ConceptComplete

Generating Functions - Key Properties

Generating functions possess rich algebraic structure that enables systematic manipulation and extraction of coefficients. Understanding these properties provides powerful techniques for solving enumeration problems.

DefinitionPartial Fractions Decomposition

A rational generating function G(x)=P(x)Q(x)G(x) = \frac{P(x)}{Q(x)} where deg(P)<deg(Q)\deg(P) < \deg(Q) can be decomposed into partial fractions. If Q(x)=(1r1x)m1(1r2x)m2(1rkx)mkQ(x) = (1-r_1x)^{m_1}(1-r_2x)^{m_2}\cdots(1-r_kx)^{m_k}, then: G(x)=i=1kj=1miAi,j(1rix)jG(x) = \sum_{i=1}^{k} \sum_{j=1}^{m_i} \frac{A_{i,j}}{(1-r_ix)^j}

Each term A(1rx)j\frac{A}{(1-rx)^j} has generating function An=0(n+j1j1)rnxnA\sum_{n=0}^{\infty} \binom{n+j-1}{j-1}r^n x^n.

Partial fractions allow us to extract closed formulas for sequence terms by converting rational functions into sums of geometric-like series.

DefinitionBinomial Series

For any real number α\alpha and x<1|x| < 1: (1+x)α=n=0(αn)xn(1+x)^\alpha = \sum_{n=0}^{\infty} \binom{\alpha}{n} x^n

where (αn)=α(α1)(αn+1)n!\binom{\alpha}{n} = \frac{\alpha(\alpha-1)\cdots(\alpha-n+1)}{n!} is the generalized binomial coefficient.

Special cases:

  • (1x)1=n=0xn(1-x)^{-1} = \sum_{n=0}^{\infty} x^n
  • (1x)2=n=0(n+1)xn(1-x)^{-2} = \sum_{n=0}^{\infty} (n+1)x^n
  • (1x)k=n=0(n+k1k1)xn(1-x)^{-k} = \sum_{n=0}^{\infty} \binom{n+k-1}{k-1} x^n
ExampleExtracting Coefficients

Find the coefficient of x10x^{10} in 1(1x)3(12x)\frac{1}{(1-x)^3(1-2x)}.

Using partial fractions: 1(1x)3(12x)=A12x+B1x+C(1x)2+D(1x)3\frac{1}{(1-x)^3(1-2x)} = \frac{A}{1-2x} + \frac{B}{1-x} + \frac{C}{(1-x)^2} + \frac{D}{(1-x)^3}

Solving (multiply both sides by denominator and compare coefficients or use cover-up method): =112x11x+2(1x)22(1x)3= \frac{1}{1-2x} - \frac{1}{1-x} + \frac{2}{(1-x)^2} - \frac{2}{(1-x)^3}

The coefficient of x10x^{10} is: 2101+2(11)2(122)=10241+22132=9132^{10} - 1 + 2(11) - 2\binom{12}{2} = 1024 - 1 + 22 - 132 = 913

DefinitionProduct Formula for EGFs

If A(x)A(x) and B(x)B(x) are exponential generating functions for {an}\{a_n\} and {bn}\{b_n\}, then A(x)B(x)A(x) \cdot B(x) is the EGF for: cn=k=0n(nk)akbnkc_n = \sum_{k=0}^{n} \binom{n}{k} a_k b_{n-k}

This accounts for the binomial coefficient arising from choosing which kk positions get elements from the first sequence.

DefinitionComposition of Generating Functions

The composition A(B(x))A(B(x)) where B(0)=0B(0) = 0 generates sequences related to tree structures and nested constructions. For OGFs, if A(x)=anxnA(x) = \sum a_n x^n and B(x)=bnxnB(x) = \sum b_n x^n, then: A(B(x))=n=0cnxnA(B(x)) = \sum_{n=0}^{\infty} c_n x^n

where cnc_n counts weighted paths or compositions.

Remark

The convolution structure for OGFs versus EGFs reflects different counting scenarios. OGF multiplication corresponds to "unlabeled" composition (like distributing indistinguishable balls), while EGF multiplication corresponds to "labeled" composition (like assigning distinct students to committees). The Lagrange inversion formula provides a method for extracting coefficients from implicitly defined generating functions, which is crucial in enumerative combinatorics for counting tree structures and solving functional equations. The connection between generating functions and complex analysis runs deep: coefficients can be extracted using Cauchy's residue theorem, linking discrete mathematics with contour integration.

These properties make generating functions a unified framework for solving diverse enumeration problems through algebraic manipulation.