A Remark on the Enumeration of Rooted Labeled Trees
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Combinatorial Proof of an Abel-Type Identity∗
Combinatorial Proof of an Abel-type Identity∗ P. Mark Kayll Department of Mathematical Sciences, University of Montana Missoula MT 59812-0864, USA [email protected] David Perkins Department of Mathematics and Computer Science Houghton College, Houghton NY 14744, USA [email protected] Identity (1) below resulted from our investigation in [21] of chip-firing games on complete graphs Kn, for n ≥ 1; see, e.g., [2] for antecedents. The left side expresses the sum of the probabilities of a game experiencing firing sequences of each possible length ℓ = 0, 1,...,n. This note gives a combinatorial proof that these probabilities sum to unity. We first manipulate n n − 1 n ℓℓ−1(n + 1 − ℓ)n−1−ℓ + = 1 (1) n + 1 µℓ¶ n(n + 1)n−1 Xℓ=1 into a form amenable to combinatorial proof. Multiplying by n(n+1)n and n n+1 using the relation ℓ = ℓ (n + 1 − ℓ)/(n + 1), we transform (1) to the equivalent form ¡ ¢ ¡ ¢ n n + 1 ℓℓ−2(n + 1 − ℓ)n−1−ℓℓ(n + 1 − ℓ) = 2n(n + 1)n−1. (2) µ ℓ ¶ Xℓ=1 To see that (2) holds, first observe that the right side enumerates the pairs (T,~e ), where T is a spanning tree of Kn+1 for which one edge e (of 2000 MSC: Primary 05A19; Secondary 05C30, 60C05. ∗Preprint to appear in J. Combin. Math. Combin. Comput. 1 its n edges) has been distinguished and oriented (in one of two possible directions). The left side also enumerates these pairs. Given (T,~e ), notice that deleting the oriented edge ~e from T leaves behind a spanning forest of Kn+1 with two components L, R (that we may consider ordered from left to right). -
Relationship of Bell's Polynomial Matrix and K-Fibonacci Matrix
American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) ISSN (Print) 2313-4410, ISSN (Online) 2313-4402 © Global Society of Scientific Research and Researchers http://asrjetsjournal.org/ Relationship of Bell’s Polynomial Matrix and k-Fibonacci Matrix Mawaddaturrohmaha, Sri Gemawatib* a,bDepartment of Mathematics, University of Riau, Pekanbaru 28293, Indonesia aEmail: [email protected] bEmail: [email protected] Abstract The Bell‟s polynomial matrix is expressed as , where each of its entry represents the Bell‟s polynomial number.This Bell‟s polynomial number functions as an information code of the number of ways in which partitions of a set with certain elements are arranged into several non-empty section blocks. Furthermore, thek- Fibonacci matrix is expressed as , where each of its entry represents the k-Fibonacci number, whose first term is 0, the second term is 1 and the next term depends on a positive integer k. This article aims to find a matrix based on the multiplication of the Bell‟s polynomial matrix and the k-Fibonacci matrix. Then from the relationship between the two matrices the matrix is obtained. The matrix is not commutative from the product of the two matrices, so we get matrix Thus, the matrix , so that the Bell‟s polynomial matrix relationship can be expressed as Keywords: Bell‟s Polynomial Number; Bell‟s Polynomial Matrix; k-Fibonacci Number; k-Fibonacci Matrix. 1. Introduction Bell‟s polynomial numbers are studied by mathematicians since the 19th century and are named after their inventor Eric Temple Bell in 1938[5] Bell‟s polynomial numbers are represented by for each n and kelementsof the natural number, starting from 1 and 1 [2, p 135] Bell‟s polynomial numbers can be formed into Bell‟s polynomial matrix so that each entry of Bell‟s polynomial matrix is Bell‟s polynomial number and is represented by [11]. -
Math 958–Topics in Discrete Mathematics Spring Semester 2018 TR 09:30–10:45 in Avery Hall (AVH) 351 1 Instructor
Math 958{Topics in Discrete Mathematics Spring Semester 2018 TR 09:30{10:45 in Avery Hall (AVH) 351 1 Instructor Dr. Tri Lai Assistant Professor Department of Mathematics University of Nebraska - Lincoln Lincoln, NE 68588-0130, USA Email: [email protected] Website: http://www.math.unl.edu/$\sim$tlai3/ Office: Avery Hall 339 Office Hours: By Appointment 2 Prerequisites Math 450. 3 Contacting me The best way to contact with me is by email, [email protected]. Please put \[MATH 958]" at the beginning of the title and make sure to include your whole name in your email. Using your official UNL email to contact me is strongly recommended. My office is Avery Hall 339. My office hours are by appoinment. 4 Course Description Bijective Combinatorics is a branch of combinatorics focusing on bijections between mathematical objects. The fact is that there many totally different objects in mathematics that are actually equinumerous, in this case, we always want to seek for a simple bijection between them. For example, the number of ways to divide that a convex (n + 2)-gon into triangles by non-intersecting diagonals is equal to the number of full binary trees with n + 1 leaves. Both objects are counted 1 2n by the well known Catalan number Cn = n+1 n . Do you know that there are more than two hundred (!!) mathematical objects are counted by the Catalan number? In this course, we will go over a number of such \Catalan objects" and investigate the beautiful bijections between them. One more example is the well-known theorem by Euler about integer partitions stating that the number of ways to write a positive integer n as the sum of distinct positive integers is equal to the number of ways to write n as the sum of odd positive integers. -
Basic Combinatorics
Basic Combinatorics Carl G. Wagner Department of Mathematics The University of Tennessee Knoxville, TN 37996-1300 Contents List of Figures iv List of Tables v 1 The Fibonacci Numbers From a Combinatorial Perspective 1 1.1 A Simple Counting Problem . 1 1.2 A Closed Form Expression for f(n) . 2 1.3 The Method of Generating Functions . 3 1.4 Approximation of f(n) . 4 2 Functions, Sequences, Words, and Distributions 5 2.1 Multisets and sets . 5 2.2 Functions . 6 2.3 Sequences and words . 7 2.4 Distributions . 7 2.5 The cardinality of a set . 8 2.6 The addition and multiplication rules . 9 2.7 Useful counting strategies . 11 2.8 The pigeonhole principle . 13 2.9 Functions with empty domain and/or codomain . 14 3 Subsets with Prescribed Cardinality 17 3.1 The power set of a set . 17 3.2 Binomial coefficients . 17 4 Sequences of Two Sorts of Things with Prescribed Frequency 23 4.1 A special sequence counting problem . 23 4.2 The binomial theorem . 24 4.3 Counting lattice paths in the plane . 26 5 Sequences of Integers with Prescribed Sum 28 5.1 Urn problems with indistinguishable balls . 28 5.2 The family of all compositions of n . 30 5.3 Upper bounds on the terms of sequences with prescribed sum . 31 i CONTENTS 6 Sequences of k Sorts of Things with Prescribed Frequency 33 6.1 Trinomial Coefficients . 33 6.2 The trinomial theorem . 35 6.3 Multinomial coefficients and the multinomial theorem . 37 7 Combinatorics and Probability 39 7.1 The Multinomial Distribution . -
Bell Polynomials and Binomial Type Sequences Miloud Mihoubi
CORE Metadata, citation and similar papers at core.ac.uk Provided by Elsevier - Publisher Connector Discrete Mathematics 308 (2008) 2450–2459 www.elsevier.com/locate/disc Bell polynomials and binomial type sequences Miloud Mihoubi U.S.T.H.B., Faculty of Mathematics, Operational Research, B.P. 32, El-Alia, 16111, Algiers, Algeria Received 3 March 2007; received in revised form 30 April 2007; accepted 10 May 2007 Available online 25 May 2007 Abstract This paper concerns the study of the Bell polynomials and the binomial type sequences. We mainly establish some relations tied to these important concepts. Furthermore, these obtained results are exploited to deduce some interesting relations concerning the Bell polynomials which enable us to obtain some new identities for the Bell polynomials. Our results are illustrated by some comprehensive examples. © 2007 Elsevier B.V. All rights reserved. Keywords: Bell polynomials; Binomial type sequences; Generating function 1. Introduction The Bell polynomials extensively studied by Bell [3] appear as a standard mathematical tool and arise in combina- torial analysis [12]. Moreover, they have been considered as important combinatorial tools [11] and applied in many different frameworks from, we can particularly quote : the evaluation of some integrals and alterning sums [6,10]; the internal relations for the orthogonal invariants of a positive compact operator [5]; the Blissard problem [12, p. 46]; the Newton sum rules for the zeros of polynomials [9]; the recurrence relations for a class of Freud-type polynomials [4] and many others subjects. This large application of the Bell polynomials gives a motivation to develop this mathematical tool. -
A FAMILY of SEQUENCES of BINOMIAL TYPE 3 and If Jp +1= N Then This Difference Is 0
A FAMILY OF SEQUENCES OF BINOMIAL TYPE WOJCIECH MLOTKOWSKI, ANNA ROMANOWICZ Abstract. For delta operator aD bDp+1 we find the corresponding polynomial se- − 1 2 quence of binomial type and relations with Fuss numbers. In the case D 2 D we show that the corresponding Bessel-Carlitz polynomials are moments of the− convolu- tion semigroup of inverse Gaussian distributions. We also find probability distributions νt, t> 0, for which yn(t) , the Bessel polynomials at t, is the moment sequence. { } 1. Introduction ∞ A sequence wn(t) n=0 of polynomials is said to be of binomial type (see [10]) if deg w (t)= n and{ for} every n 0 and s, t R we have n ≥ ∈ n n (1.1) w (s + t)= w (s)w − (t). n k k n k Xk=0 A linear operator Q of the form c c c (1.2) Q = 1 D + 2 D2 + 3 D3 + ..., 1! 2! 3! acting on the linear space R[x] of polynomials, is called a delta operator if c1 = 0. Here D denotes the derivative operator: D1 := 0 and Dtn := n tn−1 for n 1. We6 will write Q = g(D), where · ≥ c c c (1.3) g(x)= 1 x + 2 x2 + 3 x3 + .... 1! 2! 3! There is one-to-one correspondence between sequences of binomial type and delta ∞ operators, namely if Q is a delta operator then there is unique sequence w (t) of { n }n=0 binomial type satisfying Qw0(t) = 0 and Qwn(t)= n wn−1(t) for n 1. -
Fitting and Graphing Discrete Distributions
Chapter 3 Fitting and graphing discrete distributions {ch:discrete} Discrete data often follow various theoretical probability models. Graphic displays are used to visualize goodness of fit, to diagnose an appropriate model, and determine the impact of individual observations on estimated parameters. Not everything that counts can be counted, and not everything that can be counted counts. Albert Einstein Discrete frequency distributions often involve counts of occurrences of events, such as ac- cident fatalities, incidents of terrorism or suicide, words in passages of text, or blood cells with some characteristic. Often interest is focused on how closely such data follow a particular prob- ability distribution, such as the binomial, Poisson, or geometric distribution, which provide the basis for generating mechanisms that might give rise to the data. Understanding and visualizing such distributions in the simplest case of an unstructured sample provides a building block for generalized linear models (Chapter 9) where they serve as one component. The also provide the basis for a variety of recent extensions of regression models for count data (Chapter ?), allow- ing excess counts of zeros (zero-inflated models), left- or right- truncation often encountered in statistical practice. This chapter describes the well-known discrete frequency distributions: the binomial, Pois- son, negative binomial, geometric, and logarithmic series distributions in the simplest case of an unstructured sample. The chapter begins with simple graphical displays (line graphs and bar charts) to view the distributions of empirical data and theoretical frequencies from a specified discrete distribution. It then describes methods for fitting data to a distribution of a given form and simple, effective graphical methods than can be used to visualize goodness of fit, to diagnose an appropriate model (e.g., does a given data set follow the Poisson or negative binomial?) and determine the impact of individual observations on estimated parameters. -
Bell Polynomials in Combinatorial Hopf Algebras Ammar Aboud, Jean-Paul Bultel, Ali Chouria, Jean-Gabriel Luque, Olivier Mallet
Bell polynomials in combinatorial Hopf algebras Ammar Aboud, Jean-Paul Bultel, Ali Chouria, Jean-Gabriel Luque, Olivier Mallet To cite this version: Ammar Aboud, Jean-Paul Bultel, Ali Chouria, Jean-Gabriel Luque, Olivier Mallet. Bell polynomials in combinatorial Hopf algebras. 2015. hal-00945543v2 HAL Id: hal-00945543 https://hal.archives-ouvertes.fr/hal-00945543v2 Preprint submitted on 8 Apr 2015 (v2), last revised 21 Jan 2016 (v3) HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Bell polynomials in combinatorial Hopf algebras Ammar Aboud,∗ Jean-Paul Bultel,y Ali Chouriaz Jean-Gabriel Luquexand Olivier Mallet{ April 3, 2015 Keywords: Bell polynomials, Symmetric functions, Hopf algebras, Faà di Bruno algebra, Lagrange inversion, Word symmetric functions, Set partitions. Abstract Partial multivariate Bell polynomials have been defined by E.T. Bell in 1934. These polynomials have numerous applications in Combinatorics, Analysis, Algebra, Probabilities etc. Many of the formulæ on Bell polynomials involve combinatorial objects (set partitions, set partitions into lists, permutations etc). So it seems natural to investigate analogous formulæ in some combinatorial Hopf algebras with bases indexed with these objects. In this paper we investigate the connexions between Bell polynomials and several combinatorial Hopf algebras: the Hopf algebra of symmetric functions, the Faà di Bruno algebra, the Hopf algebra of word symmetric functions etc. -
A Combinatorial Proof of the Log- Concavity of the Numbers Of
Journal of Combinatorial Theory, Series A 90, 293303 (2000) doi:10.1006Âjcta.1999.3040, available online at http:ÂÂwww.idealibrary.com on A Combinatorial Proof of the Log-Concavity of the Numbers of Permutations with k Runs MiklosBona1 University of Florida, Gainesville, Florida 32611 E-mail: bonaÄmath.ufl.edu and Richard Ehrenborg2 School of Mathematics, Institute for Advanced Study, Princeton, New Jersey 08540 E-mail: jrgeÄmath.ias.edu Communicated by the Managing Editors Received February 26, 1999 We combinatorially prove that the number R(n, k) of permutations of length n having k runs is a log-concave sequence in k, for all n. We also give a new com- binatorial proof for the log-concavity of the Eulerian numbers. 2000 Academic Press 1. INTRODUCTION Let p= p1 p2 }}}pn be a permutation of the set [1, 2, ..., n] written in the one-line notation. We say that p get changes direction at position i, if either pi&1<pi>pi+ j ,orpi&1>pi>pi+1, in other words, when pi is either a peak or a valley. We say that p has k runs if there are k&1 indices i so that p changes direction at these positions. So, for example, p=3561247 has 3 runs as p changes direction when i=3 and when i=4. A geometric way to represent a permutation and its runs by a diagram is shown in Fig. 1. The runs are the line segments (or edges) between two consecutive entries where p changes direction. So a permutation has k runs if it can be represented by k line segments so that the segments go ``up'' and ``down'' 1 The paper was written while the author's stay at IAS was supported by Trustee Ladislaus von Hoffmann, the Arcana Foundation. -
MAT344 Week 1
MAT344 Week 1 2019/Sep/9 1 Announcements 1. Make sure you have access to quercus and piazza. 2. Enroll in a tutorial. 3. Read the Syllabus and Study tips (also uploaded to https://www.math.toronto.edu/balazse/2019_Fall_ MAT344/). 4. Read Chapter 1 of the textbook [KT17]. It is an easy and fun read, and explores many of the questions that we will study later in the class. 5. Note: for some reason the textbook .pdf and print versions numbers differ slightly from the (more interactive) html version. When using references, we will refer to the .pdf version. 6. Problem set 1 will be sent out on Thursday. 2 This week This week, we are talking about 1. Basic counting techniques, 2. Permutations, 3. Combinations 3 Strings (Chapter 2.1 in [KT17]) Exercise 3.1 ([Bog04], Chapter 1.2 Problem 6). An ice cream parlor offers 12 different flavors of ice cream, and triple decker cones are made in homemade waffle cones. Having chocolate ice cream as the bottom scoop is different from having chocolate ice cream as the top scoop. How many different triple decker cones of ice cream are available? Solution: We have 12 choices for all of the bottom, middle, and top scoops, and if two sequence of choices differ at any point, they result in different triple decker ice cream cones. So we have 12 · 12 · 12 = 1728 different kinds of triple decker cones. The above problem is a typical one that we will encounter in this class. We are asked \How many...", and with some logic we come up with a number. -
A New Formula of Q-Fubini Numbers Via Goncharov Polynomials
A NEW FORMULA OF q-FUBINI NUMBERS VIA GONC˘AROV POLYNOMIALS Adel Hamdi 20 aoˆut 2019 Faculty of Science of Gabes, Department of Mathematics, Cit´eErriadh 6072, Zrig, Gabes, Tunisia Abstract Connected the generalized Gon˘carov polynomials associated to a pair (∂, Z) of a delta operator ∂ and an interpolation grid Z, introduced by Lorentz, Tringali and Yan in [7], with the theory of binomial enumeration and order statistics, a new q-deformed of these polynomials given in this paper allows us to derive a new combinatorial formula of q-Fubini numbers. A combinatorial proof and some nice algebraic and analytic properties have been expanded to the q-deformed version. Keywords: q-delta operators, polynomials of q-binomial type, Gon˘carov polynomials, order partitions, q-Fubini numbers. 2010 Mathematics Subject Classification. 05A10, 41A05, 05A40. 1 Introduction This paper grew out of the recent work, generalized Gon˘carov polynomials, of Lo- rentz, Tringali and Yan in [7] where these polynomials are seen as a basis of solutions for the Interpolation problem : Find a polynomial f(x) of degree n such that the ith delta operator ∂ of f(x) at a given complex number ai has value bi, for i = 0, 1, 2, .... There is a natural q-analog of this interpolation by replacing the delta operator with a q-delta arXiv:1908.06939v1 [math.CO] 19 Aug 2019 operator, we extend these polynomials into a generalized q-Gon˘carov basis (tn,q(x))n≥0, i N defined by the q-biorthogonality relation εzi (∂q(tn,q(x))) = [n]q!δi,n, for all i, n ∈ , where Z = (zi)i≥0 is a sequence of scalars and εzi the evaluation at zi. -
Combinatorics Combinatorics I Combinatorics II Product Rule Sum Rule I Sum Rule II
Combinatorics I Combinatorics Introduction Slides by Christopher M. Bourke Instructor: Berthe Y. Choueiry Combinatorics is the study of collections of objects. Specifically, counting objects, arrangement, derangement, etc. of objects along with their mathematical properties. Counting objects is important in order to analyze algorithms and Spring 2006 compute discrete probabilities. Originally, combinatorics was motivated by gambling: counting configurations is essential to elementary probability. Computer Science & Engineering 235 Introduction to Discrete Mathematics Sections 4.1-4.6 & 6.5-6.6 of Rosen [email protected] Combinatorics II Product Rule Introduction If two events are not mutually exclusive (that is, we do them separately), then we apply the product rule. A simple example: How many arrangements are there of a deck of 52 cards? Theorem (Product Rule) In addition, combinatorics can be used as a proof technique. Suppose a procedure can be accomplished with two disjoint A combinatorial proof is a proof method that uses counting subtasks. If there are n1 ways of doing the first task and n2 ways arguments to prove a statement. of doing the second, then there are n1 · n2 ways of doing the overall procedure. Sum Rule I Sum Rule II There is a natural generalization to any sequence of m tasks; If two events are mutually exclusive, that is, they cannot be done namely the number of ways m mutually exclusive events can occur at the same time, then we must apply the sum rule. is n1 + n2 + ··· nm−1 + nm Theorem (Sum Rule) We can give another formulation in terms of sets. Let If an event e1 can be done in n1 ways and an event e2 can be done A1,A2,...,Am be pairwise disjoint sets.