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Enumerative combinatorics
Enumerative combinatorics
from Wikipedia
3 out of 4638576[1] or out of 580717,[2] if rotations and reflections are not counted as distinct, Hamiltonian cycles on a square grid graph 8х8

Enumerative combinatorics is an area of combinatorics that deals with the number of ways that certain patterns can be formed. Two examples of this type of problem are counting combinations and counting permutations. More generally, given an infinite collection of finite sets Si indexed by the natural numbers, enumerative combinatorics seeks to describe a counting function which counts the number of objects in Sn for each n. Although counting the number of elements in a set is a rather broad mathematical problem, many of the problems that arise in applications have a relatively simple combinatorial description. The twelvefold way provides a unified framework for counting permutations, combinations and partitions.

The simplest such functions are closed formulas, which can be expressed as a composition of elementary functions such as factorials, powers, and so on. For instance, as shown below, the number of different possible orderings of a deck of n cards is f(n) = n!. The problem of finding a closed formula is known as algebraic enumeration, and frequently involves deriving a recurrence relation or generating function and using this to arrive at the desired closed form.

Often, a complicated closed formula yields little insight into the behavior of the counting function as the number of counted objects grows. In these cases, a simple asymptotic approximation may be preferable. A function is an asymptotic approximation to if as . In this case, we write

Generating functions

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Generating functions are used to describe families of combinatorial objects. Let denote the family of objects and let F(x) be its generating function. Then

where denotes the number of combinatorial objects of size n. The number of combinatorial objects of size n is therefore given by the coefficient of . Some common operation on families of combinatorial objects and its effect on the generating function will now be developed. The exponential generating function is also sometimes used. In this case it would have the form

Once determined, the generating function yields the information given by the previous approaches. In addition, the various natural operations on generating functions such as addition, multiplication, differentiation, etc., have a combinatorial significance; this allows one to extend results from one combinatorial problem in order to solve others.

Union

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Given two combinatorial families, and with generating functions F(x) and G(x) respectively, the disjoint union of the two families () has generating function F(x) + G(x).

Pairs

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For two combinatorial families as above the Cartesian product (pair) of the two families () has generating function F(x)G(x).

Sequences

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A (finite) sequence generalizes the idea of the pair as defined above. Sequences are arbitrary Cartesian products of a combinatorial object with itself. Formally:

To put the above in words: An empty sequence or a sequence of one element or a sequence of two elements or a sequence of three elements, etc. The generating function would be:

Combinatorial structures

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The above operations can now be used to enumerate common combinatorial objects including trees (binary and plane), Dyck paths and cycles. A combinatorial structure is composed of atoms. For example, with trees the atoms would be the nodes. The atoms which compose the object can either be labeled or unlabeled. Unlabeled atoms are indistinguishable from each other, while labelled atoms are distinct. Therefore, for a combinatorial object consisting of labeled atoms a new object can be formed by simply swapping two or more atoms.

Binary and plane trees

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Binary and plane trees are examples of an unlabeled combinatorial structure. Trees consist of nodes linked by edges in such a way that there are no cycles. There is generally a node called the root, which has no parent node. In plane trees each node can have an arbitrary number of children. In binary trees, a special case of plane trees, each node can have either two or no children. Let denote the family of all plane trees. Then this family can be recursively defined as follows:

In this case represents the family of objects consisting of one node. This has generating function x. Let P(x) denote the generating function . Putting the above description in words: A plane tree consists of a node to which is attached an arbitrary number of subtrees, each of which is also a plane tree. Using the operation on families of combinatorial structures developed earlier, this translates to a recursive generating function:

After solving for P(x):

An explicit formula for the number of plane trees of size n can now be determined by extracting the coefficient of xn:

Note: The notation [xn] f(x) refers to the coefficient of xn in f(x). The series expansion of the square root is based on Newton's generalization of the binomial theorem. To get from the fourth to fifth line manipulations using the generalized binomial coefficient is needed.

The expression on the last line is equal to the (n − 1)st Catalan number. Therefore, pn = cn−1.

See also

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References

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Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
Enumerative combinatorics is a branch of focused on counting the number of elements in finite sets, particularly those that arise from combinatorial structures such as permutations, partitions, and posets, often seeking closed-form expressions, recurrences, or generating functions for the counting sequence. This field addresses a wide range of problems, from basic enumerations like the number of subsets of an n-element set (given by 2n) to more intricate counts involving lattice paths, polyominoes, and hyperplane arrangements, emphasizing both exact and asymptotic results. Key techniques include generating functions—such as ordinary generating functions f(n)xn\sum f(n) x^n and exponential generating functions f(n)xnn!\sum f(n) \frac{x^n}{n!}—which encode counting information and facilitate algebraic manipulations; bijective proofs, which establish equalities by exhibiting one-to-one correspondences between sets; and sieve methods like inclusion-exclusion and Möbius inversion over posets, which handle overcounts and constraints effectively. For instance, the number of derangements (permutations with no fixed points) is computed via the inclusion-exclusion principle as D(n)=k=0n(1)kn!k!D(n) = \sum_{k=0}^n (-1)^k \frac{n!}{k!}. Enumerative combinatorics intersects with numerous areas, including algebra (e.g., symmetric functions and representations), geometry (e.g., Ehrhart quasipolynomials for counting lattice points in polytopes), probability (e.g., random walks and models), and (e.g., arrangements and manifolds). Notable objects of study include Cn=1n+1(2nn)C_n = \frac{1}{n+1} \binom{2n}{n}, which count Dyck paths, binary trees, and non-crossing partitions; , which enumerate partitions into nonempty subsets; and q-analogues, which generalize classical counts to incorporate additional parameters like inversions in permutations. The field has seen significant development since the mid-20th century, driven by computational tools and connections to physics and , with foundational texts providing systematic treatments of these themes.

Basic Principles

Fundamental Counting Rules

The fundamental counting rules provide the foundational principles for enumerative combinatorics, enabling the systematic tallying of discrete objects through basic additive and multiplicative operations on sets of possibilities. These rules address scenarios where outcomes arise from either mutually exclusive choices or independent selections, forming the basis for more advanced counting techniques without relying on specific structural formulas. The sum rule, also called the addition principle or disjoint union rule, states that if a collection of possibilities can be divided into two or more mutually exclusive (disjoint) subsets, then the total number of possibilities equals the sum of the sizes of those subsets. Formally, for disjoint sets AA and BB, AB=A+B|A \cup B| = |A| + |B|. This rule applies when options cannot overlap, such as choosing between distinct categories. For example, suppose a bakery offers 5 varieties of doughnuts or 3 varieties of bagels, with no overlap in choices; the total number of selections is then 5+3=85 + 3 = 8. In contrast, the , known as the multiplication principle, governs situations where an outcome results from making independent choices across multiple stages or categories, with the total number of outcomes being the product of the options at each stage. For sets AA and BB, this corresponds to A×B=AB|A \times B| = |A| \cdot |B|. A straightforward illustration is flipping a twice, where each flip has 2 possible results (heads or tails), independent of the other, yielding 2×2=42 \times 2 = 4 sequences: heads-heads, heads-tails, tails-heads, and tails-tails. This principle extends iteratively to more stages, such as three coin flips producing 23=82^3 = 8 outcomes. These rules trace their early formalization to the , amid the emergence of modern in Europe, with playing a pivotal role through his dissertation . In this work, Leibniz explored systematic enumeration and combinations of basic elements as a foundation for , influencing the development of counting principles without explicitly naming the sum and product rules as such. Practical applications of these rules appear in simple probabilistic experiments, such as rolls. A single standard six-sided die has 6 faces, so rolling two independently produces 6×6=366 \times 6 = 36 possible outcomes, each pair equally likely. Similarly, the sum rule can tally disjoint events, like the number of ways to get a specific sum (e.g., 7) across these outcomes, though the total space relies on the . Such examples underscore how these principles scale to model real-world counting tasks efficiently.

Permutations and Combinations

Permutations refer to the ordered arrangements of distinct objects. The number of permutations of kk objects selected from nn distinct objects, denoted P(n,k)P(n,k), is given by the formula P(n,k)=n!(nk)!P(n,k) = \frac{n!}{(n-k)!}, where n!n! represents the of nn, the number of full permutations of nn distinct objects. This counts the ways to choose and arrange kk positions sequentially from the nn available items, starting with nn choices for the first position, n1n-1 for the second, and so on down to nk+1n-k+1. Combinations, in contrast, involve selections of objects without regard to order. The number of ways to choose kk objects from nn distinct ones, denoted C(n,k)C(n,k) or (nk)\binom{n}{k}, is (nk)=n!k!(nk)!\binom{n}{k} = \frac{n!}{k!(n-k)!}, also known as the . This formula arises by dividing the number of permutations P(n,k)P(n,k) by k!k!, accounting for the fact that each unordered selection corresponds to k!k! possible orderings. The binomial coefficients appear prominently in the binomial theorem, which states that (1+x)n=k=0n(nk)xk(1 + x)^n = \sum_{k=0}^{n} \binom{n}{k} x^k. A combinatorial proof interprets the left side as the expansion of the product of nn factors, each (1+x)(1 + x), where each term in the expansion arises from selecting either 1 or xx from each factor and multiplying them together. The coefficient of xkx^k counts the number of ways to choose xx from exactly kk of the nn factors (and 1 from the rest), which is precisely (nk)\binom{n}{k}. For example, the number of ways to arrange 5 distinct books on a shelf is 5!=1205! = 120, illustrating full permutations, while choosing 3 committee members from 7 people without assigning roles uses (73)=35\binom{7}{3} = 35, a . Binomial coefficients can be computed recursively using , where each entry is the sum of the two entries above it in the previous row, starting with row 0 as 1. The entries in row nn are exactly (n0),(n1),,(nn)\binom{n}{0}, \binom{n}{1}, \dots, \binom{n}{n}, providing an efficient tabular method for calculation.

Analytic Methods

Recurrence Relations

In enumerative combinatorics, recurrence relations provide a fundamental tool for counting combinatorial objects by expressing the number of structures of size nn in terms of those of smaller sizes. These relations arise naturally when a combinatorial construction decomposes a larger object into smaller substructures, leading to equations where the sequence ana_n satisfies an=f(an1,an2,,ank)a_n = f(a_{n-1}, a_{n-2}, \dots, a_{n-k}) for some function ff and fixed kk. A classic example is the , which counts the number of ways to tile a 1×n1 \times n board using 1×11 \times 1 squares and 1×21 \times 2 , satisfying the recurrence Fn=Fn1+Fn2F_n = F_{n-1} + F_{n-2} with initial conditions F1=1F_1 = 1 and F2=2F_2 = 2, as the last tile is either a square (leaving an n1n-1 board) or a domino (leaving an n2n-2 board). Linear homogeneous recurrence relations with constant coefficients, common in such counting problems, can be solved using the characteristic equation method. For a relation an=c1an1+c2an2++ckanka_n = c_1 a_{n-1} + c_2 a_{n-2} + \dots + c_k a_{n-k}, the characteristic equation is rkc1rk1ck=0r^k - c_1 r^{k-1} - \dots - c_k = 0, whose roots determine the general solution as a linear combination of terms like rinr_i^n (or nmrinn^m r_i^n for repeated roots). Applying this to the Fibonacci recurrence yields the characteristic equation r2r1=0r^2 - r - 1 = 0, with roots ϕ=1+52\phi = \frac{1 + \sqrt{5}}{2}
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