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Derangement
Derangement
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Number of possible permutations and derangements of n elements. n! (n factorial) is the number of n-permutations; !n (n subfactorial) is the number of derangements – n-permutations where all of the n elements change their initial places.

In combinatorial mathematics, a derangement is a permutation of the elements of a set in which no element appears in its original position. In other words, a derangement is a permutation that has no fixed points.

The number of derangements of a set of size n is known as the subfactorial of n or the n th derangement number or n th de Montmort number (after Pierre Remond de Montmort). Notations for subfactorials in common use include !n, Dn, dn, or n¡ .[a][1][2]

For n > 0 , the subfactorial !n equals the nearest integer to n!/e, where n! denotes the factorial of n and e ≈ 2.718281828... is Euler's number.[3]

The problem of counting derangements was first considered by Pierre Raymond de Montmort in his Essay d'analyse sur les jeux de hazard[4] in 1708; he solved it in 1713, as did Nicholas Bernoulli at about the same time.

Example

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The 9 derangements (from 24 permutations) are highlighted.

Suppose that a professor gave a test to 4 students – A, B, C, and D – and wants to let them grade each other's tests. How many ways could the professor hand the tests back to the students for grading, such that no student receives their own test back? Out of 24 possible permutations (4!) for handing back the tests,

ABCD, ABDC, ACBD, ACDB, ADBC, ADCB,
BACD, BADC, BCAD, BCDA, BDAC, BDCA,
CABD, CADB, CBAD, CBDA, CDAB, CDBA,
DABC, DACB, DBAC, DBCA, DCAB, DCBA.

there are only 9 derangements (shown in blue italics above). In every other permutation of this 4-member set, at least one student gets their own test back (shown in bold red).

Another version of the problem arises when we ask for the number of ways n letters, each addressed to a different person, can be placed in n pre-addressed envelopes so that no letter appears in the correctly addressed envelope.

Counting derangements

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Counting derangements of a set amounts to the hat-check problem, in which one considers the number of ways in which n hats (call them h1 through hn) can be returned to n people (P1 through Pn) such that no hat makes it back to its owner.[5]

Each person may receive any of the n − 1 hats that is not their own. Call the hat which the person P1 receives hi and consider hi's owner: Pi receives either P1's hat, h1, or some other. Accordingly, the problem splits into two possible cases:

  1. Pi receives a hat other than h1. This case is equivalent to solving the problem with n − 1 people and n − 1 hats because for each of the n − 1 people besides P1 there is exactly one hat from among the remaining n − 1 hats that they may not receive (for any Pj besides Pi, the unreceivable hat is hj, while for Pi it is h1). Another way to see this is to rename h1 to hi, where the derangement is more explicit: for any j from 2 to n, Pj cannot receive hj.
  2. Pi receives h1. In this case the problem reduces to n − 2 people and n − 2 hats, because P1 received hi's hat and Pi received h1's hat, effectively putting both out of further consideration.

For each of the n − 1 hats that P1 may receive, the number of ways that P2, ..., Pn may all receive hats is the sum of the counts for the two cases.

This gives us the solution to the hat-check problem: Stated algebraically, the number !n of derangements of an n-element set is for , where and [6]

The number of derangements of small lengths is given in the table below.

The number of derangements of an n-element set (sequence A000166 in the OEIS) for small n
n 0 1 2 3 4 5 6 7 8 9 10 11 12 13
!n 1 0 1 2 9 44 265 1,854 14,833 133,496 1,334,961 14,684,570 176,214,841 2,290,792,932

There are various other expressions for !n, equivalent to the formula given above. These include for and

for

where is the nearest integer function and is the floor function.[3][6]

Other related formulas include[3][7] and

The following recurrence also holds:[6]

Derivation by inclusion–exclusion principle

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One may derive a non-recursive formula for the number of derangements of an n-set, as well. For we define to be the set of permutations of n objects that fix the k th object. Any intersection of a collection of i of these sets fixes a particular set of i objects and therefore contains permutations. There are such collections, so the inclusion–exclusion principle yields and since a derangement is a permutation that leaves none of the n objects fixed, this implies

On the other hand, since we can choose elements to be in their own place and derange the other i elements in just !i ways, by definition.[8]

Growth of number of derangements as n approaches ∞

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From and by substituting one immediately obtains that This is the limit of the probability that a randomly selected permutation of a large number of objects is a derangement. The probability converges to this limit extremely quickly as n increases, which is why !n is the nearest integer to n!/e. The above semi-log graph shows that the derangement graph lags the permutation graph by an almost constant value.

More information about this calculation and the above limit may be found in the article on the statistics of random permutations.

Asymptotic expansion in terms of Bell numbers

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An asymptotic expansion for the number of derangements in terms of Bell numbers is as follows: where is any fixed positive integer, and denotes the -th Bell number. Moreover, the constant implied by the big O-term does not exceed .[9]

Generalizations

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The problème des rencontres asks how many permutations of a size-n set have exactly k fixed points.

Derangements are an example of the wider field of constrained permutations. For example, the ménage problem asks if n opposite-sex couples are seated man-woman-man-woman-... around a table, how many ways can they be seated so that nobody is seated next to his or her partner?

More formally, given sets A and S, and some sets U and V of surjections AS, we often wish to know the number of pairs of functions (fg) such that f is in U and g is in V, and for all a in A, f(a) ≠ g(a); in other words, where for each f and g, there exists a derangement φ of S such that f(a) = φ(g(a)).

Another generalization is the following problem:

How many anagrams with no fixed letters of a given word are there?

For instance, for a word made of only two different letters, say n letters A and m letters B, the answer is, of course, 1 or 0 according to whether n = m or not, for the only way to form an anagram without fixed letters is to exchange all the A with B, which is possible if and only if n = m. In the general case, for a word with n1 letters X1, n2 letters X2, ..., nr letters Xr, it turns out (after a proper use of the inclusion-exclusion formula) that the answer has the form for a certain sequence of polynomials Pn, where Pn has degree n. But the above answer for the case r = 2 gives an orthogonality relation, whence the Pn's are the Laguerre polynomials (up to a sign that is easily decided).[10]

in the complex plane

In particular, for the classical derangements, one has that where is the upper incomplete gamma function.

Computational complexity

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It is NP-complete to determine whether a given permutation group (described by a given set of permutations that generate it) contains any derangements.[11][12]

Footnotes

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References

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Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
A derangement is a of the elements of a set such that no element appears in its original or "fixed" position. In , the number of derangements of a set with n elements, denoted as !n or d(n), counts these fixed-point-free and is a fundamental object in . The concept originated in the early 18th century with Pierre Rémond de Montmort's work on probability in games of chance, particularly "Le problème des rencontres," which modeled the likelihood of no matches when shuffling a deck of cards. De Montmort formulated the problem in his 1708 book Essai d'analyse sur les jeux de hasard, and it was solved using the inclusion-exclusion principle by Nicholas Bernoulli in 1713, with de Montmort providing a recursive solution shortly after. The exact formula for the number of derangements is given by !n=n!k=0n(1)kk!,!n = n! \sum_{k=0}^{n} \frac{(-1)^k}{k!}, which can also be expressed recursively as d(n)=nd(n1)+(1)nd(n) = n \cdot d(n-1) + (-1)^n, with d(0)=1d(0) = 1 and d(1)=0d(1) = 0. For large n, !n is approximately n! / e, rounded to the nearest integer, where e ≈ 2.71828 is the base of the natural logarithm, reflecting the probability of a random permutation being a derangement approaching 1/e ≈ 0.3679. Derangements find applications in , such as the classic "hat check problem," where n people randomly retrieve hats and the probability that no one gets their own hat is !n / n! ≈ 1/e. They also appear in problems involving random mappings, error-correcting codes, and the analysis of permutations in group theory, including generalizations like k-derangements that allow up to k fixed points. The sequence of derangement numbers is cataloged in the as A000166, highlighting their role in broader mathematical structures like rook polynomials and of the second kind.

Fundamentals

Definition and Notation

A derangement is a σ\sigma of the set {1,2,,n}\{1, 2, \dots, n\} such that σ(i)i\sigma(i) \neq i for all i=1,2,,ni = 1, 2, \dots, n. The number of derangements of nn objects is denoted by the subfactorial !n!n, or alternatively by d(n)d(n). Derangements form the subset of the SnS_n consisting of all permutations with no fixed points. The probability that a in SnS_n is a derangement approaches 1/e1/e as nn approaches . The first few values of the subfactorial are given in the following table:
nn!n!n
01
10
21
32
49
544

Motivating Examples

One classic motivating example for derangements is the hat check problem, where n people check their hats at a or event, and the attendant returns the hats randomly; the goal is to find the probability that no one receives their own hat, which is given by !n / n!, where !n denotes the number of derangements of n items. This scenario illustrates the concept of a with no fixed points, as each hat represents an item that must not return to its original owner. A similar analogy arises in card shuffling, where dealing a standard deck of 52 cards results in a derangement if no card lands in its original position from the ordered deck; the probability of such an outcome approaches 1/e ≈ 0.3679 for large n, highlighting the rarity yet inevitability of completely "mixed" shuffles without fixed points. To build intuition with a small case, consider n=3 items labeled 1, 2, 3. There are 3! = 6 possible permutations:
  • (1, 2, 3): all fixed points (not a derangement)
  • (1, 3, 2): fixed point at 1
  • (2, 1, 3): fixed point at 3
  • (2, 3, 1): no fixed points (derangement, cycle (1 2 3))
  • (3, 1, 2): no fixed points (derangement, cycle (1 3 2))
  • (3, 2, 1): fixed point at 2
The two derangements are (2, 3, 1) and (3, 1, 2), out of six permutations, yielding a probability of 2/6 = 1/3. This concept traces its conceptual origins to early probability problems, such as a sorting letters into envelopes randomly with none in the correct one, serving as a foundational for derangements in combinatorial thinking.

Exact Enumeration

Inclusion-Exclusion Derivation

The inclusion-exclusion principle offers a direct combinatorial approach to derive the exact number of derangements of nn objects, denoted !n!n, by accounting for permutations that fix certain points and correcting for overcounts. Consider the set SS of all permutations of {1,2,,n}\{1, 2, \dots, n\}, so S=n!|S| = n!. For each i=1,,ni = 1, \dots, n, define AiA_i as the subset of permutations in SS that fix the element ii (i.e., π(i)=i\pi(i) = i). The derangements are precisely the permutations in the complement of the union i=1nAi\bigcup_{i=1}^n A_i, and the size of this complement is given by Si=1nAi.|S| - \left| \bigcup_{i=1}^n A_i \right|.
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