Show that the Hilbert cube is compact under the sup metric I'm trying to show that the Hilbert cube is compact, preferably by showing that it is sequentially compact.
My text defines the Hilbert cube as:
$H=\{(x_1,x_2,...) \in [0,1]^{\infty} : for \ each \ n \in \mathbb{N}, |x_n|\leq \dfrac{1}{2^n}\}$
I'm trying to show that it is compact with respect to the metric:
$d(x,y)=\underset{n}{\textrm {sup}}|x_n - y_n|$
I tried showing that it was compact vis sequential compactness by this solution here: Prove that the hilbert cube is compact
However, my solution was wrong and I understand why. I am not sure how to approach showing that it is complete and totally bounded either, but I see that it is also a possible way to solve this.
 A: Let's work on completeness.  
Let $(x_j)$ be a Cauchy sequence with respect to the given metric in the Hilbert cube.
Let's think of $x_j$ as the $j^{\text{th}}$ row of a matrix, and $x^k$ as the $k^{\text{th}}$ column, consisting of all the $k^{\text{th}}$ entries of the elements of the sequence (which are themselves sequences).  The $k^{\text{th}}$ entry of the $j^{\text{th}}$ element is $x_{jk}$.
A sketch of the first part of the proof is:


*

*assume the rows form a Cauchy sequence w.r.t. your $\sup$ metric

*see the individually each column is Cauchy w.r.t. the Euclidean metric on $\mathbb{R}$.

*use the fact that $\mathbb{R}$ is complete to establish a limit for each column

*establish a putative limit which has its elements as the limit of each column.
$$\begin{bmatrix}
x_{00} & x_{01} & x_{02} & \cdots \\
x_{10} & x_{11} & x_{12} & \cdots \\
\vdots & \vdots & \vdots & \vdots \\
x^*_0  & x^*_1  & x^*_2  & \cdots \\
\end{bmatrix}
$$
I'll call our putative limit sequence $x^* = (x_0^*, x_1^*, \ldots)$.
You should be able to fill out these details.  


Now we just have to show that this limit $x^*$ is legitimate. 
This means verifying two things:


*

*$x^*$ is actually an element of the Hilbert cube.  This is straightforward, but do verify it.

*$(x_j)$ actually converges to $x^*$ with respect to the $\sup$ metric given.  Right now we only know that each column converges w.r.t. to the Euclidean metric in $\mathbb{R}$.  


Let's attack (2). Fix $\epsilon > 0$. We can take, for each $i$, $N_i$ such that $n > N_i$ implies $|x^*_i - x_{ni}| < \epsilon$.  This is the definition of convergence in $\mathbb{R}$ applied to each column. Now the important thing is to finally use the definition of the Hilbert cube, which implies that there exists $N^*$ such that $i > N^*$ implies $|x^*_i - x_{ni}| < \epsilon$, independent of $n$.  This is because the entries of a point in the Hilbert cube get really small, indeed by definition we have $|x_{ni} \leq \frac{1}{2^i}|$ independent of $n$, therefore $|x^*_i - x_{ni}| \leq \frac{1}{2^i}$, independent of $n$.  Having established this, set $N = \max_{i \leq N^*}N_i$.  You can confirm that $n > N$ implies $|x^*_i - x_{ni}| < \epsilon$ for all $i$.  Hence we have $d(x^*, x_n) = \sup_i|x_i^* - x_{ni}| < \epsilon$ for $n > N$.  That's convergence!  
The important thing is that we can construct an infinite class of something (here the $N_i$) but only worry about finitely many of them due to the Hilbert cube's property that entries of its elements get really small.  
See if you can get total boundedness using this as inspiration, it's a nice problem and the flavor is similar!
Hint (if you want): 

 try to explicitly cover the Hilbert cube with $\epsilon$ balls of size $\epsilon = 2^{-k}$ for $k= 1,2,3$. Notice for each $k$ how you can ignore all of the indices $i>k$. What's the pattern for how many balls it requires? (you can count exactly). Generalize this construction for all $k$. Now given any $\epsilon > 2^{-k}$, your construction with balls of size $2^{-k}$ suffices, because if a finite number of balls covers a space and all you do is inflate them (keeping them centered as before), they'll always still cover the space.

