Compactness of Nested Subsets in Metric Spaces

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In summary: X_7##?In summary, the conversation discusses proving the nonempty intersection of a sequence of nonempty compact subsets of a metric space Ω, where each subsequent subset is a subset of the previous one. The suggested approach is to create a sequence of points from each subset, using sequential compactness to show that the sequence has a limit point in each subset. This implies that the limit point is in all subsets and therefore in the intersection of the subsets.
  • #1
HumbleHarry
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Homework Statement


Let ##{X_n}## be a sequence of nonempty compact subsets of the metric space Ω such that ##{X_n+1}## ##\subseteq## ##{X_n}##, with ##n: 1→∞##. Prove that their intersection is nonempty.

**By the way, I mean to subscript "n+1", not have ##{X_n}## + 1 as it seems.

Homework Equations


One can use sequential compactness to describe the compactness of each subset. Every sequence in ##{X_n}## has a subsequence ##{X_k}## where ##x_k##→##x_o## ##\in## ##{X_n}##.


The Attempt at a Solution


I tried using sequential compactness to find some limit point that's found in ##{X_n}## and all of its subsets, but I have no clue on how to do so. I've tried setting up singleton sequences around the points in each nonempty set to say they must have a subsequence that converges to said point, implying said point is in ##{X_n}##, but I don't know if this implies that these points lie in the other sets, as well.
On another line of thought, since ##{X_n+1}## is a subset of ##{X_n}##, there's some sequence in ##{X_n}## that's also in ##{X_n+1}##, both of which should converge to a point in ##{X_1}##.
Help would be appreciated.
 
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  • #2
HumbleHarry said:

Homework Statement


Let ##{X_n}## be a sequence of nonempty compact subsets of the metric space Ω such that ##{X_n+1}## ##\subseteq## ##{X_n}##, with ##n: 1→∞##. Prove that their intersection is nonempty.

**By the way, I mean to subscript "n+1", not have ##{X_n}## + 1 as it seems.

Homework Equations


One can use sequential compactness to describe the compactness of each subset. Every sequence in ##{X_n}## has a subsequence ##{X_k}## where ##x_k##→##x_o## ##\in## ##{X_n}##.


The Attempt at a Solution


I tried using sequential compactness to find some limit point that's found in ##{X_n}## and all of its subsets, but I have no clue on how to do so. I've tried setting up singleton sequences around the points in each nonempty set to say they must have a subsequence that converges to said point, implying said point is in ##{X_n}##, but I don't know if this implies that these points lie in the other sets, as well.
On another line of thought, since ##{X_n+1}## is a subset of ##{X_n}##, there's some sequence in ##{X_n}## that's also in ##{X_n+1}##, both of which should converge to a point in ##{X_1}##.
Help would be appreciated.

Start by picking any point ##x_n## in each ##X_n##. The sequence ##x_n## has a convergent subsequence, right? If L is the limit of the convergent subsequence, what can you say about what sets it must belong to?
 
  • #3
When you say ##x_n##, first as a point and then a sequence, do you mean the single point sequence ##{x_n}##?
Shouldn't ##L## be in ##X_n##, implying ##L## is in every ##X_n## before it, up until ##X_1##?
 
  • #4
HumbleHarry said:
When you say ##x_n##, first as a point and then a sequence, do you mean the single point sequence ##{x_n}##?
Shouldn't ##L## be in ##X_n##, implying ##L## is in every ##X_n## before it, up until ##X_1##?

No, I mean a sequence of possibly different points. Each point ##x_n## is a element of the corresponding set ##X_n##. So the sequence is ##x_1,x_2,x_3,...## with ##x_1## in ##X_1##, ##x_2## in ##X_2##, etc etc.
 
  • #5
Oh, so what you're doing is creating a subsequence of points from each ##X_n##, and saying that this sequence has a limit ##L##?
In this case, shouldn't the limit lie in ##X_1##, and since it's a limit point for a sequence containing points from each ##X_n+1## it should get arbitrarily close to ##x_n## in the sequence after a certain point? This would imply that some ##X_n+k## will eventually contain points infinitesimally close to ##L##, and these points would be in all sets before and after it, implying the intersection of these sets are nonempty.
 
  • #6
HumbleHarry said:
Oh, so what you're doing is creating a subsequence of points from each ##X_n##, and saying that this sequence has a limit ##L##?
In this case, shouldn't the limit lie in ##X_1##, and since it's a limit point for a sequence containing points from each ##X_n+1## it should get arbitrarily close to ##x_n## in the sequence after a certain point? This would imply that some ##X_n+k## will eventually contain points infinitesimally close to ##L##, and these points would be in all sets before and after it, implying the intersection of these sets are nonempty.

You might have the right idea, but it's hard to tell from your description of it. Could you try to write that down in a clearer way (i.e. more like a proof)?
 
  • #7
Sorry:
Create a sequence ##{x_n}## where ##x_n## ##\in## ##X_n##.
The sequence ##{x_n} \subseteq {X_1}##.
Since the sequence of ##{X_n}## is compact, ##\forall ε >0##, ##\exists x_k##, ##k \in N## such that ##d(x_k, L) < ε ## for ##k \geq n##, and where ##L## is the limit point of the subsequence.
##\forall ε## create a ball ##B(L,ε)##. This ball contains ##x_k## for ##k \geq n##.
This implies that the ball contains points from ##X_n## whose ##n \leq k##, and since ##X_n+1 \subseteq X_n##, ##B(L,ε) \subseteq X_n## whose ##n<k##.
Thus any element from the ball is an intersection point of these ##X_n##.
Is this right?
 
  • #8
HumbleHarry said:
Sorry:
Create a sequence ##{x_n}## where ##x_n## ##\in## ##X_n##.
The sequence ##{x_n} \subseteq {X_1}##.
Since the sequence of ##{X_n}## is compact, ##\forall ε >0##, ##\exists x_k##, ##k \in N## such that ##d(x_k, L) < ε ## for ##k \geq n##, and where ##L## is the limit point of the subsequence.
##\forall ε## create a ball ##B(L,ε)##. This ball contains ##x_k## for ##k \geq n##.
This implies that the ball contains points from ##X_n## whose ##n \leq k##, and since ##X_n+1 \subseteq X_n##, ##B(L,ε) \subseteq X_n## whose ##n<k##.
Thus any element from the ball is an intersection point of these ##X_n##.
Is this right?

You are saying both more and less than you have to. Here, I'll get you started. Start with the sequence ##x_1,x_2,x_3,...## that we chose before. Now you can't just assume that is convergent. But because ##X_1## is compact and that is a sequence in ##X_1## (since all of the other sets are subsets of ##X_1##), then it has a convergent subsequence, call that ##x_{k_1},x_{k_2},x_{k_3},...## that does converge to some limit L. Now, sure, L must be in ##X_1##. Now can you try and tell me clearly why L has to also be in, say for example, ##X_{100}##?
 
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  • #9
Hmm... would it be because if ##L \not\in X_100##, then that would imply that the ball ##B(L,ε)## would not be a subset of ##X_100##, though we've established that when ##n \geq k##, ##\exists k \in N##, ##x_k \in B(L,ε) \subseteq X_(n \geq k)## for ##\forall ε > 0##, implying ##X_(n \geq k) \subseteq X_100##?
 
  • #10
HumbleHarry said:
Hmm... would it be because if ##L \not\in X_100##, then that would imply that the ball ##B(L,ε)## would not be a subset of ##X_100##, though we've established that when ##n \geq k##, ##\exists k \in N##, ##x_k \in B(L,ε) \subseteq X_(n \geq k)## for ##\forall ε > 0##, implying ##X_(n \geq k) \subseteq X_100##?

Sort of. But no talk of balls is needed. Can you think of a way to define a sequence that converges to L in ##X_{100}##?? And don't start from the beginning. There's an easy way to find a subsequence of ##x_{k_1},x_{k_2},x_{k_3},...## that does.
 
  • #11
Hmm... I am really pulling a blank here.
I know the general definition of a convergent sequence is that ## \exists k \in N##, such that ##d(x_n,L)< ε##, ##\forall ε > 0##, ##k \geq n##.
Maybe construct a sequence ##x_kn## where ##d(x_kn,L)<ε##, for ##ε>0##? Would this just be the terms from ##x_n## whose index is ##k \geq n## in relation to each term being infinitesimally close to L? Such a sequence would obviously converge to L.
I am sorry if this is not it.
 
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  • #12
HumbleHarry said:
Hmm... I am really pulling a blank here.
I know the general definition of a convergent sequence is that ## \exists k \in N##, such that ##d(x_n,L)< ε##, ##\forall ε > 0##, ##k \geq n##.
Maybe construct a sequence ##x_kn## where ##d(x_kn,L)<ε##, for ##ε>0##? Would this just be the terms from ##x_n## whose index is ##k \geq n## in relation to each term being infinitesimally close to L? Such a sequence would obviously converge to L.
I am sorry if this is not it.

Don't be sorry. I know you've got exactly the right idea. You just aren't expressing it as clearly as you might. Talking about the balls is just making in confusing. If you know ##x_{k_1},x_{k_2},x_{k_3},...## converges to L then ##x_{k_{100}},x_{k_{101}},x_{k_{102}},...## also converges to L. The neat way to do this is to prove that ##x_{k_{100}}## must be in ##X_{100}## and so must ##x_{k_{101}}## etc. But you don't need to be that neat. If ##x_{k_j}##, for ANY j, is in ##X_{100}## then so are ##x_{k_{j+1}}##, ##x_{k_{j+2}}## etc. Remember that ##x_{k_j}## is in ##X_{k_j}## and ##k_j<k_{j+1}<k_{j+2}<...## since it is a subsequence.
 
  • #13
I see what you mean, actually, that's what I thought concerning ##x_kj+c \in X_j## for ##c \in N##, ie ##x_k100+c \in X_100##.
I think I can write a coherent proof now that you've helped me resolve my questions. Thank you very much for having the patience to help me! I appreciate it.
 

FAQ: Compactness of Nested Subsets in Metric Spaces

What is analysis compactness?

Analysis compactness refers to the level of closeness or tightness of data points in a dataset. It is a measure of how well the data points are clustered or spread out.

Why is analysis compactness important?

Analysis compactness is important because it provides insights into the distribution of data points and helps to identify patterns, trends, and outliers. It is also a key factor in determining the accuracy and reliability of statistical models.

How is analysis compactness measured?

Analysis compactness can be measured using various statistical methods such as standard deviation, variance, and mean absolute deviation. Other measures like density-based clustering algorithms and nearest neighbor methods can also be used to assess compactness.

What is the difference between analysis compactness and analysis completeness?

Analysis compactness and analysis completeness are two different measures of data quality. While compactness refers to the tightness of data points, completeness refers to the proportion of missing or incomplete data in a dataset. In other words, compactness measures how well the data is clustered, while completeness measures how much of the data is present.

How can analysis compactness be improved?

Analysis compactness can be improved by removing outliers and redundant data points, using dimensionality reduction techniques, and selecting appropriate statistical models. It is also important to carefully consider the data collection and cleaning processes to ensure that the data is relevant and representative of the population being studied.

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