Rencontres numbers

In combinatorial mathematics, the rencontres numbers are a triangular array of integers that enumerate permutations of the set { 1, ..., n } with specified numbers of fixed points: in other words, partial derangements. (Rencontre is French for encounter. By some accounts, the problem is named after a solitaire game.) For n ≥ 0 and 0 ≤ k ≤ n, the rencontres number Dnk is the number of permutations of { 1, ..., n } that have exactly k fixed points.

For example, if seven presents are given to seven different people, but only two are destined to get the right present, there are D7, 2 = 924 ways this could happen. Another often cited example is that of a dance school with 7 couples, where, after tea-break the participants are told to randomly find a partner to continue, then once more there are D7, 2 = 924 possibilities that 2 previous couples meet again by chance.

Numerical values

Here is the beginning of this array (sequence A008290 in the OEIS):


 k
n 
0 1 2 3 4 5 6 7 8
0 1
1 0 1
2 1 0 1
3 2 3 0 1
4 9 8 6 0 1
5 44 45 20 10 0 1
6 265 264 135 40 15 0 1
7 1854 1855 924 315 70 21 0 1
8 14833 14832 7420 2464 630 112 28 0 1
ordered by number of moved elements

The usual way (table above) to show the rencontres numbers is in columns corresponding to the number of fixed points k {\displaystyle k} .
But one can also order them in columns corresponding to the number of moved elements i = n k {\displaystyle i=n-k} (sequence A098825 in the OEIS).
Each entry in the table below on the left can be factored into two terms given in the table below on the right: the product of a binomial coefficient (given first in red) and a subfactorial (given second in blue).
In this order each column corresponds to one subfactorial:       T ( n , i ) = ( n i )     ! i {\displaystyle ~~~T(n,i)={\binom {n}{i}}~\cdot ~!i}

 i
n 
0 1 2 3 4 5 6 7 8
0 1
1 1 0
2 1 0 1
3 1 0 3 2
4 1 0 6 8 9
5 1 0 10 20 45 44
6 1 0 15 40 135 264 265
7 1 0 21 70 315 924 1855 1854
8 1 0 28 112 630 2464 7420 14832 14833
 i
n 
0 1 2 3 4 5 6 7 8
0 11
1 11 10
2 11 20 11
3 11 30 31 12
4 11 40 61 42 19
5 11 50 101 102 59 144
6 11 60 151 202 159 644 1265
7 11 70 211 352 359 2144 7265 11854
8 11 80 281 562 709 5644 28265 81854 114833

Formulas

The numbers in the k = 0 column enumerate derangements. Thus

D 0 , 0 = 1 , {\displaystyle D_{0,0}=1,\!}
D 1 , 0 = 0 , {\displaystyle D_{1,0}=0,\!}
D n + 2 , 0 = ( n + 1 ) ( D n + 1 , 0 + D n , 0 ) {\displaystyle D_{n+2,0}=(n+1)(D_{n+1,0}+D_{n,0})\!}

for non-negative n. It turns out that

D n , 0 = n ! e , {\displaystyle D_{n,0}=\left\lceil {\frac {n!}{e}}\right\rfloor ,}

where the ratio is rounded up for even n and rounded down for odd n. For n ≥ 1, this gives the nearest integer.

More generally, for any k 0 {\displaystyle k\geq 0} , we have

D n , k = ( n k ) D n k , 0 . {\displaystyle D_{n,k}={n \choose k}\cdot D_{n-k,0}.}

The proof is easy after one knows how to enumerate derangements: choose the k fixed points out of n; then choose the derangement of the other n − k points.

The numbers Dn,0/(n!) are generated by the power series ez/(1 − z); accordingly, an explicit formula for Dnm can be derived as follows:

D n , m = n ! m ! [ z n m ] e z 1 z = n ! m ! k = 0 n m ( 1 ) k k ! . {\displaystyle D_{n,m}={\frac {n!}{m!}}[z^{n-m}]{\frac {e^{-z}}{1-z}}={\frac {n!}{m!}}\sum _{k=0}^{n-m}{\frac {(-1)^{k}}{k!}}.}

This immediately implies that

D n , m = ( n m ) D n m , 0  and  D n , m n ! e 1 m ! {\displaystyle D_{n,m}={n \choose m}D_{n-m,0}\;\;{\mbox{ and }}\;\;{\frac {D_{n,m}}{n!}}\approx {\frac {e^{-1}}{m!}}}

for n large, m fixed.

Probability distribution

The sum of the entries in each row for the table in "Numerical Values" is the total number of permutations of { 1, ..., n }, and is therefore n!. If one divides all the entries in the nth row by n!, one gets the probability distribution of the number of fixed points of a uniformly distributed random permutation of { 1, ..., n }. The probability that the number of fixed points is k is

D n , k n ! . {\displaystyle {D_{n,k} \over n!}.}

For n ≥ 1, the expected number of fixed points is 1 (a fact that follows from linearity of expectation).

More generally, for i ≤ n, the ith moment of this probability distribution is the ith moment of the Poisson distribution with expected value 1.[1] For i > n, the ith moment is smaller than that of that Poisson distribution. Specifically, for i ≤ n, the ith moment is the ith Bell number, i.e. the number of partitions of a set of size i.

Limiting probability distribution

As the size of the permuted set grows, we get

lim n D n , k n ! = e 1 k ! . {\displaystyle \lim _{n\to \infty }{D_{n,k} \over n!}={e^{-1} \over k!}.}

This is just the probability that a Poisson-distributed random variable with expected value 1 is equal to k. In other words, as n grows, the probability distribution of the number of fixed points of a random permutation of a set of size n approaches the Poisson distribution with expected value 1.

See also

References

  1. ^ Jim Pitman, "Some Probabilistic Aspects of Set Partitions", American Mathematical Monthly, volume 104, number 3, March 1997, pages 201–209.
  • Riordan, John, An Introduction to Combinatorial Analysis, New York, Wiley, 1958, pages 57, 58, and 65.
  • Weisstein, Eric W. "Partial Derangements". MathWorld.
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