Sequence of Intervals - Rudin, Theorem 2.38

In summary, Rudin uses the fact that for all positive integers m and n, a_n \le a_{m+n} \le b_{m+n} \le b_m to prove Theorem 2.38. This is necessary because x is the supremum of E, not the infimum. This means that for each m, b_m is an upper bound for E, and thus x \le b_m for all m. This clarifies the issue and shows why Peter's proof is defective.
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I am reading Walter Rudin's book, Principles of Mathematical Analysis.

Currently I am studying Chapter 2:"Basic Topology".

Although I can basically follow it, I am concerned that I do not fully understand the proof of Theorem 2.38.

Rudin, Theorem 2.38 reads as follows:https://www.physicsforums.com/attachments/3789In the above proof we read the following:

" ... ... If \(\displaystyle m\) and \(\displaystyle n\) are positive integers then

\(\displaystyle a_n \le a_{m+n} \le b_{m+n} \le b_m\)

so that \(\displaystyle x \le b_m\) for each \(\displaystyle m\). ... ..."This appears to me to be true ... ... BUT ... ...

Why doesn't Rudin simply say the following:

"Let \(\displaystyle m\) be a positive integer.

Then \(\displaystyle a_m \le b_m\) ...

so that \(\displaystyle x \le b_m\) for each \(\displaystyle m\). "Since my statement is simpler than what Rudin says, I feel that I must be missing something and my analysis above must be wrong ...

Can someone point out why my proof is defective and thus clarify this issue?

Hope someone can help ... ...

Peter
 
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Peter said:
Why doesn't Rudin simply say the following:

"Let \(\displaystyle m\) be a positive integer.

Then \(\displaystyle a_m \le b_m\) ...

so that \(\displaystyle x \le b_m\) for each \(\displaystyle m\). "
Hi Peter,

The implication you have here is false. It would've been true if $x$ was the infimum of $E$ (since then $x \le a_m \le b_m$ for all $m$) but in fact $x$ is the supremum of $E$. He uses the necessary fact that for all $n$, $a_n \le b_m$ for all $m$ (notice here how $n$ is independent of $m$). With this, we know that for each $m$, $b_m$ is an upper bound for $E$. Hence, by definition of $x$, $x \le b_m$ for all $m$. Makes sense?
 

FAQ: Sequence of Intervals - Rudin, Theorem 2.38

What is the significance of Theorem 2.38 in Rudin's "Sequence of Intervals"?

Theorem 2.38 in Rudin's "Sequence of Intervals" is a fundamental result in the study of mathematical sequences. It states that if a sequence of intervals has a non-empty intersection, then the intersection contains at least one point that is a limit point of the sequence. This theorem is crucial in understanding the behavior of sequences and their convergence.

How does Theorem 2.38 relate to the Bolzano-Weierstrass theorem?

Theorem 2.38 is actually a special case of the Bolzano-Weierstrass theorem, which states that every bounded sequence in Euclidean space has a convergent subsequence. Theorem 2.38 applies specifically to a sequence of intervals, whereas the Bolzano-Weierstrass theorem applies to any bounded sequence in Euclidean space.

Can you provide an example of how Theorem 2.38 can be applied?

Sure, let's say we have a sequence of intervals defined by the closed intervals [1/n, 2/n] for n = 1,2,3,... We can see that each interval contains the point 1/n, which is a limit point of the sequence. By Theorem 2.38, the intersection of these intervals must contain this limit point. In fact, the intersection in this case is the singleton set {0}, which contains the limit point 0. This shows the importance of Theorem 2.38 in understanding the behavior of sequences.

Is Theorem 2.38 only applicable to real numbers?

No, Theorem 2.38 can be generalized to any metric space. A metric space is a set of objects with a distance function defined between them. In the case of real numbers, the distance function is simply the absolute value. However, Theorem 2.38 also holds for other metric spaces, such as complex numbers or matrices.

Are there any practical applications of Theorem 2.38?

Yes, Theorem 2.38 has many practical applications in various fields such as engineering, physics, and computer science. For example, it can be used to prove the convergence of numerical algorithms or to establish the existence of solutions in differential equations. It is also useful in analyzing the behavior of systems with discrete time steps, such as in computer simulations.

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