If $U \subset \mathbb{R}^k$ and $V \subset H^k$ are neighborhoods of $0$, prove that there exists no diffeomorphism of V with U.
Here, $H^k$ is simply the upper half-space.
I tried to solve this problem with the continuity, but it is a dead-end. I have difficulty to solve this problem. Anyone could give me a hint to complete this problem?
Some of the comments have suggested using algebraic topology. That sounds horrifying. Let's be analysts and ignore hard stuff. The idea is pretty clear. If $V$ is a neighborhood of $0$ in $H^k$, it's going to have to contain a piece of the boundary. But there's nowhere for the boundary to map to in $U$.
If $V$ is a neighborhood of $0$ in $H^k$, it contains a neighborhood of the form $D = \{x = (x_1, ..., x_k): |x| < \epsilon, x_k \ge 0\}$ for some $\epsilon > 0$. Let $f:U \to V$ be the supposed diffeomorphism. Pick $x \in U$ so that $f(x) \in D$.
Since $f$ is differentiable at $x$, we can write $f(s) = f(x) + Df_x(s-x) + h(s-x)$, where $\frac{|h(s-x)|}{|s -x|} \to 0$ as $s \to x$. Since $f$ is actually a diffeomorphism, $Df_x$ is actually a linear isomorphism.
At this point, we'd like to force $s$ out of $H^k$. Choosing a new coordinate system if necessary, we may take $Df_x$ to be the identity map, so really near $x$ we have that
A brief aside to justify the above: Let $V$ be a finite dimensional real vector space of dimension $k$. Let $T:V \to V$ be a linear isomorphism. Choose any basis $v_1, ..., v_k$ for $V$. Let $w_j := T^{-1}(v_j)$. Then $w_1, ... , w_k$ is also a basis for $V$ (you should check this).
Now let's think of $T:(V, \{w_1, ... , w_k\}) \to (V, \{v_1, ..., v_k\})$. What does $Tx$ look like for $x \in V$? Since $T$ has the property that $Tw_j = v_j$ for any $j$, if $x = x_1 w_1 + \dots + x_k w_k$, then we have that $Tx = x_1 v_1 + \dots + x_k v_k$. This is what I mean by representing $T$ by the identity through a choice of basis.
Going back to the above, we've written $f(s) = f(x) + Df_x(s-x) + h(s-x)$ for $Df_x$ a linear isomorphism from $\mathbb{R}^k \to \mathbb{R}^k$. Choose a basis $v_1, \dots, v_k$ for $\mathbb{R}^k$ so that $Df_x(v_j) = e_j$, where $e_j$ is the $j$th basis vector.
Let $| \cdot |_{v}$ denote the norm on $\mathbb{R}^k$ with respect to this new basis. Since $\mathbb{R}^k$ is a finite dimensional vector space, all norms are equivalent (exercise!). Choose a constant $c > 0$ so that $ c^{-1} |\cdot| \le | \cdot|_{v} \le c |\cdot|$.
Since $x \in U$ and $U$ is open, choose $\eta > 0$ so that $B_v(x, \eta) \subset U$, where the ball is taken in our new norm (i.e., $B_v(x, \eta) = \{y \in \mathbb{R}^k: |x - y|_v < \eta\}$).
Choose $s = x - \frac{\eta}{2} v_k$. Clearly, $s \in B_v(x, \eta)$.
Shrinking $\eta$ if necessary, we will have that $|h(s-x)| \le \frac{1}{2}|s-x|$ so long as $|s-x| < \eta$ as well.
So, $f(s) = f(x) + Df_x(s-x) + h(s-x) = f(x) + Df_x(x - \frac{\eta}{2}v_k -x) + h(s-x)$
$= f(x) - \frac{\eta}{2}e_k + h(s-x)$.
Now $f(x) \cdot e_k = 0$, by assumption (we chose $x$ specifically so that the $k$th component of $f(x)$ is $0$). Additionally, the above bound on $h(s-x)$ implies that $h(s-x) \cdot e_k \le \frac{\eta}{4}$.
Together, this shows that $f(s) \cdot e_k = -\frac{\eta}{2} + h(s-x) \cdot e_k \le -\frac{\eta}{4} < 0$, which implies that $f(s) \notin V$; this is a contradiction.