Sets, Functions

Total Page:16

File Type:pdf, Size:1020Kb

Sets, Functions Sets 1 Sets Informally: A set is a collection of (mathematical) objects, with the collection treated as a single mathematical object. Examples: • real numbers, • complex numbers, C • integers, • All students in our class Defining Sets Sets can be defined directly: e.g. {1,2,4,8,16,32,…}, {CSC1130,CSC2110,…} Order, number of occurence are not important. e.g. {A,B,C} = {C,B,A} = {A,A,B,C,B} A set can be an element of another set. {1,{2},{3,{4}}} Defining Sets by Predicates The set of elements, x, in A such that P(x) is true. {}x APx| ( ) The set of prime numbers: Commonly Used Sets • N = {0, 1, 2, 3, …}, the set of natural numbers • Z = {…, -2, -1, 0, 1, 2, …}, the set of integers • Z+ = {1, 2, 3, …}, the set of positive integers • Q = {p/q | p Z, q Z, and q ≠ 0}, the set of rational numbers • R, the set of real numbers Special Sets • Empty Set (null set): a set that has no elements, denoted by ф or {}. • Example: The set of all positive integers that are greater than their squares is an empty set. • Singleton set: a set with one element • Compare: ф and {ф} – Ф: an empty set. Think of this as an empty folder – {ф}: a set with one element. The element is an empty set. Think of this as an folder with an empty folder in it. Venn Diagrams • Represent sets graphically • The universal set U, which contains all the objects under consideration, is represented by a rectangle. The set varies depending on which objects are of interest. • Inside the rectangle, circles or other geometrical figures are used to represent sets. • Sometimes points are used to represent the particular elements of the set. U a u V e o i 7 Membership {7, “Albert”, /2, T} xA x is an element of A x is in A Examples: /2 {7, “Albert”,/2, T} /3 {7, “Albert”, /2, T} 14/2 {7, “Albert”,/2, T} 7 2/3 Containment AB A is a subset of B A is contained in B Every element of A is also an element of B. Examples: R, {3}{5,7,3} every set, A A A is a proper subset of B Set Equivalence Two sets are equal if and only if they have the same elements. That is, if A and B are sets, then A and B are equal if and only if x(x A x B). We write A = B if A and B are equal sets. • Example: • Are sets {1, 3, 5} and {3, 5,1} equal? • Are sets {1, 3, 3, 3, 5, 5, 5, 5} and {1, 3, 5} equal? Basic Operations on Sets union: AB ::{|()()} xxA xB Basic Operations on Sets intersection: AB :: { xxAxB | } Basic Operations on Sets difference: A BxxAxB:: { | ( ) ( )} Basic Operations on Sets complement: A:: {xDxA | } D A Cardinality Let S be a set. If there are exactly n distinct elements in S where n is a nonnegative integer, we say that S is a finite set and that n is the cardinality of S. The cardinality of S is denoted by |S|. • Example: – Let A be the set of odd positive integers less than 10. Then |A| = 5. – Let S be the set of letters in the English alphabet. Then |A| = 26. – Null set has no elements, | ф | = 0. 15 Infinite Sets A set is said to be infinite if it is not finite. • Example: The set of positive integers is infinite. 16 Cardinality • Finding the cardinality of |A U B|: |A U B| = |A| + |B| - |A ∩ B | • Example: A = {1,3,5,7,9}, B = {5,7,9,11} |A U B| = |A| + |B| - |A ∩ B | = 5 + 4 – 3 = 6 17 Partitions of Sets Two sets are disjoint if their intersection is empty. A collection of nonempty sets {A1, A2, …, An} is a partition of a set A if and only if A1, A2, …, An are mutually disjoint. Power Sets power set: pwo ()::{|ASSA } pow(ab , ) ab , , a , b , Cartesian Products • Sets are unordered, a different structure is needed to represent an ordered collections – ordered n-tuples. The ordered n-tuple (a1, a2,…, an) is the ordered collection that has a1 as its first element, a2 as its second element, …, and an as its nth element. • Two ordered n-tuples are equal if and only if each corresponding pair of their elements is equal. – (a1, a2,…, an) = (b1, b2,…, bn) if and only if ai = bi for i = 1, 2, …, n 20 Cartesian Products Let A and B be sets. The Cartesian product of A and B, denoted by A × B, is the set of all ordered pairs (a, b), where aA and bB . Hence, A × B = {(a,b)| aA Λ bB }. • Example: What is the Cartesian product of A = {1,2} and B = {a,b,c}? Solution: A × B = {(1,a), (1,b), (1,c), (2,a), (2,b), (2,c)} • Cartesian product of A × B and B × A are not equal, unless A = ф or B = ф (so that A × B = ф ) or A = B. B × A = {(a,1),(a,2),(b,1),(b,2),(c,1),(c,2)} 21 Cartesian products The Cartesian product of sets A1, A2, …, An, denoted by A1 ×A2 ×… × An is the set of ordered n-tuples (a1, a2, …, an), where ai belongs to Ai for i = 1,2, …, n. In other words, A1 ×A2 ×…×An = {(a1, a2, …, an) | ai Ai for i = 1,2, …, n}. • Example: What is the Cartesian product of A × B × C where A= {0,1}, B = {1,2}, and C = {0,1,2}? Solution: A × B × C= {(0,1,0), (0,1,1), (0,1,2), (0,2,0), (0,2,1), (0,2,2), (1,1,0), (1,1,1), (1,1,2), (1,2,0), (1,2,1), (1,2,2)} 22 Set Identities Distributive Law: Set Identities De Morgan’s Law: Proving Set Identities Proving Set Identities Computer Representation of Sets • Represent a subset A of U with the bit string of length n, where the ith bit in the string is 1 if ai belongs to A and is 0 if ai does not belong to A. • Example: – Let U = {1,2,3,4,5,6,7,8,9,10}, and the ordering of elements of U has the elements in increasing order; that is ai = i. What bit string represents the subset of all odd integers in U? Solution: 10 1010 1010 What bit string represents the subset of all even integers in U? Solution: 01 010 10101 What bit string represents the subset of all integers not exceeding 5 in U? Solution: 11 1110 0000 What bit string represents the complement of the set {1,3,5,7,9}? Solution: 01 0101 0101 27 Logic and Bit Operations • Computers represent information using bits. • A bit is a symbol with two possible values, 0 and 1. • By convention, 1 represents T (true) and 0 represents F (false). • A variable is called a Boolean variable if its value is either true or false. • Bit operation – replace true by 1 and false by 0 in logical operations. Table for the Bit Operators OR, AND, and XOR. x y x ν y x Λ y xy 0 0 0 0 0 0 1 1 0 1 1 0 1 0 1 1 1 1 1 0 28 Logic and Bit Operations DEFINITION 7 A bit string is a sequence of zero or more bits. The length of this string is the number of bits in the string. • Example: Find the bitwise OR, bitwise AND, and bitwise XOR of the bit string 01 1011 0110 and 11 0001 1101. Solution: 01 1011 0110 11 0001 1101 ------------------- 11 1011 1111 bitwise OR 01 0001 0100 bitwise AND 10 1010 1011 bitwise XOR 29 Set Operations • The bit string for the union is the bitwise OR of the bit string for the two sets. The bit string for the intersection is the bitwise AND of the bit strings for the two sets. • Example: – The bit strings for the sets {1,2,3,4,5} and {1,3,5,7,9} are 11 1110 0000 and 10 1010 1010, respectively. Use bit strings to find the union and intersection of these sets. Solution: Union: 11 1110 0000 V 10 1010 1010 = 11 1110 1010, {1,2,3,4,5,7,9} Intersection: 11 1110 0000 Λ 10 1010 1010 = 10 1010 0000, {1,3,5} 30 Russell’s Paradox Let WS:: Sets | SS so SW SS There is a male barber who shaves all those men, and only those men, who do not shave themselves. Does the barber shave himself?.
Recommended publications
  • Math Preliminaries
    Preliminaries We establish here a few notational conventions used throughout the text. Arithmetic with ∞ We shall sometimes use the symbols “∞” and “−∞” in simple arithmetic expressions involving real numbers. The interpretation given to such ex- pressions is the usual, natural one; for example, for all real numbers x, we have −∞ < x < ∞, x + ∞ = ∞, x − ∞ = −∞, ∞ + ∞ = ∞, and (−∞) + (−∞) = −∞. Some such expressions have no sensible interpreta- tion (e.g., ∞ − ∞). Logarithms and exponentials We denote by log x the natural logarithm of x. The logarithm of x to the base b is denoted logb x. We denote by ex the usual exponential function, where e ≈ 2.71828 is the base of the natural logarithm. We may also write exp[x] instead of ex. Sets and relations We use the symbol ∅ to denote the empty set. For two sets A, B, we use the notation A ⊆ B to mean that A is a subset of B (with A possibly equal to B), and the notation A ( B to mean that A is a proper subset of B (i.e., A ⊆ B but A 6= B); further, A ∪ B denotes the union of A and B, A ∩ B the intersection of A and B, and A \ B the set of all elements of A that are not in B. For sets S1,...,Sn, we denote by S1 × · · · × Sn the Cartesian product xiv Preliminaries xv of S1,...,Sn, that is, the set of all n-tuples (a1, . , an), where ai ∈ Si for i = 1, . , n. We use the notation S×n to denote the Cartesian product of n copies of a set S, and for x ∈ S, we denote by x×n the element of S×n consisting of n copies of x.
    [Show full text]
  • The Structure of the 3-Separations of 3-Connected Matroids
    THE STRUCTURE OF THE 3{SEPARATIONS OF 3{CONNECTED MATROIDS JAMES OXLEY, CHARLES SEMPLE, AND GEOFF WHITTLE Abstract. Tutte defined a k{separation of a matroid M to be a partition (A; B) of the ground set of M such that |A|; |B|≥k and r(A)+r(B) − r(M) <k. If, for all m<n, the matroid M has no m{separations, then M is n{connected. Earlier, Whitney showed that (A; B) is a 1{separation of M if and only if A is a union of 2{connected components of M.WhenM is 2{connected, Cunningham and Edmonds gave a tree decomposition of M that displays all of its 2{separations. When M is 3{connected, this paper describes a tree decomposition of M that displays, up to a certain natural equivalence, all non-trivial 3{ separations of M. 1. Introduction One of Tutte’s many important contributions to matroid theory was the introduction of the general theory of separations and connectivity [10] de- fined in the abstract. The structure of the 1–separations in a matroid is elementary. They induce a partition of the ground set which in turn induces a decomposition of the matroid into 2–connected components [11]. Cun- ningham and Edmonds [1] considered the structure of 2–separations in a matroid. They showed that a 2–connected matroid M can be decomposed into a set of 3–connected matroids with the property that M can be built from these 3–connected matroids via a canonical operation known as 2–sum. Moreover, there is a labelled tree that gives a precise description of the way that M is built from the 3–connected pieces.
    [Show full text]
  • The Universal Finite Set 3
    THE UNIVERSAL FINITE SET JOEL DAVID HAMKINS AND W. HUGH WOODIN Abstract. We define a certain finite set in set theory { x | ϕ(x) } and prove that it exhibits a universal extension property: it can be any desired particular finite set in the right set-theoretic universe and it can become successively any desired larger finite set in top-extensions of that universe. Specifically, ZFC proves the set is finite; the definition ϕ has complexity Σ2, so that any affirmative instance of it ϕ(x) is verified in any sufficiently large rank-initial segment of the universe Vθ ; the set is empty in any transitive model and others; and if ϕ defines the set y in some countable model M of ZFC and y ⊆ z for some finite set z in M, then there is a top-extension of M to a model N in which ϕ defines the new set z. Thus, the set shows that no model of set theory can realize a maximal Σ2 theory with its natural number parameters, although this is possible without parameters. Using the universal finite set, we prove that the validities of top-extensional set-theoretic potentialism, the modal principles valid in the Kripke model of all countable models of set theory, each accessing its top-extensions, are precisely the assertions of S4. Furthermore, if ZFC is consistent, then there are models of ZFC realizing the top-extensional maximality principle. 1. Introduction The second author [Woo11] established the universal algorithm phenomenon, showing that there is a Turing machine program with a certain universal top- extension property in models of arithmetic.
    [Show full text]
  • Matroids with Nine Elements
    Matroids with nine elements Dillon Mayhew School of Mathematics, Statistics & Computer Science Victoria University Wellington, New Zealand [email protected] Gordon F. Royle School of Computer Science & Software Engineering University of Western Australia Perth, Australia [email protected] February 2, 2008 Abstract We describe the computation of a catalogue containing all matroids with up to nine elements, and present some fundamental data arising from this cataogue. Our computation confirms and extends the results obtained in the 1960s by Blackburn, Crapo & Higgs. The matroids and associated data are stored in an online database, arXiv:math/0702316v1 [math.CO] 12 Feb 2007 and we give three short examples of the use of this database. 1 Introduction In the late 1960s, Blackburn, Crapo & Higgs published a technical report describing the results of a computer search for all simple matroids on up to eight elements (although the resulting paper [2] did not appear until 1973). In both the report and the paper they said 1 “It is unlikely that a complete tabulation of 9-point geometries will be either feasible or desirable, as there will be many thousands of them. The recursion g(9) = g(8)3/2 predicts 29260.” Perhaps this comment dissuaded later researchers in matroid theory, because their cata- logue remained unextended for more than 30 years, which surely makes it one of the longest standing computational results in combinatorics. However, in this paper we demonstrate that they were in fact unduly pessimistic, and describe an orderly algorithm (see McKay [7] and Royle [11]) that confirms their computations and extends them by determining the 383172 pairwise non-isomorphic matroids on nine elements (see Table 1).
    [Show full text]
  • Section 3: Equivalence Relations
    10.3.1 Section 3: Equivalence Relations • Definition: Let R be a binary relation on A. R is an equivalence relation on A if R is reflexive, symmetric, and transitive. • From the last section, we demonstrated that Equality on the Real Numbers and Congruence Modulo p on the Integers were reflexive, symmetric, and transitive, so we can describe them as equivalence relations. 10.3.2 Examples • What is the “smallest” equivalence relation on a set A? R = {(a,a) | a Î A}, so that n(R) = n(A). • What is the “largest” equivalence relation on a set A? R = A ´ A, so that n(R) = [n(A)]2. Equivalence Classes 10.3.3 • Definition: If R is an equivalence relation on a set A, and a Î A, then the equivalence class of a is defined to be: [a] = {b Î A | (a,b) Î R}. • In other words, [a] is the set of all elements which relate to a by R. • For example: If R is congruence mod 5, then [3] = {..., -12, -7, -2, 3, 8, 13, 18, ...}. • Another example: If R is equality on Q, then [2/3] = {2/3, 4/6, 6/9, 8/12, 10/15, ...}. • Observation: If b Î [a], then [b] = [a]. 10.3.4 A String Example • Let S = {0,1} and denote L(s) = length of s, for any string s Î S*. Consider the relation: R = {(s,t) | s,t Î S* and L(s) = L(t)} • R is an equivalence relation. Why? • REF: For all s Î S*, L(s) = L(s); SYM: If L(s) = L(t), then L(t) = L(s); TRAN: If L(s) = L(t) and L(t) = L(u), L(s) = L(u).
    [Show full text]
  • Parity Systems and the Delta-Matroid Intersection Problem
    Parity Systems and the Delta-Matroid Intersection Problem Andr´eBouchet ∗ and Bill Jackson † Submitted: February 16, 1998; Accepted: September 3, 1999. Abstract We consider the problem of determining when two delta-matroids on the same ground-set have a common base. Our approach is to adapt the theory of matchings in 2-polymatroids developed by Lov´asz to a new abstract system, which we call a parity system. Examples of parity systems may be obtained by combining either, two delta- matroids, or two orthogonal 2-polymatroids, on the same ground-sets. We show that many of the results of Lov´aszconcerning ‘double flowers’ and ‘projections’ carry over to parity systems. 1 Introduction: the delta-matroid intersec- tion problem A delta-matroid is a pair (V, ) with a finite set V and a nonempty collection of subsets of V , called theBfeasible sets or bases, satisfying the following axiom:B ∗D´epartement d’informatique, Universit´edu Maine, 72017 Le Mans Cedex, France. [email protected] †Department of Mathematical and Computing Sciences, Goldsmiths’ College, London SE14 6NW, England. [email protected] 1 the electronic journal of combinatorics 7 (2000), #R14 2 1.1 For B1 and B2 in and v1 in B1∆B2, there is v2 in B1∆B2 such that B B1∆ v1, v2 belongs to . { } B Here P ∆Q = (P Q) (Q P ) is the symmetric difference of two subsets P and Q of V . If X\ is a∪ subset\ of V and if we set ∆X = B∆X : B , then we note that (V, ∆X) is a new delta-matroid.B The{ transformation∈ B} (V, ) (V, ∆X) is calledB a twisting.
    [Show full text]
  • Chapter I Set Theory
    Chapter I Set Theory There is surely a piece of divinity in us, something that was before the elements, and owes no homage unto the sun. Sir Thomas Browne One of the benefits of mathematics comes from its ability to express a lot of information in very few symbols. Take a moment to consider the expression d sin( θ). dθ It encapsulates a large amount of information. The notation sin( θ) represents, for a right triangle with angle θ, the ratio of the opposite side to the hypotenuse. The differential operator d/dθ represents a limit, corresponding to a tangent line, and so forth. Similarly, sets are a convenient way to express a large amount of information. They give us a language we will find convenient in which to do mathematics. This is no accident, as much of modern mathematics can be expressed in terms of sets. 1 2 CHAPTER I. SET THEORY 1 Sets, subsets, and set operations 1.A What is a set? A set is simply a collection of objects. The objects in the set are called the elements . We often write down a set by listing its elements. For instance, the set S = 1, 2, 3 has three elements. Those elements are 1, 2, and 3. There is a special symbol,{ }, that we use to express the idea that an element belongs to a set. For instance, we∈ write 1 S to mean that “1 is an element of S.” For∈ the set S = 1, 2, 3 , we have 1 S, 2 S, and 3 S.
    [Show full text]
  • Computing Partitions Within SQL Queries: a Dead End? Frédéric Dumonceaux, Guillaume Raschia, Marc Gelgon
    Computing Partitions within SQL Queries: A Dead End? Frédéric Dumonceaux, Guillaume Raschia, Marc Gelgon To cite this version: Frédéric Dumonceaux, Guillaume Raschia, Marc Gelgon. Computing Partitions within SQL Queries: A Dead End?. 2013. hal-00768156 HAL Id: hal-00768156 https://hal.archives-ouvertes.fr/hal-00768156 Submitted on 20 Dec 2012 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. Computing Partitions within SQL Queries: A Dead End? Fr´ed´eric Dumonceaux – Guillaume Raschia – Marc Gelgon December 20, 2012 Abstract The primary goal of relational databases is to provide efficient query processing on sets of tuples and thereafter, query evaluation and optimization strategies are a key issue in database implementation. Producing universally fast execu- tion plans remains a challenging task since the underlying relational model has a significant impact on algebraic definition of the operators, thereby on their implementation in terms of space and time complexity. At least, it should pre- vent a quadratic behavior in order to consider scaling-up towards the processing of large datasets. The main purpose of this paper is to show that there is no trivial relational modeling for managing collections of partitions (i.e.
    [Show full text]
  • Partial Ordering Relations • a Relation Is Said to Be a Partial Ordering Relation If It Is Reflexive, Anti -Symmetric, and Transitive
    Relations Chang-Gun Lee ([email protected]) Assistant Professor The School of Computer Science and Engineering Seoul National University Relations • The concept of relations is also commonly used in computer science – two of the programs are related if they share some common data and are not related otherwise. – two wireless nodes are related if they interfere each other and are not related otherwise – In a database, two objects are related if their secondary key values are the same • What is the mathematical definition of a relation? • Definition 13.1 (Relation): A relation is a set of ordered pairs – The set of ordered pairs is a complete listing of all pairs of objects that “satisfy” the relation •Examples: – Greater Than Re lat ion = {(2 {(21)(31)(32),1), (3,1), (3,2), ...... } – R = {(1,2), (1,3), (3,0)} (1,2)∈ R, 1 R 2 :"x is related by the relation R to y" Relations • Definition 13.2 (Relation on, between sets) Let R be a relation and let A and B be sets . –We say R is a relation on A provided R ⊆ A× A –We say R is a relation from A to B provided R ⊆ A× B Example Relations •Let A={1,2,3,4} and B={4,5,6,7}. Let – R={(11)(22)(33)(44)}{(1,1),(2,2),(3,3),(4,4)} – S={(1,2),(3,2)} – T={(1,4),(1,5),(4,7)} – U={(4,4),(5,2),(6,2),(7,3)}, and – V={(1,7),(7,1)} • All of these are relations – R is a relation on A.
    [Show full text]
  • Notes for Math 450 Lecture Notes 2
    Notes for Math 450 Lecture Notes 2 Renato Feres 1 Probability Spaces We first explain the basic concept of a probability space, (Ω, F,P ). This may be interpreted as an experiment with random outcomes. The set Ω is the collection of all possible outcomes of the experiment; F is a family of subsets of Ω called events; and P is a function that associates to an event its probability. These objects must satisfy certain logical requirements, which are detailed below. A random variable is a function X :Ω → S of the output of the random system. We explore some of the general implications of these abstract concepts. 1.1 Events and the basic set operations on them Any situation where the outcome is regarded as random will be referred to as an experiment, and the set of all possible outcomes of the experiment comprises its sample space, denoted by S or, at times, Ω. Each possible outcome of the experiment corresponds to a single element of S. For example, rolling a die is an experiment whose sample space is the finite set {1, 2, 3, 4, 5, 6}. The sample space for the experiment of tossing three (distinguishable) coins is {HHH,HHT,HTH,HTT,THH,THT,TTH,TTT } where HTH indicates the ordered triple (H, T, H) in the product set {H, T }3. The delay in departure of a flight scheduled for 10:00 AM every day can be regarded as the outcome of an experiment in this abstract sense, where the sample space now may be taken to be the interval [0, ∞) of the real line.
    [Show full text]
  • Sets and Functions
    Unit SF Sets and Functions Section 1: Sets The basic concepts of sets and functions are topics covered in high school math courses and are thus familiar to most university students. We take the intuitive point of view that sets are unordered collections of objects. We first recall some standard terminology and notation associated with sets. When we speak about sets, we usually have a “universal set” U in mind, to which the various sets of our discourse belong. Definition 1 (Set notation) A set is an unordered collection of distinct objects. We use the notation x ∈ S to mean “x is an element of S” and x∈ / S to mean “x is not an element of S.” Given two subsets (subcollections) of U, X and Y , we say “X is a subset of Y ,” written X ⊆ Y , if x ∈ X implies that x ∈ Y . Alternatively, we may say that “Y is a superset of X.” X ⊆ Y and Y ⊇ X mean the same thing. We say that two subsets X and Y of U are equal if X ⊆ Y and Y ⊆ X. We use braces to designate sets when we wish to specify or describe them in terms of their elements: A = {a, b, c}, B = {2, 4, 6,...}. A set with k elements is called a k-set or set with cardinality k. The cardinality of a set A is denoted by |A|. Since a set is an unordered collection of distinct objects, the following all describe the same 3-element set {a, b, c} = {b, a, c} = {c, b, a} = {a, b, b, c, b}.
    [Show full text]
  • Arxiv:1711.00219V2 [Math.OA] 9 Aug 2019 R,Fe Rbblt,Glbr Coefficients
    CUMULANTS, SPREADABILITY AND THE CAMPBELL-BAKER-HAUSDORFF SERIES TAKAHIRO HASEBE AND FRANZ LEHNER Abstract. We define spreadability systems as a generalization of exchangeability systems in order to unify various notions of independence and cumulants known in noncommutative probability. In particular, our theory covers monotone independence and monotone cumulants which do not satisfy exchangeability. To this end we study generalized zeta and M¨obius functions in the context of the incidence algebra of the semilattice of ordered set partitions and prove an appropriate variant of Faa di Bruno’s theorem. With the aid of this machinery we show that our cumulants cover most of the previously known cumulants. Due to noncommutativity of independence the behaviour of these cumulants with respect to independent random variables is more complicated than in the exchangeable case and the appearance of Goldberg coefficients exhibits the role of the Campbell-Baker-Hausdorff series in this context. In a final section we exhibit an interpretation of the Campbell-Baker-Hausdorff series as a sum of cumulants in a particular spreadability system, thus providing a new derivation of the Goldberg coefficients. Contents 1. Introduction 2 2. Ordered set partitions 4 2.1. Set partitions 4 2.2. Ordered set partitions 5 2.3. Incidence algebras and multiplicative functions 8 2.4. Special functions in the poset of ordered set partitions 10 3. A generalized notion of independence related to spreadability systems 11 3.1. Notation and terminology 11 3.2. Spreadability systems 13 3.3. Examples from natural products of linear maps 14 3.4. E-independence 15 3.5.
    [Show full text]