Why Doesn't a Proof by Contradiction Work for Cauchy Convergence?

In summary: CCIn summary, Maxwell Rossenlichtok shows that if a sequence is cauchy, then its limit exists in the original space. However, if you let the sequence converge to a point outside of the original space, you can still prove that the sequence converges.
  • #1
happyg1
308
0
Hi,
Here's the question:
Show that if [tex]{x_n}[/tex] is a cauchy sequence of points in the metric space M, and if [tex]{x_n}[/tex] has a subsequence which converges to [tex]x \in M[/tex], Prove that [tex] x_n[/tex] itself is convergent to x.
Now, I have proved this as follows..I didn't put in all of the details...
Let [tex]{x_n_k}[/tex] be the subsequence which converges to x.
Choose [tex]n\in\mathbb{N}[/tex] such that [tex]\forall k \geq N [/tex] the distance from [tex]x_n_k[/tex] to x [tex]\leq\frac{\eps}{2}[/tex] and similarly for [tex]x_n,x_m[/tex] then you wind up with [tex]\frac{\eps}{2}+\frac{\eps}{2}=\eps so you're done.
My confusion lies in why can't you do a proof by contradiction?
You let [tex]x_n[/tex] converge to, say y, and the subsequence [tex]s_n_k[/tex] (by hypothesis) converges to x...but every subsequence of a convergent (cauchy) sequence converges to the same limit. Why doesn't this work?
CC
 
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  • #2
I'm still learning latex, so forgive my funky text there. I think you can still see what I mean. I typed in \eps instead of \epsilon, so what's blank there is [tex]\frac{\epsilon}}{2}[/tex]
sorry
CC
 
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  • #3
There is no reason why you can assume the cauchy sequence x_n converges at all in the first place, is there? Just because a sequence is cauchy does not mean that its 'limit' exists in the original space.
 
  • #4
Proposition: A Cauchy sequence that has a convergent subsequence is itself convergent.

pg. 52 of Introduction to Analysis by Maxwell Rossenlicht
 
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  • #5
ok,
so I need to think of a cauchy sequence whose limit lies outside fo the origional space that has a subsequence whose limit lies inside the space to show that that this won't work...
 
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  • #6
My head hurts
 
  • #8
we don't know about complete yet...
 
  • #9
dang it!
I was all sassy and confident about understanding this stuff...My final is coming up and now I'm all confused...I neeed a counterexample but I'm afraid to thinK about it because I'll doubt what I already proved...
arrrrrrghhhhhh
 
  • #10
Here's my whack at it...

happyg1 said:
Hi,
Here's the question:
Show that if [itex]{x_n}[/itex] is a cauchy sequence of points in the metric space M, and if [itex]{x_n}[/itex] has a subsequence which converges to [itex]x \in M[/itex], Prove that [itex] x_n[/itex] itself is convergent to x.
Now, I have proved this as follows..I didn't put in all of the details...
Let [itex]x_{n_{k}}[/itex] be the subsequence which converges to x.
Choose [itex]n\in\mathbb{N}[/itex] such that [itex]\forall k \geq N [/itex] the distance from [itex]x_{n_{k}}[/itex] to x [itex]\leq\frac{\epsilon}{2}[/itex] and similarly for [itex]x_n,x_m[/itex] then you wind up with [itex]\frac{\epsilon}{2}+\frac{\epsilon}{2}=\epsilon [/itex] so you're done.
CC

Here's my whack at it...

Let [itex]\left\{ x_{n_{k}}\right\} \rightarrow x[/itex] be the convergent subsequence of the Cauchy sequence [itex]\left\{ x_{n}\right\}[/itex] .

Choose an integer N such that [itex]\forall n,m \geq N [/itex] we have [itex] d\left( x_{n},x_{m}\right) \leq\frac{\epsilon}{2}[/itex].

Fix [itex]k\in\mathbb{N}[/itex] such that [itex]x_{n_k}\geq N[/itex] and large enough that [itex] d\left( x_{n_{k}},x\right) \leq\frac{\epsilon}{2}[/itex],

then set [itex]m = n_k \geq N[/itex] so that [itex]\forall n\geq N [/itex]

[tex]d\left( x_{n},x\right) \leq d\left( x_{n},x_{m}\right) + d\left( x_{n_{k}},x\right) \leq \frac{\epsilon}{2} + \frac{\epsilon}{2} = \epsilon[/tex]

and hence [itex]\left\{ x_{n}\right\} \rightarrow x[/itex]
 
  • #11
yes yes yes
that's exactly what I did...I need to try to understand why the contradiction method doesn't work...If the sebsequence converges to x then why won't the sequence?
 
  • #12
Look, here's what you're doing:

x_n is cauchy.

y_n is a subsequence that converges to y

This implies x_n converges to y.

That's fine.

What you can't do is ***suppose that x_n converges to something** and show x=y. Because we do not know x_n converges a priori. It does, as your first correct proof shows, and moreover constructively we show it converges to the right thing, but we can't assume that it converges since that is not part of the hypothesis.

After all, if x_n is convergent sequence, and any subsequence converges to x then x_n does, that's fine, no need to invoke cauchyness. But we do not know x_n converges which you incorrectly suppose when trying to prove it by contradiction.As a proof of why your misproof is a misproof, consider the sequence

0,1,0,1,0,1,0,1,...

the eve terms converge to 0, and IF the sequence converged as a whole it converges to zero therefore, but clearly it doesn't converge at all. See, you need to invoke the cauchyness of the initial sequence.
 
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  • #13
happyg1 said:
ok,
so I need to think of a cauchy sequence whose limit lies outside fo the origional space that has a subsequence whose limit lies inside the space to show that that this won't work...

why try to do that since you've just proved that you can't?
 
  • #14
The fact that, if a Cauchy Sequence has a convergent subsequence, the whole series converges is true in any metric space.

Let {an} be a Cauchy Sequence and let S be an infinite set of natural numbers such that the subset {an} with n in S converges to a.

Then, given [itex]\epsilon> 0[/itex], there exist N1 such that if n is in S and n> N1, [itex]|a_n- a|<\frac{\epsilon}{2}[/itex].

Since {an} is Cauchy, there exist N2 such that if m and n are both larger than N2 then [itex]|a_n- a_m|< \frac{\epsilon}{2}[/itex].

Let n be a positive integer larger than both N1 and N2 and contained in S and, for any m> N2, look at
[tex]|a_m- a|\leq|a_m- a_n|+ |a_n- a|[/tex]

You can use that "lemma" to show that if "monotone convergence" is true, then the Cauchy Criterion is true.
 
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  • #15
Hi,
Thanks Matt for the explicit explanation. I understand this now. I hate going around and round in circles like that. That lemma is neat, too..sheds some more light on the ideas of metric spaces and cauchyness.
The fog has lifted.
Ya'll are awesome
CC
 
  • #16
I thought of a better explanation.

Your proof by contradiction works, in as much as it negates an assumption, it's just that your assumption was:

x_n converges AND its limit is not the limit of the subsequence.

so, you do get a contradiction and hence that is false.
 
  • #17
I just got this problem for HW (out of Papa Rudin, ch. 3, #22):

Suppose that X is a metric space in which every Cauchy sequence has a convergent subsequence. Prove that X is complete.

Yep, thanks.
 

1. What is metric cauchy confusion?

Metric cauchy confusion is a statistical method used to measure the similarity or dissimilarity between two sets of data. It utilizes a metric to calculate the distance between data points, and then applies a threshold to determine if the data points are considered similar or dissimilar.

2. How is metric cauchy confusion different from other similarity measures?

Metric cauchy confusion differs from other similarity measures, such as Pearson correlation or cosine similarity, in that it takes into account the distance between data points rather than just their direction. This makes it a more robust measure for datasets with varying scales or dimensions.

3. What are the applications of metric cauchy confusion?

Metric cauchy confusion can be applied in various fields such as data mining, machine learning, and pattern recognition. It is commonly used to identify similar documents, images, or other types of data in large datasets.

4. How is the threshold determined in metric cauchy confusion?

The threshold in metric cauchy confusion is usually determined through trial and error or by using domain knowledge. It is typically set to a value that maximizes the accuracy of the classification or clustering task at hand.

5. What are the limitations of metric cauchy confusion?

One limitation of metric cauchy confusion is that it is highly dependent on the choice of metric. Different metrics may yield different results, making it difficult to compare across studies. Additionally, it may not perform well with highly imbalanced datasets or datasets with high dimensionality.

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