my way out and I figured it out. Thanks so much everyone!
 

1. What is Lipschitz continuity?

Lipschitz continuity is a mathematical concept used to describe the behavior of a function. A function is considered Lipschitz continuous if there exists a constant value, called the Lipschitz constant, that bounds the ratio of the change in the function's output to the change in its input. In simpler terms, this means that the function's rate of change is not too steep or too flat.

2. How is Lipschitz continuity related to measure theory?

In measure theory, Lipschitz continuity is used to define a special type of function called a Lipschitz function. This type of function has the property that the measure of its image set (or range) is no greater than the measure of its domain set (or input set) multiplied by the Lipschitz constant. This is important because it helps to establish the relationship between the size or length of sets and the behavior of functions that map between them.

3. What are some real-world applications of Lipschitz continuity?

Lipschitz continuity has many practical applications, particularly in the fields of mathematics, physics, and engineering. In physics, it is used to study the stability of dynamical systems and the behavior of physical processes. In engineering, it is used to analyze the stability and convergence of numerical methods. In mathematics, it is used to prove the existence and uniqueness of solutions to differential equations and other mathematical problems.

4. Can a function be Lipschitz continuous but not differentiable?

Yes, a function can be Lipschitz continuous without being differentiable. In fact, there are many examples of functions that are Lipschitz continuous but not differentiable, such as the absolute value function and the Weierstrass function. This is because Lipschitz continuity only requires that the function's rate of change be bounded, while differentiability requires that the function's rate of change be continuous.

5. How is Lipschitz continuity different from uniform continuity?

Lipschitz continuity and uniform continuity are both types of continuity, but they differ in their definitions and properties. Lipschitz continuity involves a constant ratio between the change in the function's output and the change in its input, while uniform continuity involves a constant difference between the function's output for any two points in its domain. Additionally, Lipschitz continuity implies uniform continuity, but the reverse is not necessarily true.

Similar threads

Replies
3
Views
1K
Replies
4
Views
746
Replies
3
Views
2K
Replies
3
Views
195
Replies
1
Views
934
Replies
3
Views
326
Replies
5
Views
1K
  • Calculus
Replies
3
Views
909
  • Differential Equations
Replies
5
Views
653
Back
Top