Equivalence Relations and Partitions
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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. -
Math 296. Homework 3 (Due Jan 28) 1
Math 296. Homework 3 (due Jan 28) 1. Equivalence Classes. Let R be an equivalence relation on a set X. For each x ∈ X, consider the subset xR ⊂ X consisting of all the elements y in X such that xRy. A set of the form xR is called an equivalence class. (1) Show that xR = yR (as subsets of X) if and only if xRy. (2) Show that xR ∩ yR = ∅ or xR = yR. (3) Show that there is a subset Y (called equivalence classes representatives) of X such that X is the disjoint union of subsets of the form yR for y ∈ Y . Is the set Y uniquely determined? (4) For each of the equivalence relations from Problem Set 2, Exercise 5, Parts 3, 5, 6, 7, 8: describe the equivalence classes, find a way to enumerate them by picking a nice representative for each, and find the cardinality of the set of equivalence classes. [I will ask Ruthi to discuss this a bit in the discussion session.] 2. Pliability of Smooth Functions. This problem undertakes a very fundamental construction: to prove that ∞ −1/x2 C -functions are very soft and pliable. Let F : R → R be defined by F (x) = e for x 6= 0 and F (0) = 0. (1) Verify that F is infinitely differentiable at every point (don’t forget that you computed on a 295 problem set that the k-th derivative exists and is zero, for all k ≥ 1). −1/x2 ∞ (2) Let ϕ : R → R be defined by ϕ(x) = 0 for x ≤ 0 and ϕ(x) = e for x > 0. -
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 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. -
Surviving Set Theory: a Pedagogical Game and Cooperative Learning Approach to Undergraduate Post-Tonal Music Theory
Surviving Set Theory: A Pedagogical Game and Cooperative Learning Approach to Undergraduate Post-Tonal Music Theory DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Angela N. Ripley, M.M. Graduate Program in Music The Ohio State University 2015 Dissertation Committee: David Clampitt, Advisor Anna Gawboy Johanna Devaney Copyright by Angela N. Ripley 2015 Abstract Undergraduate music students often experience a high learning curve when they first encounter pitch-class set theory, an analytical system very different from those they have studied previously. Students sometimes find the abstractions of integer notation and the mathematical orientation of set theory foreign or even frightening (Kleppinger 2010), and the dissonance of the atonal repertoire studied often engenders their resistance (Root 2010). Pedagogical games can help mitigate student resistance and trepidation. Table games like Bingo (Gillespie 2000) and Poker (Gingerich 1991) have been adapted to suit college-level classes in music theory. Familiar television shows provide another source of pedagogical games; for example, Berry (2008; 2015) adapts the show Survivor to frame a unit on theory fundamentals. However, none of these pedagogical games engage pitch- class set theory during a multi-week unit of study. In my dissertation, I adapt the show Survivor to frame a four-week unit on pitch- class set theory (introducing topics ranging from pitch-class sets to twelve-tone rows) during a sophomore-level theory course. As on the show, students of different achievement levels work together in small groups, or “tribes,” to complete worksheets called “challenges”; however, in an important modification to the structure of the show, no students are voted out of their tribes. -
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). -
Pitch-Class Set Theory: an Overture
Chapter One Pitch-Class Set Theory: An Overture A Tale of Two Continents In the late afternoon of October 24, 1999, about one hundred people were gathered in a large rehearsal room of the Rotterdam Conservatory. They were listening to a discussion between representatives of nine European countries about the teaching of music theory and music analysis. It was the third day of the Fourth European Music Analysis Conference.1 Most participants in the conference (which included a number of music theorists from Canada and the United States) had been looking forward to this session: meetings about the various analytical traditions and pedagogical practices in Europe were rare, and a broad survey of teaching methods was lacking. Most felt a need for information from beyond their country’s borders. This need was reinforced by the mobility of music students and the resulting hodgepodge of nationalities at renowned conservatories and music schools. Moreover, the European systems of higher education were on the threshold of a harmoni- zation operation. Earlier that year, on June 19, the governments of 29 coun- tries had ratifi ed the “Bologna Declaration,” a document that envisaged a unifi ed European area for higher education. Its enforcement added to the urgency of the meeting in Rotterdam. However, this meeting would not be remembered for the unusually broad rep- resentation of nationalities or for its political timeliness. What would be remem- bered was an incident which took place shortly after the audience had been invited to join in the discussion. Somebody had raised a question about classroom analysis of twentieth-century music, a recurring topic among music theory teach- ers: whereas the music of the eighteenth and nineteenth centuries lent itself to general analytical methodologies, the extremely diverse repertoire of the twen- tieth century seemed only to invite ad hoc approaches; how could the analysis of 1. -
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). -
General Topology
General Topology Tom Leinster 2014{15 Contents A Topological spaces2 A1 Review of metric spaces.......................2 A2 The definition of topological space.................8 A3 Metrics versus topologies....................... 13 A4 Continuous maps........................... 17 A5 When are two spaces homeomorphic?................ 22 A6 Topological properties........................ 26 A7 Bases................................. 28 A8 Closure and interior......................... 31 A9 Subspaces (new spaces from old, 1)................. 35 A10 Products (new spaces from old, 2)................. 39 A11 Quotients (new spaces from old, 3)................. 43 A12 Review of ChapterA......................... 48 B Compactness 51 B1 The definition of compactness.................... 51 B2 Closed bounded intervals are compact............... 55 B3 Compactness and subspaces..................... 56 B4 Compactness and products..................... 58 B5 The compact subsets of Rn ..................... 59 B6 Compactness and quotients (and images)............. 61 B7 Compact metric spaces........................ 64 C Connectedness 68 C1 The definition of connectedness................... 68 C2 Connected subsets of the real line.................. 72 C3 Path-connectedness.......................... 76 C4 Connected-components and path-components........... 80 1 Chapter A Topological spaces A1 Review of metric spaces For the lecture of Thursday, 18 September 2014 Almost everything in this section should have been covered in Honours Analysis, with the possible exception of some of the examples. For that reason, this lecture is longer than usual. Definition A1.1 Let X be a set. A metric on X is a function d: X × X ! [0; 1) with the following three properties: • d(x; y) = 0 () x = y, for x; y 2 X; • d(x; y) + d(y; z) ≥ d(x; z) for all x; y; z 2 X (triangle inequality); • d(x; y) = d(y; x) for all x; y 2 X (symmetry). -
The Axiom of Choice and Its Implications
THE AXIOM OF CHOICE AND ITS IMPLICATIONS KEVIN BARNUM Abstract. In this paper we will look at the Axiom of Choice and some of the various implications it has. These implications include a number of equivalent statements, and also some less accepted ideas. The proofs discussed will give us an idea of why the Axiom of Choice is so powerful, but also so controversial. Contents 1. Introduction 1 2. The Axiom of Choice and Its Equivalents 1 2.1. The Axiom of Choice and its Well-known Equivalents 1 2.2. Some Other Less Well-known Equivalents of the Axiom of Choice 3 3. Applications of the Axiom of Choice 5 3.1. Equivalence Between The Axiom of Choice and the Claim that Every Vector Space has a Basis 5 3.2. Some More Applications of the Axiom of Choice 6 4. Controversial Results 10 Acknowledgments 11 References 11 1. Introduction The Axiom of Choice states that for any family of nonempty disjoint sets, there exists a set that consists of exactly one element from each element of the family. It seems strange at first that such an innocuous sounding idea can be so powerful and controversial, but it certainly is both. To understand why, we will start by looking at some statements that are equivalent to the axiom of choice. Many of these equivalences are very useful, and we devote much time to one, namely, that every vector space has a basis. We go on from there to see a few more applications of the Axiom of Choice and its equivalents, and finish by looking at some of the reasons why the Axiom of Choice is so controversial. -
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. -
6. Localization
52 Andreas Gathmann 6. Localization Localization is a very powerful technique in commutative algebra that often allows to reduce ques- tions on rings and modules to a union of smaller “local” problems. It can easily be motivated both from an algebraic and a geometric point of view, so let us start by explaining the idea behind it in these two settings. Remark 6.1 (Motivation for localization). (a) Algebraic motivation: Let R be a ring which is not a field, i. e. in which not all non-zero elements are units. The algebraic idea of localization is then to make more (or even all) non-zero elements invertible by introducing fractions, in the same way as one passes from the integers Z to the rational numbers Q. Let us have a more precise look at this particular example: in order to construct the rational numbers from the integers we start with R = Z, and let S = Znf0g be the subset of the elements of R that we would like to become invertible. On the set R×S we then consider the equivalence relation (a;s) ∼ (a0;s0) , as0 − a0s = 0 a and denote the equivalence class of a pair (a;s) by s . The set of these “fractions” is then obviously Q, and we can define addition and multiplication on it in the expected way by a a0 as0+a0s a a0 aa0 s + s0 := ss0 and s · s0 := ss0 . (b) Geometric motivation: Now let R = A(X) be the ring of polynomial functions on a variety X. In the same way as in (a) we can ask if it makes sense to consider fractions of such polynomials, i.