Finding Direct Partition Bijections by Two-Directional Rewriting Techniques
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Principle of Inclusion-Exclusion
Inclusion / Exclusion — §3.1 67 Principle of Inclusion-Exclusion Example. Suppose that in this class, 14 students play soccer and 11 students play basketball. How many students play a sport? Solution. Let S be the set of students who play soccer and B be the set of students who play basketball. Then, |S ∪ B| = |S| + |B| . Inclusion / Exclusion — §3.1 68 Principle of Inclusion-Exclusion When A = A1 ∪···∪Ak ⊂U (U for universe) and the sets Ai are pairwise disjoint,wehave|A| = |A1| + ···+ |Ak |. When A = A1 ∪···∪Ak ⊂U and the Ai are not pairwise disjoint, we must apply the principle of inclusion-exclusion to determine |A|: |A1 ∪ A2| = |A1| + |A2|−|A1 ∩ A2| |A1 ∪ A2 ∪ A3| = |A1| + |A2| + |A3|−|A1 ∩ A2|−|A1 ∩ A3| −|A2 ∩ A3| + |A1 ∩ A2 ∩ A3| |A1 ∪···∪Am| = |Ai |− |Ai ∩ Aj | + Ai ∩ Aj ∩ Ak ··· ! ! ! " " " " It may be more convenient to apply inclusion/exclusion where the Ai are forbidden subsets of U,inwhichcase . Inclusion / Exclusion — §3.1 69 mmm...PIE The key to using the principle of inclusion-exclusion is determining the right choice of Ai .TheAi and their intersections should be easy to count and easy to characterize. Notation: π = p1p2 ···pn is the one-line notation for a permutation of [n] whose first element is p1,secondelementisp2,etc. Example. How many permutations p = p1p2 ···pn are there in which at least one of p1 and p2 are even? Solution. Let U be the set of n-permutations. Let A1 be the set of permutations where p1 is even. Let A2 be the set of permutations where p2 is even. -
Bijective Proofs for Schur Function Identities Which Imply Dodgson's Condensation Formula and Plücker Relations
Bijective proofs for Schur function identities which imply Dodgson’s condensation formula and Pl¨ucker relations Markus Fulmek Institut f¨urMathematik der Universit¨atWien Strudlhofgasse 4, A-1090 Wien, Austria [email protected] Michael Kleber Massachusetts Institute of Technology 77 Massachusetts Avenue, Cambridge, MA 02139, USA [email protected] Submitted: July 3, 2000; Accepted: March 7, 2001. MR Subject Classifications: 05E05 05E15 Abstract We present a “method” for bijective proofs for determinant identities, which is based on translating determinants to Schur functions by the Jacobi–Trudi identity. We illustrate this “method” by generalizing a bijective construction (which was first used by Goulden) to a class of Schur function identities, from which we shall obtain bijective proofs for Dodgson’s condensation formula, Pl¨ucker relations and a recent identity of the second author. 1 Introduction Usually, bijective proofs of determinant identities involve the following steps (cf., e.g, [19, Chapter 4] or [23, 24]): Expansion of the determinant as sum over the symmetric group, • Interpretation of this sum as the generating function of some set of combinatorial • objects which are equipped with some signed weight, Construction of an explicit weight– and sign–preserving bijection between the re- • spective combinatorial objects, maybe supported by the construction of a sign– reversing involution for certain objects. the electronic journal of combinatorics 8 (2001), #R16 1 Here, we will present another “method” of bijective proofs for determinant identitities, which involves the following steps: First, we replace the entries ai;j of the determinants by hλ i+j (where hm denotes i− • the m–th complete homogeneous function), Second, by the Jacobi–Trudi identity we transform the original determinant identity • into an equivalent identity for Schur functions, Third, we obtain a bijective proof for this equivalent identity by using the interpre- • tation of Schur functions in terms of nonintersecting lattice paths. -
MTH 102A - Part 1 Linear Algebra 2019-20-II Semester
MTH 102A - Part 1 Linear Algebra 2019-20-II Semester Arbind Kumar Lal1 February 3, 2020 1Indian Institute of Technology Kanpur 1 2 • Matrix A = behaves like the scalar 3 when multiplied with 2 1 1 1 2 1 1 x = . That is, = 3 . 1 2 1 1 1 • Physically: The Linear function f (x) = Ax magnifies the nonzero 1 2 vector ∈ C three (3) times. 1 1 1 • Similarly, A = −1 . So, behaves by changing the −1 −1 1 direction of the vector −1 1 2 2 • Take A = . Do I have a nonzero x ∈ C which gets 1 3 magnified by A? • So I am looking for x 6= 0 and α s.t. Ax = αx. Using x 6= 0, we have Ax = αx if and only if [αI − A]x = 0 if and only if det[αI − A] = 0. √ α − 1 −2 2 • det[αI − A] = det = α − 4α + 1. So α = 2 ± 3. −1 α − 3 √ √ 1 + 3 −2 • Take α = 2 + 3. To find x, solve √ x = 0: −1 3 − 1 √ 3 − 1 using GJE, for instance. We get x = . Moreover 1 √ √ √ 1 2 3 − 1 3 + 1 √ 3 − 1 Ax = = √ = (2 + 3) . 1 3 1 2 + 3 1 n • We call λ ∈ C an eigenvalue of An×n if there exists x ∈ C , x 6= 0 s.t. Ax = λx. We call x an eigenvector of A for the eigenvalue λ. We call (λ, x) an eigenpair. • If (λ, x) is an eigenpair of A, then so is (λ, cx), for each c 6= 0, c ∈ C. -
Inclusion‒Exclusion Principle
Inclusionexclusion principle 1 Inclusion–exclusion principle In combinatorics, the inclusion–exclusion principle (also known as the sieve principle) is an equation relating the sizes of two sets and their union. It states that if A and B are two (finite) sets, then The meaning of the statement is that the number of elements in the union of the two sets is the sum of the elements in each set, respectively, minus the number of elements that are in both. Similarly, for three sets A, B and C, This can be seen by counting how many times each region in the figure to the right is included in the right hand side. More generally, for finite sets A , ..., A , one has the identity 1 n This can be compactly written as The name comes from the idea that the principle is based on over-generous inclusion, followed by compensating exclusion. When n > 2 the exclusion of the pairwise intersections is (possibly) too severe, and the correct formula is as shown with alternating signs. This formula is attributed to Abraham de Moivre; it is sometimes also named for Daniel da Silva, Joseph Sylvester or Henri Poincaré. Inclusion–exclusion illustrated for three sets For the case of three sets A, B, C the inclusion–exclusion principle is illustrated in the graphic on the right. Proof Let A denote the union of the sets A , ..., A . To prove the 1 n Each term of the inclusion-exclusion formula inclusion–exclusion principle in general, we first have to verify the gradually corrects the count until finally each identity portion of the Venn Diagram is counted exactly once. -
Multipermutations 1.1. Generating Functions
CORE Metadata, citation and similar papers at core.ac.uk Provided by Elsevier - Publisher Connector JOURNALOF COMBINATORIALTHEORY, Series A 67, 44-71 (1994) The r- Multipermutations SEUNGKYUNG PARK Department of Mathematics, Yonsei University, Seoul 120-749, Korea Communicated by Gian-Carlo Rota Received October 1, 1992 In this paper we study permutations of the multiset {lr, 2r,...,nr}, which generalizes Gesse| and Stanley's work (J. Combin. Theory Ser. A 24 (1978), 24-33) on certain permutations on the multiset {12, 22..... n2}. Various formulas counting the permutations by inversions, descents, left-right minima, and major index are derived. © 1994 AcademicPress, Inc. 1. THE r-MULTIPERMUTATIONS 1.1. Generating Functions A multiset is defined as an ordered pair (S, f) where S is a set and f is a function from S to the set of nonnegative integers. If S = {ml ..... mr}, we write {m f(mD, .... m f(mr)r } for (S, f). Intuitively, a multiset is a set with possibly repeated elements. Let us begin by considering permutations of multisets (S, f), which are words in which each letter belongs to the set S and for each m e S the total number of appearances of m in the word is f(m). Thus 1223231 is a permutation of the multiset {12, 2 3, 32}. In this paper we consider a special set of permutations of the multiset [n](r) = {1 r, ..., nr}, which is defined as follows. DEFINITION 1.1. An r-multipermutation of the set {ml ..... ran} is a permutation al ""am of the multiset {m~ .... -
Basic Operations for Fuzzy Multisets
Basic operations for fuzzy multisets A. Riesgoa, P. Alonsoa, I. D´ıazb, S. Montesc aDepartment of Mathematics, University of Oviedo, Spain bDepartment of Informatics, University of Oviedo, Spain cDepartment of Statistics and O.R., University of Oviedo, Spain Abstract In their original and ordinary formulation, fuzzy sets associate each element in a reference set with one number, the membership value, in the real unit interval [0; 1]. Among the various existing generalisations of the concept, we find fuzzy multisets. In this case, membership values are multisets in [0; 1] rather than single values. Mathematically, they can be also seen as a generalisation of the hesitant fuzzy sets, but in this general environment, the information about repetition is not lost, so that, the opinions given by the experts are better managed. Thus, we focus our study on fuzzy multisets and their basic operations: complement, union and intersection. Moreover, we show how the hesitant operations can be worked out from an extension of the fuzzy multiset operations and investigate the important role that the concepts of order and sorting sequences play in the basic difference between these two related approaches. Keywords: Fuzzy multiset; Hesitant fuzzy set; Complement; Aggregate union; Aggregate intersection. 1. Introduction The concept of a fuzzy set was originally introduced by Lotfi A. Zadeh as an extension of classical set theory [15]. Fuzzy sets aim to account for real-life situations where there is either limited knowledge or some sort of implicit ambiguity about whether an element should be considered a member of a set. This extension from the classical (or crisp) sets to the fuzzy ones is accomplished by replacing the Boolean characteristic function of a set, which maps each element of the universal set to either 0 (non-member) or 1 (member), with a more sophisticated membership function that maps elements into the real interval [0; 1]. -
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. -
Symmetric Relations and Cardinality-Bounded Multisets in Database Systems
Symmetric Relations and Cardinality-Bounded Multisets in Database Systems Kenneth A. Ross Julia Stoyanovich Columbia University¤ [email protected], [email protected] Abstract 1 Introduction A relation R is symmetric in its ¯rst two attributes if R(x ; x ; : : : ; x ) holds if and only if R(x ; x ; : : : ; x ) In a binary symmetric relationship, A is re- 1 2 n 2 1 n holds. We call R(x ; x ; : : : ; x ) the symmetric com- lated to B if and only if B is related to A. 2 1 n plement of R(x ; x ; : : : ; x ). Symmetric relations Symmetric relationships between k participat- 1 2 n come up naturally in several contexts when the real- ing entities can be represented as multisets world relationship being modeled is itself symmetric. of cardinality k. Cardinality-bounded mul- tisets are natural in several real-world appli- Example 1.1 In a law-enforcement database record- cations. Conventional representations in re- ing meetings between pairs of individuals under inves- lational databases su®er from several consis- tigation, the \meets" relationship is symmetric. 2 tency and performance problems. We argue that the database system itself should pro- Example 1.2 Consider a database of web pages. The vide native support for cardinality-bounded relationship \X is linked to Y " (by either a forward or multisets. We provide techniques to be im- backward link) between pairs of web pages is symmet- plemented by the database engine that avoid ric. This relationship is neither reflexive nor antire- the drawbacks, and allow a schema designer to flexive, i.e., \X is linked to X" is neither universally simply declare a table to be symmetric in cer- true nor universally false. -
An Overview of the Applications of Multisets1
Novi Sad J. Math. Vol. 37, No. 2, 2007, 73-92 AN OVERVIEW OF THE APPLICATIONS OF MULTISETS1 D. Singh2, A. M. Ibrahim2, T. Yohanna3 and J. N. Singh4 Abstract. This paper presents a systemization of representation of multisets and basic operations under multisets, and an overview of the applications of multisets in mathematics, computer science and related areas. AMS Mathematics Subject Classi¯cation (2000): 03B65, 03B70, 03B80, 68T50, 91F20 Key words and phrases: Multisets, power multisets, dressed epsilon sym- bol, multiset ordering, Dershowtiz-Mana ordering 1. Introduction A multiset (mset, for short) is an unordered collection of objects (called the elements) in which, unlike a standard (Cantorian) set, elements are allowed to repeat. In other words, an mset is a set to which elements may belong more than once, and hence it is a non-Cantorian set. In this paper, we endeavour to present an overview of basics of multiset and applications. The term multiset, as Knuth ([46], p. 36) notes, was ¯rst suggested by N.G.de Bruijn in a private communication to him. Owing to its aptness, it has replaced a variety of terms, viz. list, heap, bunch, bag, sample, weighted set, occurrence set, and ¯reset (¯nitely repeated element set) used in di®erent contexts but conveying synonimity with mset. As mentioned earlier, elements are allowed to repeat in an mset (¯nitely in most of the known application areas, albeit in a theoretical development in¯nite multiplicities of elements are also dealt with (see [11], [37], [51], [72] and [30], in particular). The number of copies (([17], P. -
Week 3-4: Permutations and Combinations
Week 3-4: Permutations and Combinations March 10, 2020 1 Two Counting Principles Addition Principle. Let S1;S2;:::;Sm be disjoint subsets of a ¯nite set S. If S = S1 [ S2 [ ¢ ¢ ¢ [ Sm, then jSj = jS1j + jS2j + ¢ ¢ ¢ + jSmj: Multiplication Principle. Let S1;S2;:::;Sm be ¯nite sets and S = S1 £ S2 £ ¢ ¢ ¢ £ Sm. Then jSj = jS1j £ jS2j £ ¢ ¢ ¢ £ jSmj: Example 1.1. Determine the number of positive integers which are factors of the number 53 £ 79 £ 13 £ 338. The number 33 can be factored into 3 £ 11. By the unique factorization theorem of positive integers, each factor of the given number is of the form 3i £ 5j £ 7k £ 11l £ 13m, where 0 · i · 8, 0 · j · 3, 0 · k · 9, 0 · l · 8, and 0 · m · 1. Thus the number of factors is 9 £ 4 £ 10 £ 9 £ 2 = 7280: Example 1.2. How many two-digit numbers have distinct and nonzero digits? A two-digit number ab can be regarded as an ordered pair (a; b) where a is the tens digit and b is the units digit. The digits in the problem are required to satisfy a 6= 0; b 6= 0; a 6= b: 1 The digit a has 9 choices, and for each ¯xed a the digit b has 8 choices. So the answer is 9 £ 8 = 72. The answer can be obtained in another way: There are 90 two-digit num- bers, i.e., 10; 11; 12;:::; 99. However, the 9 numbers 10; 20;:::; 90 should be excluded; another 9 numbers 11; 22;:::; 99 should be also excluded. So the answer is 90 ¡ 9 ¡ 9 = 72. -
Rational Catalan Combinatorics 1
Rational Catalan Combinatorics Drew Armstrong et al. University of Miami www.math.miami.edu/∼armstrong March 18, 2012 This talk will advertise a definition. Here is it. Definition Let x be a positive rational number written as x = a=(b − a) for 0 < a < b coprime. Then we define the Catalan number 1 a + b (a + b − 1)! Cat(x) := = : a + b a; b a!b! !!! Please note the a; b-symmetry. This talk will advertise a definition. Here is it. Definition Let x be a positive rational number written as x = a=(b − a) for 0 < a < b coprime. Then we define the Catalan number 1 a + b (a + b − 1)! Cat(x) := = : a + b a; b a!b! !!! Please note the a; b-symmetry. This talk will advertise a definition. Here is it. Definition Let x be a positive rational number written as x = a=(b − a) for 0 < a < b coprime. Then we define the Catalan number 1 a + b (a + b − 1)! Cat(x) := = : a + b a; b a!b! !!! Please note the a; b-symmetry. Special cases. When b = 1 mod a ::: I Eug`eneCharles Catalan (1814-1894) (a < b) = (n < n + 1) gives the good old Catalan number n 1 2n + 1 Cat(n) = Cat = : 1 2n + 1 n I Nicolaus Fuss (1755-1826) (a < b) = (n < kn + 1) gives the Fuss-Catalan number n 1 (k + 1)n + 1 Cat = : (kn + 1) − n (k + 1)n + 1 n Special cases. When b = 1 mod a ::: I Eug`eneCharles Catalan (1814-1894) (a < b) = (n < n + 1) gives the good old Catalan number n 1 2n + 1 Cat(n) = Cat = : 1 2n + 1 n I Nicolaus Fuss (1755-1826) (a < b) = (n < kn + 1) gives the Fuss-Catalan number n 1 (k + 1)n + 1 Cat = : (kn + 1) − n (k + 1)n + 1 n Special cases. -
Essential Spectral Theory, Hall's Spectral Graph Drawing, the Fiedler
Spectral Graph Theory Lecture 2 Essential spectral theory, Hall's spectral graph drawing, the Fiedler value Daniel A. Spielman August 31, 2018 2.1 Eigenvalues and Optimization The Rayleigh quotient of a vector x with respect to a matrix M is defined to be x T M x : x T x At the end of the last class, I gave the following characterization of the largest eigenvalue of a symmetric matrix in terms of the Rayleigh quotient. Theorem 2.1.1. Let M be a symmetric matrix and let x be a non-zero vector that maximizes the Rayleigh quotient with respect to M : x T M x : x T x Then, x is an eigenvector of M with eigenvalue equal to the Rayleigh quotient. Moreover, this eigenvalue is the largest eigenvalue of M . Proof. We first observe that the maximum is achieved: As the Rayleigh quotient is homogeneous, it suffices to consider unit vectors x. As the set of unit vectors is a closed and compact set, the maximum is achieved on this set. Now, let x be a non-zero vector that maximizes the Rayleigh quotient. We recall that the gradient of a function at its maximum must be the zero vector. Let's compute that gradient. We have rx T x = 2x; and rx T M x = 2M x: So, x T M x (x T x)(2M x) − (x T M x)(2x) r = : x T x (x T x)2 In order for this to be zero, we must have x T M x M x = x: x T x That is, if and only if x is an eigenvector of M with eigenvalue equal to its Rayleigh quotient.