$d_p$ and $d_\infty$ in $\mathbb{C}^n$ are uniformly equivalent I need to prove this
Show 
with the metrics $d_p$ and $d_{\infty}$ in $\mathbb{C}^n$ are uniformly equivalents, with $p \in [1, \infty)$.
So, I have in my book two definitions about equivalence metrics in a metric space $(X,d)$.
1) Two metrics $d_1$ and $d_2$ are uniformly equivalents if there are constants $a, b >0$ such that $ad_1(x,y) \leq d_2(x,y) \leq bd_1(x,y), \forall x, y \in \mathbb{C}^n$ or equivalently, if $a \leq \dfrac{d_2(x,y)}{d_1(x,y)}\leq b $ for all $x \neq y$.
2) The second definition is about topology equivalence. We say that two metrics $d_1$ and $d_2$ are topology equivalents if any sequence convergent in space $X$ with the metric $d_1$ also converge in the metric $d_2$ and for the same limit point.
I have already proved two facts:
a) If two metrics $d_1$ and $d_2$ are uniformly equivalent in $X$, then a subset $M$ of $X$ is bounded with respect to the metric $d_1$ if, and only if, $M$ is bounded with respect to the metric $d_2$.
b) If two metrics are uniformly equivalent, then they are topologically equivalent.
But my problem above continues. 
I could this:
By definition we know that 
$$
d_p (x,y) = \left(  \sum_{i=1}^{n} \ |x_i -y_i|^{p} \right)^{1/p} \mbox{e}\;\;
d_\infty (x,y) = \sup_{i=1,..., n}{ |x_i - y_i| }.
$$
So, by Minkowski's inequality, we have
$$
d_p (x,y) = \left(  \sum_{i=1}^{n} \ |x_i -y_i|^{p} \right)^{1/p} \leq 
\left(  \sum_{i=1}^{n} \ |x_i|^{p} \right)^{1/p} +
\left(  \sum_{i=1}^{n} \ |y_i|^{p} \right)^{1/p} \leq M_1 + M_2 = M
$$
and
$$
d_\infty = \sup_{i=1,..., n}{ |x_i - y_i| } \leq N.
$$
How $0 < d_p(x,y)$ and $0 < d_\infty (x,y)$ for all $x \neq y$, we have with statements above that 
$$
0 \leq \dfrac{d_p(x,y)}{d_{\infty}(x,y)} \leq \dfrac{M}{N}=b, b>0.
$$
My problem here is how can I to prove with there is a constant positive $a$ such that $a \leq \dfrac{d_p(x,y)}{d_{\infty}(x,y)}$.
Thanks.

For any $p\ge 1$ and any $u=(u_1,u_2,\dots,u_n)\in\mathbb{R}^n$,
$$\|u\|_p = \left(\sum_{k=1}^n |u_k|^p\right)^{1/p} \le \left(\sum_{k=1}^n (\sup_k|u_k|)^p\right)^{1/p} = \left(n\|u\|_{\infty}\right)^{1/p}=n^{1/p}\|u\|_{\infty}$$
Estimate each term in the sum by the largest one. It's that simple. For the distance, apply this to $u=x-y$.
