Hard MVT theorem proof Tutorial Q7

In summary, the "Hard MVT theorem proof Tutorial Q7" provides a detailed exploration of the Hard Mean Value Theorem, focusing on its application and proof strategies. It emphasizes the conditions under which the theorem holds, offers examples to illustrate the concepts, and guides the reader through the logical steps necessary to understand and apply the theorem effectively. The tutorial aims to reinforce comprehension through practice problems and clarifications of common misconceptions.
  • #1
TanWu
17
5
Homework Statement
Throughout this Tutorial Q6 to Q9 let ##c: \mathbb{R} \rightarrow \mathbb{R}## be a differentiable function whose derivative ##c^{\prime}## is continuous at 0 with ##c^{\prime}(0)=1##.
Relevant Equations
##c^{\prime}(0)=1##
The tutorial question I am working on is,
1714949809233.png

(a) Attempt
We can use mean value theorem since

##(c: \mathbb{R} \rightarrow \mathbb{R}~countinity ) \implies (c: [-d, d] \rightarrow [c(-d), c(d)]~countinity)##

Thus ##c: [-d, d] \rightarrow [c(-d), c(d)] ## is differentiable on ##(-d, d)##, then there exists ##b \in (-d, d)## such that ##c'(b) = \frac{c(-d) - c(d)}{-d - d} = \frac{f(-d) - f(d)}{-2d}##

Using result from Q6,
##b \in (-d, d) \implies \frac{1}{2} < \frac{f(-d) - f(d)}{-2d} < \frac{3}{2}##

Not sure how to prove from here.

(b) Attempt
##\frac{2}{3}(t - s) < e(t) - e(s) < 2(t - s)##
##\frac{2}{3}(t - s) + e(s) < e(t) < 2(t - s) + e(s)##

However, this is far from the expression that I am trying to prove.

(c) Nothing yet (may rely on (a) and (b))

I express gratitude to those who help
 
Last edited by a moderator:
Physics news on Phys.org
  • #2
TanWu said:
Using result from Q6, ##b \in (-d, d) \implies \frac{1}{2} < \frac{f(-d) - f(d)}{-2d} < \frac{3}{2}##
How did you arrive at this inequality? You don't show Q6, so it's hard to tell where it comes from.
Also, it doesn't seem that you have used the given information that f'(0) = 1.
 
  • Like
Likes TanWu
  • #3
Mark44 said:
How did you arrive at this inequality? You don't show Q6, so it's hard to tell where it comes from.
Also, it doesn't seem that you have used the given information that f'(0) = 1.
Thank you Sir. My apologies the Q6 I posted is awaiting approval. There was a typo, it is meant to be c'(0) = 1. (The derivative of the function c at 0). The result I am using is from Q6.
1714954424559.png
 
  • #4
You can use the chain rule to express the derivative of e in terms of the derivative of c. This lets you put bounds on the derivative of e. Try starting with that and then see how the mean value theorem helps
 
  • Like
Likes TanWu
  • #5
Please avoid using some ambiguous word soup as a title. None of us know what Hard or Medium hard means and which Q7 of which tutorial it is. The only helpful part of the title currently is "MVT".
 
  • Like
Likes TanWu
  • #6
Office_Shredder said:
You can use the chain rule to express the derivative of e in terms of the derivative of c. This lets you put bounds on the derivative of e. Try starting with that and then see how the mean value theorem helps
nuuskur said:
Please avoid using some ambiguous word soup as a title. None of us know what Hard or Medium hard means and which Q7 of which tutorial it is. The only helpful part of the title currently is "MVT".
Both sir thank you.

Using your hint,

##(a)##
Let ##e(t) = x## and ##e(s) = y##, and we perform change of variables for the definition of inverse function.
Thus ##e(c(v)) = v## and ##c(e(w)) = w##

Thus,

##e(c(v)) = v## and ##e(t) = x## imply ##t = c(v)## and ##x = v## which then imply ##t = c(x)##

##e(c(v)) = v## and ##e(s) = y## imply ##s = c(v)## and ##y = v## which then imply ##s = c(y)##

Now we find difference quotient for inverse function, we call this difference quotient a speical name the MVT. ##e(t)##:

##\frac{e(t) - e(s)}{t - s} = \frac{x - y}{c(x) - c(y)} = \frac{1}{\frac{c(x) - c(y)}{x - y}} = \frac{1}{c'(z)}## where since this a difference quotient, ##x < z < y##

Then using for all ##z \in (-d,d)##, we have ##\frac{1}{2} < c'(z) < \frac{3}{2}##

Then inverting the inequality,

##\frac{1}{\frac{1}{2}} > \frac{1}{c'(z)} > \frac{1}{\frac{3}{2}}##

##2 > \frac{1}{c'(z)} > \frac{2}{3}##

##\frac{2}{3} < \frac{e(t) - e(s)}{t - s} < 2##

QED.

Whew, That is a tiresome proof.
 
  • #7
(b)

##\frac{2}{3} < \frac{e(t) - e(s)}{t - s} < 2 \implies \frac{e(t) - e(s)}{t - s} < 2##

Then take absolute value of each side,

##\frac{|e(t) - e(s)|}{|t - s|} < |2|##

Which is equivalent to

##\frac{|e(t) - e(s)|}{|t - s|} < 2##

With some rearrangement, we get

##-2|t - s| + e(s) < e(t) < 2|t - s| + e(s)##

QED.
 
  • #8
For ##(c)##,

For ##e(c(-d), c(d)) \rightarrow (-d,d)## to be continuous, it must be right continuous at ##c(d)## and left hand continuous at ##c(-d)## and uniformly continuous on ##(-d,d)##.

That is, ##\lim_{t \rightarrow [c(- d)^+]} e(t) = e(c(-d))= -d## and ##\lim_{t \rightarrow [c(d)^-]} e(t) = e(c(d))= d## from the definition of e as the inverse of c on ##(-d,d)##.

To prove that uniform continuity on ##t \in (c(-d), c(d))##, since we know that, ##|e(t) - e(s)| < 2|t - s|## this implies that ##|e(t) - e(s)| \le C|t - s|## where ##c < 2 \in \mathbb{R}##.

Take ##C = 1.5## Then ##|e(t) - e(s)| \le 1.5|t - s|## thus e is lipschitz continuous and thus continuous on the interval as required. QED.

I assume if no one replies my proof is correct for (a) and (c) which I still have slight doubts about.
 
  • #9
If e is a function from ##(-c(d),c(d))\to \mathbb{R}## then since the domain is an open interval the endpoints aren't in the domain. Hence proving it's continuous at the endpoints makes no sense - the function isn't even defined at those points
 
  • Like
Likes TanWu
  • #10
Office_Shredder said:
If e is a function from ##(-c(d),c(d))\to \mathbb{R}## then since the domain is an open interval the endpoints aren't in the domain. Hence proving it's continuous at the endpoints makes no sense - the function isn't even defined at those points
Thank you Sir that is a good point, I forgot that right/left continuity is only for half closed/open interval or closed interval. So one only needs lipschitz continuity to prove uniform continuity in this instance. Do you know of any other ways to prove uniform continuity without using lipschitz continuity since I have not really studied that yet much.
 
  • #11
I agree that this function is uniformly continuous, but you said it "must" be uniformly continuous and it's not true that continuous functions are uniformly continuous on open intervals in general.

But I do think the thing you did is correct. No need for word salad with different forms of continuity - it's continuous at s because for any epsilon if ##|s-t|<\epsilon/2## then ##|e(s)-e(t)|<\epsilon##
 
  • Like
Likes TanWu
  • #12
Office_Shredder said:
I agree that this function is uniformly continuous, but you said it "must" be uniformly continuous and it's not true that continuous functions are uniformly continuous on open intervals in general.

But I do think the thing you did is correct. No need for word salad with different forms of continuity - it's continuous at s because for any epsilon if ##|s-t|<\epsilon/2## then ##|e(s)-e(t)|<\epsilon##
Thank you Sir. Would you know why you set ##|s-t|<\epsilon/2##. I mean like why use ##\epsilon/2## instead of ##\delta##? It is because for continuity and limit proofs ##\delta \le \epsilon## so you were just expressing ##\delta(\epsilon )## (Delta as a function of epsilon)?
 
  • #13
TanWu said:
Thank you Sir. Would you know why you set ##|s-t|<\epsilon/2##. I mean like why use ##\epsilon/2## instead of ##\delta##? It is because for continuity and limit proofs ##\delta \le \epsilon## so you were just expressing ##\delta(\epsilon )## (Delta as a function of epsilon)?

I was expressing ##\delta## as a function of ##\epsilon##.

It is not true that ##\delta \leq \epsilon## in general
 
  • Like
Likes TanWu

FAQ: Hard MVT theorem proof Tutorial Q7

What is the Hard MVT theorem?

The Hard Mean Value Theorem (Hard MVT) is a generalization of the classical Mean Value Theorem. It states that under certain conditions, there exists at least one point in the interval where the derivative of a function equals the average rate of change of the function over that interval. This theorem is often used in advanced calculus and analysis to establish properties of functions.

What are the prerequisites for understanding the Hard MVT theorem proof?

To understand the proof of the Hard MVT theorem, one should have a solid grasp of calculus concepts, particularly derivatives, continuity, and the classical Mean Value Theorem. Familiarity with advanced topics such as differentiability and the properties of real-valued functions is also beneficial.

How is the Hard MVT theorem different from the classical Mean Value Theorem?

The Hard MVT theorem extends the classical Mean Value Theorem by relaxing some of the conditions required for its application. While the classical MVT requires the function to be continuous on a closed interval and differentiable on an open interval, the Hard MVT may involve more complex conditions and can apply to a wider class of functions, including those that are not necessarily differentiable everywhere.

Can you provide a brief outline of the proof for the Hard MVT theorem?

The proof of the Hard MVT theorem typically involves constructing an auxiliary function that captures the essence of the average rate of change. By applying the classical Mean Value Theorem to this auxiliary function, one can establish the existence of a point where the derivative matches the average rate of change over the interval. The proof often requires careful consideration of the function's behavior at the endpoints and the use of limits.

What are some applications of the Hard MVT theorem in real analysis?

The Hard MVT theorem has several applications in real analysis, including proving the existence of solutions to differential equations, establishing properties of continuous functions, and analyzing the behavior of functions in optimization problems. It is also useful in understanding the relationship between a function and its derivatives, particularly in the study of monotonicity and concavity.

Back
Top