[Math Talk #18] Secret of Shuffling - Derangements

mathsolver (53)in #math • 6 years ago (edited)

[1]

0. What is a Derangement?

In mathematics, permutation is an act of arranging all the members of a set into some sequence or order. A permutation which leaves no element fixed is called a derangement . In many cases, we deal with $n$ items (a finite set), each labeled as

$\left\{ 1, 2, ..., n \right\}$

So, put another way, a derangement of $n$ objects is a permutation of them without fixed points. The number of derangements of $n$ obejcts is usually written as $D_n$ .

For example, $D_1 = 0$ , since singleton set can not have any derangements. Also $D_2 = 1$ , because only derangement is assigning

$1 \mapsto 2,\ 2 \mapsto 1$

Let's look at one more example $D_3$ . There are total 2 derangements,

Element	1	2	3
maps to	2	3	1
maps to	3	1	2

In general, we need to ask the question:

Of the $n!$ different permutations of $n$ distinct objects, how many leave no object in its original place?

The answer to the question will provide a general formula for $D_n$ as well as

$p_n = \frac{D_n}{n!}$

the probability that a permutation of $n$ objects is a derangement.

1. Old but Gold Recurrence Relation

Viewing $D_1, D_2,D_3, ..., D_n,...$ as a sequence $\left\{ D_n \right\}$ , let's find a recurrence relation for $D_n$ . Since we already know the value of $D_1$ and $D_2$ , let's focus on the values of $D_n$ for $n \geq 3$ .

If a permutation

$\sigma: \left\{ 1, 2, ..., n \right\} \rightarrow \left\{ 1, 2, ..., n \right\}$

is a derangement, then it must be that $\sigma(1) \neq 1$ , which leaves $n-1$ possibilities for it. Among those, choose

$\sigma(1) = k (> 1)$

Now there are two possibilities.

1. If $\sigma(k) = 1$ , this is nothing but derangement of

$\left\{ 2, 3, ...,k-1, k+1,...n \right\} = \left\{ 1, 2, ..., n \right\} \setminus \left\{1,k \right\}$
and there are exactly $D_{n-2}$ of these.

2. If $\sigma(k) \neq 1$ , this is nothing but derangement of

$\left\{ 2, 3, ..., n \right\}$

$\left\{ 1, 2, ..., k-1, k+1, ..., n \right\}$

with a restriction $\sigma(k) \neq 1$ . The situation is totally equivalent to derangement of

$\left\{ 2, 3, ..., n \right\}$

if $1$ is viewed as $k$ ; so there are exactly $D_{n-1}$ of these.

Since our choice of $k$ was arbitrary, summing all those cases gives

$D_n = (n-1)(D_{n-1} + D_{n-2})\ (n \geq 3)$

How do we solve this?

Clever trick is to divide both sides by $n!$ so that

$\begin{align*} \frac{D_n}{n!} &= \frac{(n-1)D_{n-1}}{n!} + \frac{(n-1)D_{n-2}}{n!} \\ &= \frac{D_{n-1}}{(n-1)!} \left(1- \frac{1}{n} \right ) + \frac{D_{n-2}}{(n-2)!} \cdot \frac{1}{n}\\ \iff p_n &= \left(1- \frac{1}{n} \right ) p_{n-1} + \frac{1}{n} \cdot p_{n-2} \\ \iff (p_n &- p_{n-1}) = -\frac{1}{n} (p_{n-1} - p_{n-2}) \end{align*}$

Defining $q_n = p_n - p_{n-1}$ gives

$\begin{align*} q_n &= \left( -\frac{1}{n} \right ) \times \left( -\frac{1}{n-1} \right ) \times ... \times \left( -\frac{1}{1} \right ) \\ &= (-1)^{n} \frac{1}{n!} \end{align*}$

so that

$p_n = q_1 + ... + q_n = \sum_{i=1}^{n} (-1)^{i}\frac{1}{i!}$

with

$D_n = n! \left( \sum_{i=1}^{n} (-1)^{i}\frac{1}{i!} \right)$

2. The other way around

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We've established the formula for derangement, but there is a more intuitive way of relating the total number of permutations $n!$ with $D_n$ .

We can count the total $n!$ ways by partitioning the ways into $n+1$ disjoint subsets,

$T_0,T_1,T_2,...,T_n$

where $T_r$ is the set of permutations in which there are exactly $n-r$ fixed points. If there are $n-r$ fixed points, we first choose $n-r$ fixed points among $n$ , and arrange leftovers ( $r$ elements) to have no fixed points. This gives

$|S_r| = \binom{n}{n-r}D_r = \binom{n}{r}D_r$

so that

$n! = \sum_{r=0}^{n} \binom{n}{r} D_r$

This simple looking relationship is indeed very useful, as we will use in further sections.

3. Expected Number of Fixed points

A rather paradoxical situation arise when we calculate the expeced number of fixed points. Suppose that we perform the experiment of matching the initial arrangement $\left\{ 1, 2, ..., n \right\}$ with a random permutation of itself. Define a random variable $X$ as the number of fixed points in single experiment. Then clearly, $X$ can have values

$0, 1, 2, ..., n$

So the expectation is nothing but

$\mathbb{E}[X] = \sum_{r=0}^{n} r\mathbb{P}(X = r)$

In the previous Section, we already obtained the formula for $\mathbb{P}(X = r)$ , which is

$\mathbb{P}(X = r) = \frac{|T_{n-r}|}{n!} = \frac{\binom{n}{n-r} D_{n-r}}{n!}$

Now, straightforward calculation gives

$\begin{align*} \mathbb{E}[X] &= \sum_{r=0}^{n} r \frac{\binom{n}{n-r} D_{n-r}}{n!} \\ &= \sum_{s=0}^{n} (n-s) \frac{\binom{n}{s}D_s}{n!}\ (\because s = n-r) \\ &= \sum_{s=0}^{n-1} \frac{D_s}{s!(n-s-1)!}\ (\because n-n=0)\\ &= \frac{1}{(n-1)!}\sum_{s=0}^{n-1} \binom{n-1}{s}D_s \end{align*}$

Since

$\sum_{s=0}^{n-1} \binom{n-1}{s}D_s = (n-1)!$

by our previous result in Section 3, we get

$\mathbb{E}[X] = 1$

regardless of $n$ .

How should we interpret this weird result? Many of us tend to think the expected number of fixed points will grow as $n$ increases. However, the probability of making an element $i$ as a fixed point is

inversely proportional to $n$

This will help you understand what's going on more clearly. Consider a random variable $X_i$ such that $X_i = 1$ if $i$ is a fixed point, and 0 otherwise. Then the expectation of number of fixed points is

$\sum_{i=1}^{n} \mathbb{P}(X_i = 1) = \frac{1}{n} \times n =1$

so that $1/n, n$ cancel out making the expectation constant; 1.

4. Asymptotic Property of derangement

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Finally, one can ask that

What is the asymptotic behavior of $p_n$ as $n$ gets large?

This is nothing but asking the limit

$p=\lim_{n \rightarrow \infty} p_n = \sum_{i=0}^{\infty}(-1)^i \frac{1}{i!}$

one can easily show that the limit actually exists, and surprisingly, using the well known fact

$e^{x} = \sum_{i=0}^{\infty} \frac{x^i}{i!}\ (\text{for all } x \in \mathbb{R})$

gives $p = \frac{1}{e}$ .

Also one can ask that

What is the asymptotic behavior of $\mathbb{P}(X = r)$ as $n$ gets large?

Recall that we defined random variable $X$ as number of fixed points in single random permutation? Using the definition in Section 2 gives

$\mathbb{P}(X = r) = \frac{\binom{n}{n-r}D_{n-r}}{n!} = \frac{1}{r!} \cdot \frac{D_{n-r}}{(n-r)!}$

We already proved that $\frac{D_{n-r}}{(n-r)!} \rightarrow \frac{1}{e}$ , so that

$\mathbb{P}_n(X = r) \rightarrow \frac{e^{-1}}{r!}$

5. Computer Simulation

5-1. Expected number of fixed point revisited

Here is the MATLAB simulation for accumulated expectation of number of fixed points for various $n$ . As the number of simulation grows, you can see the accumulated expectation approaches to unity, regardless of $n$ .

5-2. Asymptotic property of Derangement revisited

Now here is the MATLAB simulation for $p_n$ the probability of having derangement in single permutation.

You can see it clearly converges to $1/e$ , oscillating in small region centered $1/e$ .

6. Conclusion

Number of derangement of $n$ items is equal to

$n!\sum_{r=0}^{n} \frac{(-1)^r}{r!}$
The expectated number of fixed points in single random permutation is 1, regardless of $n$ .
The probability of having derangement converges to $p_n \rightarrow 1/e$ .

7. Citations

[1] Image Source Link, By RokerHRO - Own work, CC BY 3.0

[2] Julian Havil, Nonplussed! Mathematical Proof of Implausible Ideas. Chapter 5 page 53

[3] Julian Havil, Nonplussed! Mathematical Proof of Implausible Ideas. Chapter 5 page 56

MATLAB plots are self-made.

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6 years ago in #math by mathsolver (53)

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[Math Talk #18] Secret of Shuffling - Derangements

0. What is a Derangement?

1. Old but Gold Recurrence Relation

2. The other way around

3. Expected Number of Fixed points

4. Asymptotic Property of derangement

5. Computer Simulation

5-1. Expected number of fixed point revisited

5-2. Asymptotic property of Derangement revisited

6. Conclusion

7. Citations

Hi @mathsolver!

Contribute to Open Source with utopian.io

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