Sequence of metric spaces is compact iff each metric space is compact

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The discussion centers on proving the compactness of the product space X formed by a sequence of metric spaces (X_n, d_n). It establishes that X is compact if and only if each metric space (X_n, d_n) is compact. The forward implication is demonstrated by showing that a convergent subsequence in X leads to a convergent sequence in each (X_n, d_n). The challenge lies in proving the reverse implication, where the participant seeks to combine convergent subsequences from each (X_k, d_k) to form a convergent subsequence in X. A key insight is that the metrics d_k and d_k' yield the same topology, simplifying the convergence proof.
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Homework Statement .

Let ##(X_n,d_n)_{n \in \mathbb N}## be a sequence of metric spaces. Consider the product space ##X=\prod_{n \in \mathbb N} X_n## with the distance ##d((x_n)_{n \in \mathbb N},(y_n)_{n \in \mathbb N})=\sum_{n \in \mathbb N} \dfrac{d_n(x_n,y_n)}{n^2[1+d_n(x_n,y_n)]}##.

Prove that ##(X,d)## is compact if and only if each ##(X_n,d_n)## is compact.

The attempt at a solution.

I didn't have problems to prove the forward implication. Let ##n \in \mathbb N## fixed, call it ##n=n_0## and let
Now let ##x_i## an arbitrary element in the space ##(X_i,d_i)## for ##i \neq n_0## and define the sequence ##\{\vec{x}^n\}_{n \in \mathbb N}## in ##(X,d)## as follows:

##\vec{x}^j=(x_1,x_2,...,x_{n_0-1},y^k,x_{n_0+1},...)##. By hypothesis, there is ##\{\vec{x}^{n_k}\}_{k \in \mathbb N}## convergent subsequence of ##\{\vec{x}^n\}_{n \in \mathbb N}##. Let ##\{a^{\infty}\}=(a_1,a_2,...,a_{n_0-1},a_{n_0},a_{n_0+1},...,)## be the limit of ##\{\vec{x}^{n_k}\}_{k \in \mathbb N}##. Let's prove that ##\lim_{k \to \infty} y^{n_k}=a_{n_0}##

Let ##0<\epsilon<1##, then, there is ##N: \space \forall k\geq N##, ##d(\vec{x}^{n_k},\vec{a}^{\infty})<\dfrac{\epsilon}{{n_0}^2}##.

But then, ##\dfrac{d_{n_0}(y^{n_k},a_{n_0})}{{n_0}^2[1+d_{n_0}(y^{n_k},a_{n_0})]}=\dfrac{d_{n_0}({x_{n_0}}^{n_k},a_{n_0})}{{n_0}^2[1+d_{n_0}({x_{n_0}}^{n_k},a_{n_0})]}\leq \sum_{j \in \mathbb N} \dfrac{d_j({x_j}^{n_k},a_j)}{j^2[1+d_j({x_j}^{n_k},a_j)]}=d(\vec{x}^{n_k},\vec{a}^{\infty})<\dfrac{\epsilon}{{n_0}^2}## for all ##k\geq N##.

So, ##d_{n_0}(y^{n_k},a_{n_0})<\dfrac{\epsilon}{1-\epsilon}<\epsilon## for all ##k\geq N##. This shows ##y^{n_k} \to a_{n_0}##, so the sequence ##\{y^j\}_{j \in \mathbb N}## has a convergent subsequence in ##(X_{n_0},d_{n_0})##. As ##\{y^j\}_{j \in \mathbb N}## was an arbitrary sequence, the metric space ##(X_{n_0},d_{n_0})## is compact. Since ##n_0## was an arbitrary natural number, then for each ##n \in \mathbb N##, the space ##(X_n,d_n)## is compact.

I have problems to prove the other implication. I want to show that if each ##(X_n,d_n)## is compact, then the space ##(X,d)## has to be compact. So, let ##\{\vec{x}^n\}_{n \in \mathbb N}## be a sequence in ##(X,d)## with ##\vec{x}^n=(x_{1}^n,x_{2}^n,...,x_{k}^n,...)##. Then ##\{x_{k}^n\}_{n \in \mathbb N}## is a sequence in the space ##(X_k,d_k)##. For each ##k##, I know there is a convergent subsequence Then ##\{x_{k}^{n_j}\}_{j \in \mathbb N}##. The problem is I have an infinite number of indexes ##j## dancing around, for each space ##(X_k,d_k)## I have a convergent subsequence, how can I combine the indexes of each space to choose the appropiate convergent subsequence of ##\{\vec{x}^n\}_{n \in \mathbb N}## in the space ##(X,d)##?

I hope my question and notation are clear. For me, one of the difficulties of this exercise is that one can easily be confused by all the sequences and indexes playing here.
 
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mahler1 said:
I have problems to prove the other implication. I want to show that if each ##(X_n,d_n)## is compact, then the space ##(X,d)## has to be compact. So, let ##\{\vec{x}^n\}_{n \in \mathbb N}## be a sequence in ##(X,d)## with ##\vec{x}^n=(x_{1}^n,x_{2}^n,...,x_{k}^n,...)##. Then ##\{x_{k}^n\}_{n \in \mathbb N}## is a sequence in the space ##(X_k,d_k)##. For each ##k##, I know there is a convergent subsequence Then ##\{x_{k}^{n_j}\}_{j \in \mathbb N}##. The problem is I have an infinite number of indexes ##j## dancing around, for each space ##(X_k,d_k)## I have a convergent subsequence, how can I combine the indexes of each space to choose the appropiate convergent subsequence of ##\{\vec{x}^n\}_{n \in \mathbb N}## in the space ##(X,d)##?

I hope my question and notation are clear. For me, one of the difficulties of this exercise is that one can easily be confused by all the sequences and indexes playing here.

For any sequence on X, there is a subsequence such that its projection onto X_1 converges. Take this subsequence. It has a subsequence whose projection onto X_2 converges. Take this subsequence. It has a subsequence whose projection onto X_3 converges. ...

EDIT: One point which is worth noting is that the metrics d_k and d_k&#039; = \displaystyle\frac{d_k}{k^2(1 + d_k)} give rise to exactly the same topology on X_k, so a sequence converges with respect to d_k if and only if it converges with respect to d_k&#039;.
 
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pasmith said:
EDIT: One point which is worth noting is that the metrics d_k and d_k&#039; = \displaystyle\frac{d_k}{k^2(1 + d_k)} give rise to exactly the same topology on X_k, so a sequence converges with respect to d_k if and only if it converges with respect to d_k&#039;.

This last remark was particularly helpful, it was much more difficult trying to prove d_k&lt;\epsilon without using that fact.
 
Question: A clock's minute hand has length 4 and its hour hand has length 3. What is the distance between the tips at the moment when it is increasing most rapidly?(Putnam Exam Question) Answer: Making assumption that both the hands moves at constant angular velocities, the answer is ## \sqrt{7} .## But don't you think this assumption is somewhat doubtful and wrong?

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