Differentiability of Monotone Function's: Lebesgue's Theorem

In summary: So basically, you are saying that for any real number x, there exists a ball around x with a radius of at least r.
  • #1
Artusartos
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0
a)Does convergence imply being properly defined? So would it not be properly defined if it was divergent?

b)I am having trouble why the last part (in the attachment) says, "Then, by (1), [itex]f(x_0) - f(x) \geq dfrac{2^k} for all [itex]x < x_0[/itex]." But does (1) tell us that it's "equal" instead of "greater than or equal to"?

Thanks in advance
 

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  • #2
a) We define the function

[tex]f(x) = \sum_{q_n\leq x} \frac{1}{2^n}[/tex]

But if the series in the RHS diverges for a certain ##x##. Then we would have ##f(x)=+\infty##. But ##+\infty## is not a real number. We want ##f(x)## to be a real number for any ##x##. This is only satisfied if the series converge.

So yes, if we say that ##f(x)## is well-defined, then that actually means that the series converge.

b) I'm not quite understanding why you expect the equality to hold. You have

[tex]f(q_k) - f(x) = \sum_{x<q_n\leq q_k} \frac{1}{2^n} \geq \frac{1}{2^k}[/tex]

The inequality in the end of course holds because ##1/2^k## is a term in the sum on the LHS. So the inequality means that you drop every term in the sum except ##1/2^k##. I don't get why you think that this should be an equality.
 
  • #3
micromass said:
a) We define the function

[tex]f(x) = \sum_{q_n\leq x} \frac{1}{2^n}[/tex]

But if the series in the RHS diverges for a certain ##x##. Then we would have ##f(x)=+\infty##. But ##+\infty## is not a real number. We want ##f(x)## to be a real number for any ##x##. This is only satisfied if the series converge.

So yes, if we say that ##f(x)## is well-defined, then that actually means that the series converge.

b) I'm not quite understanding why you expect the equality to hold. You have

[tex]f(q_k) - f(x) = \sum_{x<q_n\leq q_k} \frac{1}{2^n} \geq \frac{1}{2^k}[/tex]

The inequality in the end of course holds because ##1/2^k## is a term in the sum on the LHS. So the inequality means that you drop every term in the sum except ##1/2^k##. I don't get why you think that this should be an equality.

Thanks, but I have one more question...

[tex]f(q_k) - f(x) = \sum_{x<q_n\leq q_k} \frac{1}{2^n} \geq \frac{1}{2^n} [/tex], right? But then how do we have [itex]|f(x)-f(x_0)| = \frac{1}{2^n} < \frac{1}{2^n}[/itex]?

I don't know, I think I'm confused about what n represents.
 
  • #4
Basically, for any finite n, you can construct a ball about xo that excludes all the

elements Sn:={q1,...,qn}, since min{ |xo-qi| i=1,2,..n} :=d >0

meaning, for finitely-many elements qi , the distance from xo to any of them is

non-zero, and the since the set Sn is finite, the set of distances from xo to any member in Sn has a minimum ( no need to worry about infs.) ,

say that minimum is called d.

Then, within the ball, say, B(x,d/2) , the only possible elements of C in the ball

are {q(n+1),q(n+2),...} . Now, by definition of f(x), can

you see why the difference can be made as small as possible (think of the elements of C between x and xo in the ball)?
 
Last edited:
  • #5
Bacle2 said:
Basically, for any finite n, you can construct a ball about xo that excludes all the

elements Sn:={q1,...,qn}, since min{ |xo-qi| i=1,2,..n} :=d >0

meaning, for finitely-many elements qi , the distance from xo to any of them is

non-zero, and the since the set Sn is finite, the set of distances from xo to any member in Sn has a minimum ( no need to worry about infs.) ,

say that minimum is called d.

Then, within the ball, say, B(x,d/2) , the only possible elements of C in the ball

are {q(n+1),q(n+2),...} . Now, by definition of f(x), can

you see why the difference can be made as small as possible (think of the elements of C between x and xo in the ball)?

Thanks a lot, but I'm not really familiar with "balls"...
 
  • #6
An open ball B(x,r) in the real line , i.e., x is any real number and d is a positive real is the set:

B(x,r):={ y in Reals : |x-y|<d }
 

Related to Differentiability of Monotone Function's: Lebesgue's Theorem

1. What is the definition of monotonicity?

Monotonicity refers to the property of a function where the values either increase or decrease in a consistent manner as the input increases. A function is considered monotonic if it is either strictly increasing or strictly decreasing.

2. What does it mean for a function to be differentiable?

A function is differentiable if it has a derivative at every point in its domain. This means that the function is smooth and has a well-defined slope at each point.

3. What is Lebesgue's Theorem?

Lebesgue's Theorem is a mathematical theorem that states that a monotone function is differentiable almost everywhere. This means that the set of points where the function is not differentiable has Lebesgue measure zero. In simpler terms, the non-differentiable points are negligible in terms of their contribution to the overall behavior of the function.

4. How is Lebesgue's Theorem used in mathematics?

Lebesgue's Theorem is a fundamental result in mathematical analysis and is used in various fields such as real analysis, functional analysis, and measure theory. It is particularly useful in proving the existence and properties of derivatives for monotone functions.

5. What are some examples of monotone functions?

Some examples of monotone functions include linear functions, exponential functions, and power functions. These functions have a consistent pattern of increasing or decreasing values as the input increases. However, it is important to note that not all monotone functions are differentiable, as they may have discontinuities or sharp corners in their graphs.

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