Proving Properties of Bounded Sets in Real Numbers

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In summary: I see. The goal is to show that ##\epsilon \sup(A)## is the least upper bound for B. So I have to show that ##\epsilon \sup(A) \leq \sup(B)##. I still can't do it. I am trying to show that there is a lower bound for ##\sup(B)##, which is ##\epsilon \sup(A)##, but I can't do it. I think I am getting confused by the fact that I am using the Completeness Axiom too much. I am not sure how to do it without using it. Could you give me another hint? Furthermore, I am not sure how perform the proof when ε
  • #1
CAF123
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Homework Statement


Prove:
1) a)If A is a nonempty bounded subset of ##\mathbb{R}## and B={##\epsilon x: x \in A##} then ##\sup{B} = \epsilon \sup(A),## where ##\epsilon > 0## any positive real number.

b) If A+B = {a+b, ##a \in A, b \in B##}, A, B nonempty bounded subsets of ##\mathbb{R}## then ##\sup(A+B) = \sup(A) + \sup(B)##

2)If ##x_n## converges, then ##x_n/n## converges.

The Attempt at a Solution



1) a) By Completeness axiom, ##\sup(A)## exists. Each element of B looks like ##\epsilon x_i##, each ##x_i \in A##. Write B = ##\epsilon##{x : ##x \in A##} = ##\epsilon A##. The supremum of A is an element of A and so let ##\sup(A) = x_o \in A##. So ##\sup(B) = \epsilon x_o = \epsilon (x_o) = \epsilon (sup(A))##.

b)##\sup(A)## and ##\sup(B)## exist by Completeness. By defintion, ##a \leq \sup(A) \forall a \in A,## and ##b \leq \sup(B) \forall b \in B##. So the largest element ##a+b \leq \sup(A) + \sup(B)## and the equality true if ##\sup(A) \in A, \sup(B) \in B##. A + B is closed so the equality is true (not sure about this).

2) ##x_n## converges so ##|x_n| \leq a \Rightarrow -a < x_n < a \Rightarrow -a/n < x_n/n < a/n## i.e ##|x_n/n| \leq a/n##. This does not prove anything helpful.

Instead, given that ##x_n## converges, we have that for ##n \geq N, |x_n| \leq a## so ##1/n \leq 1/N \Rightarrow x_n/n \leq x_n/N \leq a/N## I think this is wrong though since I have assumed the ##x_i## are positive. Is there a way to do this via epsilon-N? I can't see it yet.

Many thanks.
 
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  • #2
CAF123 said:

Homework Statement



2)If ##x_n## converges, then ##x_n/n## converges.

The Attempt at a Solution




2) ##x_n## converges so ##|x_n| \leq a \Rightarrow -a < x_n < a \Rightarrow -a/n < x_n/n < a/n## i.e ##|x_n/n| \leq a/n##. This does not prove anything helpful.

Why doesn't it? What happens as ##n\rightarrow\infty## in that last inequality?
 
  • #3
Hi LCKurtz,
LCKurtz said:
Why doesn't it? What happens as ##n\rightarrow\infty## in that last inequality?

I don't know whether you can say anything as ##n \rightarrow \infty##: All I have proven is that the sequence is bounded. But bounded ##\neq## convergence. E.g my sequence might fluctuate between -a/n and a/n indefinitely.
 
  • #4
CAF123 said:
Hi LCKurtz,


I don't know whether you can say anything as ##n \rightarrow \infty##: All I have proven is that the sequence is bounded. But bounded ##\neq## convergence. E.g my sequence might fluctuate between -a/n and a/n indefinitely.

You have ##\left|\frac{x_n} n\right|\le\frac a n##. ##a## is a fixed number. What happens to the right side of that as ##n\rightarrow\infty##?
 
  • #5
LCKurtz said:
You have ##\left|\frac{x_n} n\right|\le\frac a n##. ##a## is a fixed number. What happens to the right side of that as ##n\rightarrow\infty##?

I see, I neglected that n wasn't constant. The RHS tends to zero, so that ##x_n/n \rightarrow 0?##
 
  • #6
CAF123 said:
I see, I neglected that n wasn't constant. The RHS tends to zero, so that ##x_n/n \rightarrow 0?##

Yes.
 
  • #7
"The supremum of A is an element of A..."

The supremum of A may or may not be an element of A. For example, what is the sup A if A=(1,2)? What about if A=[1,2]? If we let, say, M= sup A, then one part of our defintion says that x≤M for all x in A. Since ε is a small positive number, εx≤εM for all x in A; this implies that εM is a upperbound for the set B... Now, use the other part of our definition for the supremum of a set to finish it off! =)
 
  • #8
We can also write ## \epsilon x \leq \sup(B)##. Also##\epsilon x \leq \epsilon \sup(A) ## also. We may have ##\epsilon \sup(A) \leq \sup(B)## or ##\sup(B) \leq \epsilon \sup(A)## in which case the equality holds.

Ideas for 1 b)? I said before that A + B was finite, but I don't think this is the case since A and B need not be finite. The only result I think I am getting is that the inequality holds: i.e since ##a \leq \sup(A)## and ##b \leq \sup(B)## then ##a+b \leq \sup(A) + \sup(B)## If ##\sup(A) \in A,\,\sup(B) \in B## then obviously the equality holds but that assumption is not applicable here.
 
  • #9
"We can also write ϵx≤sup(B) . Alsoϵx≤ϵsup(A) also. We may have ϵsup(A)≤sup(B) or sup(B)≤ϵsup(A) in which case the equality holds."


I am not sure what you are getting at in the above statement. Read carefully the definition of the supremum of a subset of the real numbers. In my previous post, we let M= sup A. We desire to show that εM is the supremum of the set B. We have already shown that εM is an upperbound for the set B. What remains to be shown is that εM is the LEAST upper bound for the set B. Then, we can conclude
ε sup A = εM = sup B​
as desired.

Glancing at my analysis text (Ross), the author states that if A is a subset of the real numbers that is bounded above and M = sup A, then
(i) x≤M for all x in A, and
(ii) If M1< M, then there is an x in A such that M1<x.

We have already shown condition (i). Hence, we are left to verify that εM satisfies condition (ii). Here is a start: Let M0 < εM. Now, M0=εM1 for some real number M1.

From here, use the fact that M = sup A.
 
  • #10
jmjlt88 said:
"We can also write ϵx≤sup(B) . Alsoϵx≤ϵsup(A) also. We may have ϵsup(A)≤sup(B) or sup(B)≤ϵsup(A) in which case the equality holds."


I am not sure what you are getting at in the above statement. Read carefully the definition of the supremum of a subset of the real numbers. In my previous post, we let M= sup A. We desire to show that εM is the supremum of the set B. We have already shown that εM is an upperbound for the set B. What remains to be shown is that εM is the LEAST upper bound for the set B. Then, we can conclude
ε sup A = εM = sup B​
as desired.

Glancing at my analysis text (Ross), the author states that if A is a subset of the real numbers that is bounded above and M = sup A, then
(i) x≤M for all x in A, and
(ii) If M1< M, then there is an x in A such that M1<x.

We have already shown condition (i). Hence, we are left to verify that εM satisfies condition (ii). Here is a start: Let M0 < εM. Now, M0=εM1 for some real number M1.

From here, use the fact that M = sup A.

I don't have that definition in my book.. Anyway, that would imply ##M_1< M = \sup(A)##. But we also know ##x ≤ \sup(A)## for x in A. So ##M_1 < x## since there exists an x such that supA - p≤ x ≤ sup A, p small > 0.
 
  • #11
Right! Now, because
M1<x≤M​
we have that
______________ .​
 
  • #12
jmjlt88 said:
Right! Now, because
M1<x≤M​
we have that
______________ .​

Then there exists an x greater than this M1 and smaller than sup A, so there is an εx greater than εM1 and smaller than εsup A. So from this, I think I can conclude that ##\epsilon \sup A ## is the lowest upper bound and since ##\epsilon x \leq \sup B## also I can say ##\epsilon \sup A = \sup B##. Right?
 
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FAQ: Proving Properties of Bounded Sets in Real Numbers

What is the purpose of conducting a two small analysis?

The purpose of conducting a two small analysis is to compare two small groups or samples in order to determine if there are any significant differences between them. This can help in making informed decisions and understanding the potential impact of certain variables on the groups.

How is a two small analysis different from a t-test?

A two small analysis is a broad term that can encompass various statistical tests, including t-tests. The main difference between a two small analysis and a t-test is that a t-test specifically compares the means of two groups, while a two small analysis can compare various aspects such as means, medians, variances, and proportions.

What are some assumptions of a two small analysis?

Some common assumptions of a two small analysis include normality of the data, equal variances between the groups, independence of observations within each group, and a large enough sample size. Violation of these assumptions can affect the accuracy and reliability of the results.

Can a two small analysis be used for non-numerical data?

Yes, a two small analysis can be used for non-numerical data. In this case, non-parametric tests such as the Mann-Whitney U test or the Wilcoxon signed-rank test can be used instead of parametric tests like t-tests.

How do I interpret the results of a two small analysis?

The results of a two small analysis will typically include a p-value, which indicates the probability of obtaining the observed results by chance. A p-value less than the chosen significance level (usually 0.05) indicates that there is a significant difference between the groups. Additionally, effect size measures such as Cohen's d can also be used to determine the magnitude of the difference between the groups.

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