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The Egorychev method is a collection of techniques introduced by Georgy Egorychev for finding identities among sums of
binomial coefficient In mathematics, the binomial coefficients are the positive integers that occur as coefficients in the binomial theorem. Commonly, a binomial coefficient is indexed by a pair of integers and is written \tbinom. It is the coefficient of the t ...
s, Stirling numbers, Bernoulli numbers, Harmonic numbers, Catalan numbers and other combinatorial numbers. The method relies on two observations. First, many identities can be proved by extracting coefficients of
generating function In mathematics, a generating function is a way of encoding an infinite sequence of numbers () by treating them as the coefficients of a formal power series. This series is called the generating function of the sequence. Unlike an ordinary ser ...
s. Second, many generating functions are convergent power series, and coefficient extraction can be done using the Cauchy residue theorem (usually this is done by integrating over a small circular contour enclosing the origin). The sought-for identity can now be found using manipulations of integrals. Some of these manipulations are not clear from the generating function perspective. For instance, the integrand is usually a
rational function In mathematics, a rational function is any function that can be defined by a rational fraction, which is an algebraic fraction such that both the numerator and the denominator are polynomials. The coefficients of the polynomials need not be ...
, and the sum of the residues of a rational function is zero, yielding a new expression for the original sum. The
residue at infinity In complex analysis, a branch of mathematics, the residue at infinity is a residue of a holomorphic function on an annulus having an infinite external radius. The ''infinity'' \infty is a point added to the local space \mathbb C in order to render ...
is particularly important in these considerations. Some of the integrals employed by the Egorychev method are: * First binomial coefficient integral :: = \underset \; \frac = \frac \int_ \frac \; dz where 0 < \rho < \infty * Second binomial coefficient integral :: = \underset \; \frac = \frac \int_ \frac \; dz where 0 < \rho < 1 * Exponentiation integral :: n^k = k! \; \underset \; \frac = \frac \int_ \frac \; dz: where 0 < \rho < \infty *
Iverson bracket In mathematics, the Iverson bracket, named after Kenneth E. Iverson, is a notation that generalises the Kronecker delta, which is the Iverson bracket of the statement . It maps any statement to a function of the free variables in that statement ...
:: k \le n = \underset \; \frac\frac = \frac \int_ \frac\frac \; dz where 0 < \rho < 1 *
Stirling number of the first kind In mathematics, especially in combinatorics, Stirling numbers of the first kind arise in the study of permutations. In particular, the Stirling numbers of the first kind count permutations according to their number of cycles (counting fixed poin ...
:: \left \right= \frac \; \underset \; \frac \left(\log\frac\right)^k = \frac \frac \int_ \frac \left(\log\frac\right)^k \; dz where 0 < \rho < 1 *
Stirling number of the second kind In mathematics, particularly in combinatorics, a Stirling number of the second kind (or Stirling partition number) is the number of ways to partition a set of ''n'' objects into ''k'' non-empty subsets and is denoted by S(n,k) or \textstyle \le ...
:: \left\ = \frac \; \underset \; \frac = \frac \frac \int_ \frac \; dz where 0 < \rho < \infty.


Example I

Suppose we seek to evaluate :S_j(n) = \sum_^n (-1)^k which is claimed to be :(-1)^n . Introduce : = \frac \int_ \frac \; dz and : = \frac \int_ \frac \; dw. This yields for the sum : \begin & \frac \int_ \frac \frac \int_ \frac \sum_^n (-1)^k \frac \; dw \; dz \\ pt= & \frac \int_ \frac \frac \int_ \frac \left(1-\frac\right)^n \; dw \; dz \\ pt= & \frac \int_ \frac \frac \int_ \frac (-1-w-wz)^n \; dw \; dz \\ pt= & \frac \int_ \frac \frac \int_ \frac (1+w+wz)^n \; dw \; dz. \end This is :\frac \int_ \frac \frac \int_ \frac \sum_^n w^q (1+z)^q \; dw \; dz. Extracting the residue at w=0 we get : \begin & \frac \int_ \frac (1+z)^j \; dz \\ pt= & \frac \int_ \frac\; dz \\ pt= & (-1)^n \end thus proving the claim.


Example II

Suppose we seek to evaluate \sum_^n k . Introduce : = \frac \int_ \frac \frac \; dz. Observe that this is zero when k> n so we may extend k to infinity to obtain for the sum : \begin & \frac \int_ \frac \frac \sum_ k \frac \; dz \\ pt= & \frac \int_ \frac \frac \frac \; dz \\ pt= & \frac \int_ \frac \frac \frac \; dz. \end Now put z(1-z)=w so that (observe that with w=z+\cdots the image of , z, =\varepsilon with \varepsilon small is another closed circle-like contour which makes one turn and which we may certainly deform to obtain another circle , w, =\gamma) :z = \frac \quad\text\quad (1-2z)^2 = 1-4w and furthermore :dz = -\frac \times \frac \times (-4) \times (1-4w)^ \; dw = (1-4w)^ \; dw to get for the integral :\frac \int_ \frac \frac (1-4w)^ \; dw = \frac \int_ \frac \frac \; dw. This evaluates by inspection to (use the Newton binomial) : \begin & 4^ = 4^ = \frac \prod_^ (n-1/2-q) \\ = & \frac \prod_^ (2n-2q-1) = \frac \frac \\ pt= & \frac = \frac n . \end Here the mapping from z=0 to w=0 determines the choice of square root. For the conditions on \epsilon and \gamma we have that for the series to converge we require , z/(1-z), < 1 or \epsilon/(1-\epsilon) < 1 or \epsilon < 1/2. The closest that the image contour of , z, =\epsilon comes to the origin is \epsilon-\epsilon^2 so we choose \gamma < \epsilon-\epsilon^2 for example \gamma = \epsilon^2-\epsilon^3. This also ensures that \gamma < 1/4 so , w, =\gamma does not intersect the branch cut [1/4,\infty) (and is contained in the image of , z, =\epsilon). For example \epsilon = 1/3 and \gamma = 2/27 will work. This example also yields to simpler methods but was included here to demonstrate the effect of substituting into the variable of integration.


Computation using formal power series

We may use the change of variables rule 1.8 (5) from the Egorychev text (page 16) on the integral : \frac \int_ \frac \frac \frac \; dz = \underset \frac \frac \frac with A(z) = \frac and f(z) = \frac. We get h(z) = z (1-z) and find :\underset \frac \left.\left \frac \right_ with g the inverse of h. This becomes : \underset \frac \left.\left[ \frac \right]\_ or alternatively :\underset \frac \left.\left \frac \right_ = \underset \frac \left.\left \frac \right_ Observe that (1-2z)^2 = 1 - 4z + 4z^2 = 1-4z(1-z) = 1-4w so this is :\underset \frac \frac and the rest of the computation continues as before.


External links


Computational examples of using the Egorychev method to evaluate sums involving types of combinatorial numbers


References

* {{cite book , last1= Egorychev, first1= G. P. , authorlink=Georgy Petrovich Egorychev , title= Integral representation and the Computation of Combinatorial sums , publisher= American Mathematical Society, year= 1984 , ref= Ego84 , url=https://books.google.com/books/about/Integral_Representation_and_the_Computat.html?id=QTfxn_gEbVYC Factorial and binomial topics *