# Intro

(Throughout this post I will be using the convention $\log \equiv \log_e$ and $\lg \equiv \log_2$).

In my post on Shannon entropy I introduced the notion of information entropy with respect to discrete random variables. There we derived, albeit in a hand-wavy fashion, the entropy formula:

$H(X) = H(p_1,\ldots,p_n) = -\sum p_i \cdot \lg p_i$

I hinted at the fact that $H$ is maximized when the underlying random variable $X$ is distributed uniformly. We saw this in the coin-toss example by seeing how any deviation from a fair-coin reduced the entropy.

My aim in this post is to prove (at least in the discrete case) that $H$ is indeed maximized when $X$ is distributed uniformly.

As an aside, one thing I neglected to mention in my original post on entropy is how $H$ is computed when $p = 0$. By convention, $-p\cdot \lg p \equiv 0$ when $p = 0$.

This agrees with our intuition since the case $p = 0$ is analogous to the case $p = 1$ in that the event will almost-surely not occur. And recall that $-1\cdot \lg 1 = 0$. So $p = 0$ can be seen as the symmetric case to $p = 1$, making the above convention not far-fetched.

You can actually show using methods of indeterminate forms that $p\cdot \lg p \to 0 \text{ as } p \to 0$. So that if we interpret the product as a limit $p\to 0$ the above equality ceases to be a convention but instead a fact.

$\log x \leq x - 1 \enspace \forall x >0$

Where equality holds if and only if $x = 1$.

# Step 1

The proof follows directly from the definition,

$\log x = \int_1^x \frac{1}{t} \enspace .$

If $x\geq 1$ then

$\log x = \int_1^x \frac{1}{t} \leq \int_1^x 1 = x - 1 \enspace .$

If $0 < x < 1$ then

$\log x = \int_1^x \frac{1}{t} = -\int_x^1 \frac{1}{t} \leq -\int_x^1 1 = -(1-x) = x - 1 \enspace .$

Where the inequality follows from the fact that

$t\in [x,1] \Rightarrow 1/t \geq 1 \Rightarrow -1/t \leq -1 \enspace .$

If $x = 1$ it follows trivially that $\log x = x - 1$. We want to show, however, that $x = 1$ is the only root.

For this we define the auxiliary function

$f(x) = \log x - x + 1$

Suppose that $f$ has two roots

\begin{aligned} x &= 1 \\ x_0 &\neq 1 \end{aligned}

Assume, without loss of generality, that $x_0 < 1$.

According to Rolle’s theorem, since $f$ is continuous on $[x_0,1]$ and differentiable on $(x_0,x)$, there must exist a point $\xi \in (x_0,1)$ such that

$f'(\xi) = \frac{1}{\xi} - 1 = 0 \enspace .$

However, the only root of $1/\xi -1 = 0$ is $\xi = 1$.

But $1 \notin (x_0,1)$.

Therefore, $x = 1$ is the only root of $f$.

Next we want to show that $\lg n$ is an upper bound for $H$.

# Step 2

By the change of base formula I can write

$\lg(n) = \frac{\log(n)}{\log(2)}$

Suppose for now that $p_j > 0$ for each $j = 1,\ldots, n$.

Then,

\begin{aligned} H(X) - \lg n &= H(p_1,\ldots,p_n) - \lg n \\ &= -\sum p_j \cdot \lg p_j - \lg n \\ &= -\sum p_j \cdot \frac{\log p_j }{\log 2} - \frac{\log p_j }{\log 2} \\ &= -\frac{1}{\log 2}\cdot \sum p_j \cdot\left(\log p_j + \log n \right) \qquad \small{\text{since} \sum p_j = 1} \\ &= -\frac{1}{\log 2}\cdot \sum p_j \cdot\left(\log p_j \cdot n \right) \qquad \star \\ &= \frac{1}{\log 2} \cdot \sum p_j \cdot\left(\log \frac{1}{p_j \cdot n} \right) \\ &\leq \frac{1}{\log 2} \cdot \sum p_j \cdot\left(\frac{1}{p_j \cdot n} - 1 \right) \qquad \small{\text{from the inequality in step 1}} \\ &= \frac{1}{\log 2} \cdot \sum \left(\frac{1}{n} - p_j \right) \\ &= \frac{1}{\log 2}\cdot\left(n\cdot\frac{1}{n} - 1\right) \\ &= \frac{1}{\log 2}\cdot\left(1 - 1\right) \\ &= 0 \\ &\Rightarrow H \leq \lg n \\ &\square \end{aligned}

Suppose $p_j = 0$ for some $j$. Recall from the preliminary discussion above that $-p\cdot\lg p \equiv 0$ by convention. Then from step $\star$ above simply note that

$p_j = 0 \Rightarrow -p_j\cdot \lg p = 0 < \frac{1}{n} - 0 = \frac{1}{n} - p_j \enspace$

which is precisely the inequality that is required to be shown in the last step. The inequality therefore still holds.

Finally, as discussed earlier, equality is reached if and only if

$\frac{1}{p_j\cdot n} = 1 \quad \forall j \enspace .$

That is, when $p_j = \frac{1}{n}$ for each $j$.

That is, when $X$ is uniformly distributed.

$\square$

Published 12 Jul 2018