Proof of Shannon's entropy formula: Suppose that $p_1,...,p_n$, and $q_1,...,q_n$ are positive numbers... Suppose that $p_1 ,...,p_n,$ and $q_1,...,q_n$ are positive numbers and $\sum_i p_i$ = 1 = $\sum_i q_i$. 
Show that $\sum_{i=1}^m p_i \log(1/q_i)$ ≥ $\sum_{i=1}^m p_i \log(1/p_i)$ with equality if and only if $p_i$ = $q_i$, i = 1, ..., n. 

My Attempt (I think I made an error early on?):
\begin{align}
\sum_{i=1}^m p_i \log(1/q_i) - \sum_{i=1}^m p_i \log(1/p_i) &= \sum_{i=1}^m p_i [ \log(1/q_i) - \log (1/p_i)] \\
   &=\sum_{i=1}^m p_i \log(p_i/q_i) \\
   &= \sum_{i=1}^m p_i \ln(p_i/q_i) \\
   &≥ \sum_{i=1}^m (p_i/q_i -1) \\
   &=  \sum_{i=1}^m 1/q_i -\sum_{i=1}^m 1/p_i \\
   &= 0
\end{align}
 A: *

*It looks like your big error is in the 2nd line. It should instead be the following: For each $i=1,2,\ldots, n$: 


$p_i \ln (p_i/q_i) = -p_i \ln(q_i/p_i) \geq -p_i(q_i/p_i -1) = (p_i - q_i)$. Also, for your proof you need to observe that the equality $-p_i \ln(q_i/p_i) = -p_i(q_i/p_i -1)$ holds if and only if $q_i/p_i=1$ or equivalently, if and only if $p_i=q_i$.  [and so of course $-p_i\ln(p_i/q_i) =$ 0].


*The step $\sum_{i=1}^n p_i \log(p_i/q_i)$ $=$ $\sum_{i=1}^n p_i \ln(p_i/q_i)$ [at the end of your top line of your proof] is technically incorrect, as $\log A \not = \ln A$ for any positive $A$. For example, does $\log 10 = \ln 10$?


However, what is correct is that $\sum_{i=1}^n p_i \log(p_i/q_i)$ is nonnegative iff $\sum_{i=1}^n p_i \ln(p_i/q_i)$ is nonnegative, which is what it looked like you were trying to show. To this end, you could say at the end of your top line of your proof that $\sum_{i=1}^n p_i \log(p_i/q_i)$ is positive/zero iff $\sum_{i=1}^n p_i \ln(p_i/q_i)$ is positive/zero, which is what you will next show.
See if this is enough for you to correct.
A: The quantity $\sum_{i=1}^m p_i \log(1/q_i) - \sum_{i=1}^m p_i \log(1/p_i)$ is called the relative entropy (or Kullback–Leibler divergence) of the probability distributions $p$ and $q$. It is usually denoted as $D(p | q)$. What you are looking to prove here is that $D(p||q) \geq 0$.
$D(p||q) = \sum _i p_i \log \frac{p_i}{q_i} = \sum _i p(x) \left(- \log \frac{q_i}{p_i} \right) =  \mathbb{E}_{p_i} \left[ -\log \left( \frac{q_i}{p_i} \right) \right] $
The common way of proving this relies on the Jenson's inequality which states that if a function $f(x)$ is convex then 
$\mathbb{E}_{p_i} \left[ f(x) \right] \geq f\left( \mathbb{E}_{p_i}[x] \right)$
with equality only if and only if $x$ is a constant. Since the function $f(x) = -\log(x)$ is convex what follows from this is that
\begin{align}
D(p||q) &= \mathbb{E}_{p_i} \left[ -\log \left( \frac{q_i}{p_i} \right) \right] \\
&\geq -\log \left( \mathbb{E}_{p_i}\left[\frac{q_i}{p_i}\right] \right) \\
&= -\log \left( \sum _i p_i \frac{q_i}{p_i} \right) \\
&= -\log \left( \sum _i q_i \right) \\
&= -\log 1 \\
&= 0 \\
\implies D(p||q) \geq 0
\end{align}
From Jensons inequality, the inequality above will only equate if $p_i/q_i$ is a constant for all $i$. This implies that $p_i = k q_i$ for some constant $k$. What follows from this is 
\begin{align}
\sum_i p_i &= \sum_i k q_i \\
&= k \sum_i q_i \\
&= k \times 1 \\
\implies k = 1
\end{align}
It thus follows that we get equality only if and only if $p_i = q_i$ $\forall i$.
