Metric Spaces of Bounded Sequences

In summary: If you take any two elements of the sequence and add them together, the result will always be 0 or 1. This means that the sequence does not converge to anything in L.
  • #1
octane90
2
0
I was attempting to find a counterexample to the problem below. I think I may have, but was ultimately left with more questions than answers.

Consider the space, [itex]L[/itex], of all bounded sequences with the metric [itex]\rho_1[/itex]

[itex]\displaystyle \rho_1(x,y)=\sum\limits_{t=1}^{\infty}2^{-t}|x_t-y_t|[/itex]

Show that a sequence that converges to [itex]X^*[/itex] in [itex](L,\rho_1)[/itex] does not necessarily converge to [itex]X^*[/itex] in [itex](L,\rho_\infty)[/itex]

Where, [itex]\rho_\infty(x,y)=sup_t|x_t-y_t|[/itex].

I believe convergence means:

X converges to Y if [itex] \forall \epsilon>0 \exists N[/itex] s.t. [itex] \sum\limits_{t=N}^{\infty}2^{-t}|x_t-y_t|<\epsilon [/itex]

I believe that [itex]x_n=sin (n)[/itex] converges to the 0 sequence in [itex]\rho_1[/itex], but not in [itex]\rho_\infty[/itex]. However, it seems like [itex]x_n=sin (n)[/itex] could also be shown to converge to {1,1,1,1,...} under [itex]\rho_1[/itex] which shouldn't be allowed.

Could anyone help me out?
 
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  • #2
octane90 said:
Where, [itex]\rho_\infty(x,y)=sup(x_t,y_t)[/itex].

I doubt that this is correct. It should be [itex]\rho_\infty(x,y)=\sup_t |x_t-y_t|[/itex].

I believe convergence means:

X converges to Y if [itex] \forall \epsilon>0 \exists N[/itex] s.t. [itex] \sum\limits_{t=N}^{\infty}2^{-t}|x_t-y_t|<\epsilon [/itex]

This is not what convergence is. First of all you must have a sequence of elements in L. That is, you have to start from a sequence of sequences. Let [itex]X_k=(x_k^n)_n[/itex], then we can say that [itex]X_k\rightarrow Y[/itex] if for each [itex]\varepsilon>0[/itex], there exists an N such that for all [itex]k\geq N[/itex] holds that [itex]\rho_1(X_k,Y)<\varepsilon[/itex]. That last inequality is of course the same as

[tex]\sum_{t=1}^{+\infty} 2^{-t}|x_k^t-y^t|<\varepsilon[/tex]

I believe that [itex]x_n=sin (n)[/itex] converges to the 0 sequence in [itex]\rho_1[/itex], but not in [itex]\rho_\infty[/itex]. However, it seems like [itex]x_n=sin (n)[/itex] could also be shown to converge to {1,1,1,1,...} under $\rho_1$ which should be allowed.

This is of course incorrect, since [itex]x_n=\sin(n)[/itex] is only one sequence. Again: you must have a sequence of sequences.
 
  • #3
Thanks, that was very helpful. It makes sense that I need to be thinking of a sequence of sequences. I was just having trouble wrapping my head around this L space.

And you are of course right about my typo in the problem set-up, I fixed it.
 
  • #4
The counterexample you're looking for is very easy. Think about sequences with only 0's and 1's.
 

1. What is a metric space of bounded sequences?

A metric space of bounded sequences is a mathematical concept used in analysis to study sequences of real numbers that have a finite upper bound. It is a set of sequences with a defined distance function or metric, which measures the difference between any two sequences in the space.

2. How is a metric space of bounded sequences different from a regular metric space?

A metric space of bounded sequences is a specific type of metric space that deals with sequences, while a regular metric space can contain any type of elements. The main difference is that in a metric space of bounded sequences, the elements are sequences with a defined distance function, while in a regular metric space, the elements can be any type of objects with a defined distance function.

3. What is a Cauchy sequence in a metric space of bounded sequences?

A Cauchy sequence is a type of sequence where the terms eventually become arbitrarily close to each other as the sequence progresses. In a metric space of bounded sequences, a Cauchy sequence is a sequence where the distance between any two terms in the sequence becomes arbitrarily small as the terms approach infinity.

4. How is convergence defined in a metric space of bounded sequences?

In a metric space of bounded sequences, convergence is defined as a sequence where the terms eventually become arbitrarily close to a fixed limit as the sequence progresses. This means that the distance between any two terms in the sequence approaches zero as the terms approach infinity.

5. What are some practical applications of metric spaces of bounded sequences?

Metric spaces of bounded sequences have applications in various fields, including analysis, functional analysis, and topology. They are useful in studying the convergence and continuity of functions, as well as in the study of infinite-dimensional spaces. They are also used in computer science for data analysis and machine learning algorithms.

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