# Legendre duality and statistical mechanics

I’d like to make another attempt at a topic I handled badly before: How Legendre duality shows up in statistical mechanics (or, at least, toy models thereof).

We are going to be considering systems with ${n}$ parts, and asking how many states they can be in. The answers will be exponential in ${n}$, and all that we care about is the base of that exponent. For example, the number of ways to partition an ${n}$ element set into two sets of size ${n/2}$ (if ${n}$ is even) is ${\binom{n}{n/2}}$ which Stirling’s formula shows to be ${\approx 2^n/\sqrt{2 \pi n} = \exp(n (\log 2 + o(1) ))}$. All we will care about is that ${\log 2}$.

How many ways can we partition an ${n}$ element set into a set of size roughly ${n/3}$ and a set of size roughly ${2n/3}$? More mucking around with Stirling’s formula will get us the answer $\displaystyle \exp\left (n \left(- \frac{1}{3} \log \frac{1}{3} - \frac{2}{3} \log \frac{2}{3} + o(1) \right) \right).$

I’ll show you a non-rigorous way to get that answer without getting into the details of Stirlings formula.

Let’s suppose there is a function ${h(p)}$ so that the number of ways to partition an ${n}$ element set into a piece of size ${pn}$ and a piece of size ${(1-p)n}$ is roughly ${\exp(n h(p))}$. Let’s look at the generating function $\displaystyle Z_n(x) = \sum_{k=0}^n \binom{n}{k} e^{xk}.$

Then we expect $\displaystyle Z_n(x) \approx \sum_{k=0}^{n} \exp\left(n h\!\left(\frac{k}{n} \right) + x n \frac{k}{n} \right).$

For fixed ${x}$, there is presumably some ${p_0}$ which maximizes ${h(p) + p x}$. The terms coming from ${k/n}$ near that ${p_0}$ will overwhelm the others, so we should have $\displaystyle Z_n(x) \approx \exp( n \max_{0 \leq p \leq 1} (h(p) + p x))$

or $\displaystyle \frac{1}{n} \log Z_n(x) \approx \max_{0 \leq p \leq 1} h(p) + p x.$

Set ${z(x) = \lim_{n \rightarrow \infty} \frac{1}{n} \log Z_n(x)}$. So we should have $\displaystyle z(x) = \max_{0 \leq p \leq 1} h(p) + p x.$

In other words, we expect ${z}$ and ${h}$ to be Legendre dual. In particular, we expect to have all of the following formulas: $\displaystyle z(x) = \max_{0 \leq p \leq 1} h(p) + p x \quad h(p) = \min_{x} z(x) - p x.$ $\displaystyle \frac{- \partial h}{\partial p} \circ \frac{\partial z}{\partial x} = \mathrm{Id} \quad \frac{\partial z}{\partial x} \circ \frac{- \partial h}{\partial p} = \mathrm{Id}.$

To spell the last one out in words, ${- \frac{\partial h}{\partial p}}$ is a function of ${p}$ and ${\frac{\partial z}{\partial x}}$ is a function of ${x}$; they should be inverse functions. (Keeping the signs straight is one of the real nuisances in this subject.)

In this case, we can compute ${z(x)}$ explicitly. We have ${Z_n(x) = (1+e^x)^n}$, so ${z(x) = \lim_{n \rightarrow \infty} \log(1+e^x) = \log (1+e^x)}$ so ${\frac{\partial z}{\partial x} = \frac{e^x}{1+e^x}}$. We use the relation that the derivatives of Legendre dual functions are inverse. Inverting ${p = \frac{e^x}{e^x+1}}$ gives ${x = \log p - \log (1-p)}$ so $\displaystyle \frac{\partial h}{\partial p} = - \log p + \log (1-p)$

and we can compute the integral to get $\displaystyle h(p) = - p \log p - (1-p) \log (1-p).$

I’m trying to learn to write shorter posts, so I’ll stop here for now. Next time, we compute the shape of a random partition of ${n}$.

## 3 thoughts on “Legendre duality and statistical mechanics”

1. Consider a fair 2^n-sided die. If I roll it and tell you what I got, you get n = -log_2(1/2^n) bits of information. If I paint half of it black and then roll it, then when it comes up black you only get 1 bit, but a number still gives n bits.

Consider an unfair coin that comes up heads p of the time, tails 1-p. How much information do you expect to get when I tell you what I flipped? Answer: this h(p), assuming the logs are log_2.