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Derangement
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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
[edit]
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
[edit]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:
- 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.
- 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.
| 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
[edit]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 ∞
[edit]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
[edit]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
[edit]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 A → S, we often wish to know the number of pairs of functions (f, g) 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 particular, for the classical derangements, one has that where is the upper incomplete gamma function.
Computational complexity
[edit]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]
Table of factorial and derangement values Permutations, Derangements, 0 1 =1×100
1 =1×100
= 1 1 1 =1×100
0 = 0 2 2 =2×100
1 =1×100
= 0.5 3 6 =6×100
2 =2×100
≈0.33333 33333 4 24 =2.4×101
9 =9×100
= 0.375 5 120 =1.20×102
44 =4.4×101
≈0.36666 66667 6 720 =7.20×102
265 =2.65×102
≈0.36805 55556 7 5,040 =5.04×103
1,854 ≈1.85×103
≈0.36785,71429 8 40,320 ≈4.03×104
14,833 ≈1.48×104
≈0.36788 19444 9 362,880 ≈3.63×105
133,496 ≈1.33×105
≈0.36787 91887 10 3,628,800 ≈3.63×106
1,334,961 ≈1.33×106
≈0.36787 94643 11 39,916,800 ≈3.99×107
14,684,570 ≈1.47×107
≈0.36787 94392 12 479,001,600 ≈4.79×108
176,214,841 ≈1.76×108
≈0.36787 94413 13 6,227,020,800 ≈6.23×109
2,290,792,932 ≈2.29×109
≈0.36787 94412 14 87,178,291,200 ≈8.72×1010
32,071,101,049 ≈3.21×1010
≈0.36787 94412 15 1,307,674,368,000 ≈1.31×1012
481,066,515,734 ≈4.81×1011
≈0.36787 94412 16 20,922,789,888,000 ≈2.09×1013
7,697,064,251,745 ≈7.70×1012
≈0.36787 94412 17 355,687,428,096,000 ≈3.56×1014
130,850,092,279,664 ≈1.31×1014
≈0.36787 94412 18 6,402,373,705,728,000 ≈6.40×1015
2,355,301,661,033,953 ≈2.36×1015
≈0.36787 94412 19 121,645,100,408,832,000 ≈1.22×1017
44,750,731,559,645,106 ≈4.48×1016
≈0.36787 94412 20 2,432,902,008,176,640,000 ≈2.43×1018
895,014,631,192,902,121 ≈8.95×1017
≈0.36787 94412 21 51,090,942,171,709,440,000 ≈5.11×1019
18,795,307,255,050,944,540 ≈1.88×1019
≈0.36787 94412 22 1,124,000,727,777,607,680,000 ≈1.12×1021
413,496,759,611,120,779,881 ≈4.13×1020
≈0.36787 94412 23 25,852,016,738,884,976,640,000 ≈2.59×1022
9,510,425,471,055,777,937,262 ≈9.51×1021
≈0.36787 94412 24 620,448,401,733,239,439,360,000 ≈6.20×1023
228,250,211,305,338,670,494,289 ≈2.28×1023
≈0.36787 94412 25 15,511,210,043,330,985,984,000,000 ≈1.55×1025
5,706,255,282,633,466,762,357,224 ≈5.71×1024
≈0.36787 94412 26 403,291,461,126,605,635,584,000,000 ≈4.03×1026
148,362,637,348,470,135,821,287,825 ≈1.48×1026
≈0.36787 94412 27 10,888,869,450,418,352,160,768,000,000 ≈1.09×1028
4,005,791,208,408,693,667,174,771,274 ≈4.01×1027
≈0.36787 94412 28 304,888,344,611,713,860,501,504,000,000 ≈3.05×1029
112,162,153,835,443,422,680,893,595,673 ≈1.12×1029
≈0.36787 94412 29 8,841,761,993,739,701,954,543,616,000,000 ≈8.84×1030
3,252,702,461,227,859,257,745,914,274,516 ≈3.25×1030
≈0.36787 94412 30 265,252,859,812,191,058,636,308,480,000,000 ≈2.65×1032
97,581,073,836,835,777,732,377,428,235,481 ≈9.76×1031
≈0.36787 94412
Footnotes
[edit]- ^ The name "subfactorial" originates with William Allen Whitworth.[1]
References
[edit]- ^ a b Cajori, Florian (2011). A History of Mathematical Notations: Two volumes in one. Cosimo, Inc. p. 77. ISBN 9781616405717 – via Google.
- ^ Graham, Ronald L.; Knuth, Donald E.; Patashnik, Oren (1994). Concrete Mathematics. Reading, MA: Addison–Wesley. ISBN 0-201-55802-5.
- ^ a b c Hassani, Mehdi (2003). "Derangements and applications". Journal of Integer Sequences. 6 (1). Article 03.1.2. Bibcode:2003JIntS...6...12H – via cs.uwaterloo.ca.
- ^ de Montmort, P.R. (1713) [1708]. Essay d'analyse sur les jeux de hazard (in French) (Revue & augmentée de plusieurs Lettres, seconde ed.). Paris, FR: Jacque Quillau (1708) / Jacque Quillau (1713).
- ^ Scoville, Richard (1966). "The Hat-Check Problem". American Mathematical Monthly. 73 (3): 262–265. doi:10.2307/2315337. JSTOR 2315337.
- ^ a b c Stanley, Richard (2012). Enumerative Combinatorics, volume 1 (2 ed.). Cambridge University Press. Example 2.2.1. ISBN 978-1-107-60262-5.
- ^ Weisstein, Eric W. "Subfactorial". MathWorld.
- ^ Bizley, M.T.L. (May 1967). "A note on derangements". Math. Gaz. 51 (376): 118–120. doi:10.2307/3614384. JSTOR 3614384.
- ^ Hassani, Mehdi (2020). "Derangements and Alternating Sum of Permutations by Integration". Journal of Integer Sequences. 23. Article 20.7.8.
- ^ Even, S.; Gillis, J. (1976). "Derangements and Laguerre polynomials". Mathematical Proceedings of the Cambridge Philosophical Society. 79 (1): 135–143. Bibcode:1976MPCPS..79..135E. doi:10.1017/S0305004100052154. S2CID 122311800. Retrieved 27 December 2011.
- ^ Lubiw, Anna (1981). "Some NP-complete problems similar to graph isomorphism". SIAM Journal on Computing. 10 (1): 11–21. doi:10.1137/0210002. MR 0605600.
- ^ Babai, László (1995). "Automorphism groups, isomorphism, reconstruction". Handbook of Combinatorics (PDF). Vol. 1, 2. Amsterdam, NL: Elsevier. ch. 27, pp. 1447–1540. MR 1373683 – via cs.uchicago.edu.
External links
[edit]- Baez, John (2003). "Let's get deranged!" (PDF) – via math.ucr.edu.
- Bogart, Kenneth P.; Doyle, Peter G. (1985). "Non-sexist solution of the ménage problem" – via math.dartmouth.edu.
- Weisstein, E.W. "Derangement". MathWorld / Wolfram Research – via mathworld.wolfram.com.
Derangement
View on GrokipediaFundamentals
Definition and Notation
A derangement is a permutation of the set such that for all .[3] The number of derangements of objects is denoted by the subfactorial , or alternatively by .[3] Derangements form the subset of the symmetric group consisting of all permutations with no fixed points.[3] The probability that a random permutation in is a derangement approaches as approaches infinity.[3] The first few values of the subfactorial are given in the following table:| 0 | 1 |
| 1 | 0 |
| 2 | 1 |
| 3 | 2 |
| 4 | 9 |
| 5 | 44 |
Motivating Examples
One classic motivating example for derangements is the hat check problem, where n people check their hats at a restaurant 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.[4] This scenario illustrates the concept of a permutation with no fixed points, as each hat represents an item that must not return to its original owner.[5] 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.[6] 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
Exact Enumeration
Inclusion-Exclusion Derivation
The inclusion-exclusion principle offers a direct combinatorial approach to derive the exact number of derangements of objects, denoted , by accounting for permutations that fix certain points and correcting for overcounts. Consider the set of all permutations of , so . For each , define as the subset of permutations in that fix the element (i.e., ). The derangements are precisely the permutations in the complement of the union , and the size of this complement is given by The principle of inclusion-exclusion expands the union as Thus, To evaluate the terms, note that for any distinct indices with , the intersection consists of permutations that fix all elements in , leaving the remaining elements to be permuted arbitrarily; hence, . There are exactly such subsets of size . Substituting these into the inclusion-exclusion expansion yields The term corresponds to , with no fixed points enforced. Each subsequent term alternates in sign to add back or subtract overcounted permutations that fix exactly points (though the principle counts intersections regardless of additional fixed points). Simplifying the general term, , so This closed-form sum corrects for the inclusion of permutations with fixed points by successively adjusting for multiple fixed points.[7] To verify the formula, consider . The total permutations are . Computing the sum: , so . Explicitly, the derangements of are and , confirming the count. For , , matching the single derangement .[8]Recurrence Relations
The number of derangements of objects, denoted , satisfies the recurrence relation for , with initial conditions and .[9] This relation arises from a combinatorial argument considering the position to which the first element is mapped in a derangement of . Suppose element 1 is mapped to position where ; there are choices for . If element is then mapped back to position 1, the remaining elements must be deranged, yielding possibilities. Otherwise, if element is not mapped to 1, the problem reduces to deranging the remaining elements after renaming to account for the derangement of 1 and , yielding possibilities. Summing over the choices for gives the recurrence.[10] An alternative recursive formula is for , again with . This form can be derived algebraically from the inclusion-exclusion principle applied to derangements.[9] The exponential generating function for the derangement numbers is This closed form facilitates further analysis of the sequence, such as extracting coefficients or deriving asymptotic properties.[9] These recurrences enable efficient computation of derangement numbers for moderate via dynamic programming, building a table iteratively. For example, the values up to are:| 0 | 1 |
| 1 | 0 |
| 2 | 1 |
| 3 | 2 |
| 4 | 9 |
| 5 | 44 |
| 6 | 265 |
| 7 | 1854 |
| 8 | 14833 |
| 9 | 133496 |
| 10 | 1334961 |
Asymptotic Behavior
Limiting Ratio to Factorials
In the context of the hat check problem, where n people randomly receive n hats and the probability that no one receives their own hat is the proportion of derangements among all permutations, this probability is given by !n / n!. The exact number of derangements !n, as derived via the inclusion-exclusion principle, is !n = n! \sum_{k=0}^n \frac{(-1)^k}{k!}. Dividing by n! yields the probability !n / n! = \sum_{k=0}^n \frac{(-1)^k}{k!}. As n approaches infinity, the partial sum \sum_{k=0}^n \frac{(-1)^k}{k!} converges to the infinite series expansion of e^{-1}, since the Taylor series for e^x is \sum_{k=0}^\infty \frac{x^k}{k!} and substituting x = -1 gives e^{-1} = \sum_{k=0}^\infty \frac{(-1)^k}{k!}. The remainder after n terms is bounded by the next term in the alternating series, ensuring the limit \lim_{n \to \infty} \sum_{k=0}^n \frac{(-1)^k}{k!} = e^{-1}. Thus, !n / n! \to 1/e as n \to \infty, implying the asymptotic relation !n \sim n! / e. This limit establishes that for large n, the number of derangements is approximately n! / e. Furthermore, !n is the nearest integer to n! / e for all n > 0, a property arising from the error term in the inclusion-exclusion sum. The error |!n - n! / e| is strictly less than 1/2 for n \geq 1, confirming the rounding behavior; for example, with n=5, !5 = 44 while 5! / e = 120 / e \approx 44.145, and the difference is about 0.145.Series Expansions and Approximations
The number of derangements admits a refined asymptotic expansion beyond the leading term , incorporating higher-order corrections expressed as a series in powers of . Specifically, where denotes the -th Bell number, counting the partitions of a -element set. This expansion arises from analyzing the remainder in the inclusion-exclusion series via integral methods and reveals a direct tie between derangement asymptotics and Bell numbers in the subleading terms. The truncation of the inclusion-exclusion series provides practical approximations for , with controlled error. The exact formula can be rewritten as , where the remainder satisfies for any fixed , as the tail of the alternating series for alternates and decreases. Truncating at small (e.g., ) yields high accuracy for large , since the error decays super-exponentially. An equivalent series expression for , obtained by reindexing the inclusion-exclusion sum, is which inverts the roles of fixed and deranged subsets and connects derangements to the sequence of factorials via binomial coefficients. Integral representations facilitate both exact evaluation and asymptotic analysis of derangements, often through Poissonization, which models fixed points via a Poisson process of rate 1. One such form for the remainder is , linking the deviation from the leading asymptotic to a logarithmic integral that can be expanded term-by-term for higher precision. Another representation is , derivable by term-by-term integration of the series and useful for probabilistic interpretations via the gamma function.Extensions
Partial Derangements
Partial derangements, also known as rencontres numbers, refer to the permutations of elements that have exactly fixed points, where . The notation is commonly used to denote this quantity.[11] The special case corresponds to the full derangement number , which counts permutations with no fixed points.[1] The rencontres numbers satisfy the relation , where is the subfactorial (derangement number) for elements; this arises by choosing the fixed points and deranging the remaining elements.[12] An explicit formula via inclusion-exclusion is This expression derives from selecting the fixed points and applying the inclusion-exclusion principle to count derangements on the rest.[11] A fundamental property is that , confirming that the rencontres numbers partition the full set of permutations by the number of fixed points.[12] For fixed and large , the asymptotic behavior is given by which reflects the limiting Poisson distribution (with mean 1) of the number of fixed points in a random permutation.[13] The following table provides values of for small :| 0 | 1 | ||||
| 1 | 0 | 1 | |||
| 2 | 1 | 0 | 1 | ||
| 3 | 2 | 3 | 0 | 1 | |
| 4 | 9 | 8 | 6 | 0 | 1 |