# Show extremum of the Entropy is a maximum

## Homework Statement

The Entropy of a probability distribution is given by,

$S = -k_B \sum _{i=1}^N p(i)\ln{p(i)}$

I've shown that the extremum of such a function is given by,

$S' = k_B \ln{N}$ (which is a positive quantity)

Now I want to show that this is a maximum by showing that

$S' - S = k_B \ln{N} + \sum _{i=1}^N p(i)\ln{p(i)} > 0$

## Homework Equations

The $p(i)$'s are constrained by

$\Sum_{i=1}^N p(i) =1$

## The Attempt at a Solution

I'm kind of stuck here. The second term is inherently negative, so it's not a priori obvious that $S' - S > 0$. I would probably want to take the ratio and show $\frac{S'}{S} \geq 1$ but I'm not sure how to do this.

Any ideas?

I suggest writing a Taylor series for $S$ in terms of the $p_i$ and looking at the second-order term.

Dick
Homework Helper

## Homework Statement

The Entropy of a probability distribution is given by,

$S = -k_B \sum _{i=1}^N p(i)\ln{p(i)}$

I've shown that the extremum of such a function is given by,

$S' = k_B \ln{N}$ (which is a positive quantity)

Now I want to show that this is a maximum by showing that

$S' - S = k_B \ln{N} + \sum _{i=1}^N p(i)\ln{p(i)} > 0$

## Homework Equations

The $p(i)$'s are constrained by

$\Sum_{i=1}^N p(i) =1$

## The Attempt at a Solution

I'm kind of stuck here. The second term is inherently negative, so it's not a priori obvious that $S' - S > 0$. I would probably want to take the ratio and show $\frac{S'}{S} \geq 1$ but I'm not sure how to do this.

Any ideas?

You only found one extremum which has a positive entropy (using Lagrange multipliers, I presume). The only other place you could have an extremum is on the boundary. The boundary of your set of p(i) would be the case where one of the p(i) is 1 and the rest are 0. What's the entropy there? You have to think about limits, since log(0) is undefined.

The boundary of your set of p(i) would be the case where one of the p(i) is 1 and the rest are 0.

Hello Dick!

The boundary of the space in question is actually much bigger than this - it's the set of points for which at least one p(i) is zero. So doing it this way, some work remains.

Dick
Homework Helper
Hello Dick!

The boundary of the space in question is actually much bigger than this - it's the set of points for which at least one p(i) is zero. So doing it this way, some work remains.

Good point. But if you fix one of your N p(i) to be zero. Then you are in the N-1 case with the remaining p(i). Suggests you use induction. In the case N=2, the boundary is the two points (p(1),p(2)) equal to (1,0) or (0,1).

Good point. But if you fix one of your N p(i) to be zero. Then you are in the N-1 case with the remaining p(i). Suggests you use induction. In the case N=2, the boundary is the two points (p(1),p(2)) equal to (1,0) or (0,1).

Correct But (at the risk of giving away the plot), I think the easiest way to demonstrate the global nature of the max is to notice that you can always increase the entropy by transferring probability from somewhere with a large p(i) to somewhere with a small p(i).

Dick
Correct 