A Workshop for High School Students on Naive Set Theory
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Mathematics 144 Set Theory Fall 2012 Version
MATHEMATICS 144 SET THEORY FALL 2012 VERSION Table of Contents I. General considerations.……………………………………………………………………………………………………….1 1. Overview of the course…………………………………………………………………………………………………1 2. Historical background and motivation………………………………………………………….………………4 3. Selected problems………………………………………………………………………………………………………13 I I. Basic concepts. ………………………………………………………………………………………………………………….15 1. Topics from logic…………………………………………………………………………………………………………16 2. Notation and first steps………………………………………………………………………………………………26 3. Simple examples…………………………………………………………………………………………………………30 I I I. Constructions in set theory.………………………………………………………………………………..……….34 1. Boolean algebra operations.……………………………………………………………………………………….34 2. Ordered pairs and Cartesian products……………………………………………………………………… ….40 3. Larger constructions………………………………………………………………………………………………..….42 4. A convenient assumption………………………………………………………………………………………… ….45 I V. Relations and functions ……………………………………………………………………………………………….49 1.Binary relations………………………………………………………………………………………………………… ….49 2. Partial and linear orderings……………………………..………………………………………………… ………… 56 3. Functions…………………………………………………………………………………………………………… ….…….. 61 4. Composite and inverse function.…………………………………………………………………………… …….. 70 5. Constructions involving functions ………………………………………………………………………… ……… 77 6. Order types……………………………………………………………………………………………………… …………… 80 i V. Number systems and set theory …………………………………………………………………………………. 84 1. The Natural Numbers and Integers…………………………………………………………………………….83 2. Finite induction -
Redalyc.Sets and Pluralities
Red de Revistas Científicas de América Latina, el Caribe, España y Portugal Sistema de Información Científica Gustavo Fernández Díez Sets and Pluralities Revista Colombiana de Filosofía de la Ciencia, vol. IX, núm. 19, 2009, pp. 5-22, Universidad El Bosque Colombia Available in: http://www.redalyc.org/articulo.oa?id=41418349001 Revista Colombiana de Filosofía de la Ciencia, ISSN (Printed Version): 0124-4620 [email protected] Universidad El Bosque Colombia How to cite Complete issue More information about this article Journal's homepage www.redalyc.org Non-Profit Academic Project, developed under the Open Acces Initiative Sets and Pluralities1 Gustavo Fernández Díez2 Resumen En este artículo estudio el trasfondo filosófico del sistema de lógica conocido como “lógica plural”, o “lógica de cuantificadores plurales”, de aparición relativamente reciente (y en alza notable en los últimos años). En particular, comparo la noción de “conjunto” emanada de la teoría axiomática de conjuntos, con la noción de “plura- lidad” que se encuentra detrás de este nuevo sistema. Mi conclusión es que los dos son completamente diferentes en su alcance y sus límites, y que la diferencia proviene de las diferentes motivaciones que han dado lugar a cada uno. Mientras que la teoría de conjuntos es una teoría genuinamente matemática, que tiene el interés matemático como ingrediente principal, la lógica plural ha aparecido como respuesta a considera- ciones lingüísticas, relacionadas con la estructura lógica de los enunciados plurales del inglés y el resto de los lenguajes naturales. Palabras clave: conjunto, teoría de conjuntos, pluralidad, cuantificación plural, lógica plural. Abstract In this paper I study the philosophical background of the relatively recent (and in the last few years increasingly flourishing) system of logic called “plural logic”, or “logic of plural quantifiers”. -
TOPOLOGY and ITS APPLICATIONS the Number of Complements in The
TOPOLOGY AND ITS APPLICATIONS ELSEVIER Topology and its Applications 55 (1994) 101-125 The number of complements in the lattice of topologies on a fixed set Stephen Watson Department of Mathematics, York Uniuersity, 4700 Keele Street, North York, Ont., Canada M3J IP3 (Received 3 May 1989) (Revised 14 November 1989 and 2 June 1992) Abstract In 1936, Birkhoff ordered the family of all topologies on a set by inclusion and obtained a lattice with 1 and 0. The study of this lattice ought to be a basic pursuit both in combinatorial set theory and in general topology. In this paper, we study the nature of complementation in this lattice. We say that topologies 7 and (T are complementary if and only if 7 A c = 0 and 7 V (T = 1. For simplicity, we call any topology other than the discrete and the indiscrete a proper topology. Hartmanis showed in 1958 that any proper topology on a finite set of size at least 3 has at least two complements. Gaifman showed in 1961 that any proper topology on a countable set has at least two complements. In 1965, Steiner showed that any topology has a complement. The question of the number of distinct complements a topology on a set must possess was first raised by Berri in 1964 who asked if every proper topology on an infinite set must have at least two complements. In 1969, Schnare showed that any proper topology on a set of infinite cardinality K has at least K distinct complements and at most 2” many distinct complements. -
Determinacy in Linear Rational Expectations Models
Journal of Mathematical Economics 40 (2004) 815–830 Determinacy in linear rational expectations models Stéphane Gauthier∗ CREST, Laboratoire de Macroéconomie (Timbre J-360), 15 bd Gabriel Péri, 92245 Malakoff Cedex, France Received 15 February 2002; received in revised form 5 June 2003; accepted 17 July 2003 Available online 21 January 2004 Abstract The purpose of this paper is to assess the relevance of rational expectations solutions to the class of linear univariate models where both the number of leads in expectations and the number of lags in predetermined variables are arbitrary. It recommends to rule out all the solutions that would fail to be locally unique, or equivalently, locally determinate. So far, this determinacy criterion has been applied to particular solutions, in general some steady state or periodic cycle. However solutions to linear models with rational expectations typically do not conform to such simple dynamic patterns but express instead the current state of the economic system as a linear difference equation of lagged states. The innovation of this paper is to apply the determinacy criterion to the sets of coefficients of these linear difference equations. Its main result shows that only one set of such coefficients, or the corresponding solution, is locally determinate. This solution is commonly referred to as the fundamental one in the literature. In particular, in the saddle point configuration, it coincides with the saddle stable (pure forward) equilibrium trajectory. © 2004 Published by Elsevier B.V. JEL classification: C32; E32 Keywords: Rational expectations; Selection; Determinacy; Saddle point property 1. Introduction The rational expectations hypothesis is commonly justified by the fact that individual forecasts are based on the relevant theory of the economic system. -
180: Counting Techniques
180: Counting Techniques In the following exercise we demonstrate the use of a few fundamental counting principles, namely the addition, multiplication, complementary, and inclusion-exclusion principles. While none of the principles are particular complicated in their own right, it does take some practice to become familiar with them, and recognise when they are applicable. I have attempted to indicate where alternate approaches are possible (and reasonable). Problem: Assume n ≥ 2 and m ≥ 1. Count the number of functions f :[n] ! [m] (i) in total. (ii) such that f(1) = 1 or f(2) = 1. (iii) with minx2[n] f(x) ≤ 5. (iv) such that f(1) ≥ f(2). (v) that are strictly increasing; that is, whenever x < y, f(x) < f(y). (vi) that are one-to-one (injective). (vii) that are onto (surjective). (viii) that are bijections. (ix) such that f(x) + f(y) is even for every x; y 2 [n]. (x) with maxx2[n] f(x) = minx2[n] f(x) + 1. Solution: (i) A function f :[n] ! [m] assigns for every integer 1 ≤ x ≤ n an integer 1 ≤ f(x) ≤ m. For each integer x, we have m options. As we make n such choices (independently), the total number of functions is m × m × : : : m = mn. (ii) (We assume n ≥ 2.) Let A1 be the set of functions with f(1) = 1, and A2 the set of functions with f(2) = 1. Then A1 [ A2 represents those functions with f(1) = 1 or f(2) = 1, which is precisely what we need to count. We have jA1 [ A2j = jA1j + jA2j − jA1 \ A2j. -
Chapter 1 Logic and Set Theory
Chapter 1 Logic and Set Theory To criticize mathematics for its abstraction is to miss the point entirely. Abstraction is what makes mathematics work. If you concentrate too closely on too limited an application of a mathematical idea, you rob the mathematician of his most important tools: analogy, generality, and simplicity. – Ian Stewart Does God play dice? The mathematics of chaos In mathematics, a proof is a demonstration that, assuming certain axioms, some statement is necessarily true. That is, a proof is a logical argument, not an empir- ical one. One must demonstrate that a proposition is true in all cases before it is considered a theorem of mathematics. An unproven proposition for which there is some sort of empirical evidence is known as a conjecture. Mathematical logic is the framework upon which rigorous proofs are built. It is the study of the principles and criteria of valid inference and demonstrations. Logicians have analyzed set theory in great details, formulating a collection of axioms that affords a broad enough and strong enough foundation to mathematical reasoning. The standard form of axiomatic set theory is denoted ZFC and it consists of the Zermelo-Fraenkel (ZF) axioms combined with the axiom of choice (C). Each of the axioms included in this theory expresses a property of sets that is widely accepted by mathematicians. It is unfortunately true that careless use of set theory can lead to contradictions. Avoiding such contradictions was one of the original motivations for the axiomatization of set theory. 1 2 CHAPTER 1. LOGIC AND SET THEORY A rigorous analysis of set theory belongs to the foundations of mathematics and mathematical logic. -
Fast and Private Computation of Cardinality of Set Intersection and Union
Fast and Private Computation of Cardinality of Set Intersection and Union Emiliano De Cristofaroy, Paolo Gastiz, and Gene Tsudikz yPARC zUC Irvine Abstract. With massive amounts of electronic information stored, trans- ferred, and shared every day, legitimate needs for sensitive information must be reconciled with natural privacy concerns. This motivates var- ious cryptographic techniques for privacy-preserving information shar- ing, such as Private Set Intersection (PSI) and Private Set Union (PSU). Such techniques involve two parties { client and server { each with a private input set. At the end, client learns the intersection (or union) of the two respective sets, while server learns nothing. However, if desired functionality is private computation of cardinality rather than contents, of set intersection, PSI and PSU protocols are not appropriate, as they yield too much information. In this paper, we design an efficient crypto- graphic protocol for Private Set Intersection Cardinality (PSI-CA) that yields only the size of set intersection, with security in the presence of both semi-honest and malicious adversaries. To the best of our knowl- edge, it is the first protocol that achieves complexities linear in the size of input sets. We then show how the same protocol can be used to pri- vately compute set union cardinality. We also design an extension that supports authorization of client input. 1 Introduction Proliferation of, and growing reliance on, electronic information trigger the increase in the amount of sensitive data stored and processed in cyberspace. Consequently, there is a strong need for efficient cryptographic techniques that allow sharing information with privacy. Among these, Private Set Intersection (PSI) [11, 24, 14, 21, 15, 22, 9, 8, 18], and Private Set Union (PSU) [24, 15, 17, 12, 32] have attracted a lot of attention from the research community. -
1 Measurable Sets
Math 4351, Fall 2018 Chapter 11 in Goldberg 1 Measurable Sets Our goal is to define what is meant by a measurable set E ⊆ [a; b] ⊂ R and a measurable function f :[a; b] ! R. We defined the length of an open set and a closed set, denoted as jGj and jF j, e.g., j[a; b]j = b − a: We will use another notation for complement and the notation in the Goldberg text. Let Ec = [a; b] n E = [a; b] − E. Also, E1 n E2 = E1 − E2: Definitions: Let E ⊆ [a; b]. Outer measure of a set E: m(E) = inffjGj : for all G open and E ⊆ Gg. Inner measure of a set E: m(E) = supfjF j : for all F closed and F ⊆ Eg: 0 ≤ m(E) ≤ m(E). A set E is a measurable set if m(E) = m(E) and the measure of E is denoted as m(E). The symmetric difference of two sets E1 and E2 is defined as E1∆E2 = (E1 − E2) [ (E2 − E1): A set is called an Fσ set (F-sigma set) if it is a union of a countable number of closed sets. A set is called a Gδ set (G-delta set) if it is a countable intersection of open sets. Properties of Measurable Sets on [a; b]: 1. If E1 and E2 are subsets of [a; b] and E1 ⊆ E2, then m(E1) ≤ m(E2) and m(E1) ≤ m(E2). In addition, if E1 and E2 are measurable subsets of [a; b] and E1 ⊆ E2, then m(E1) ≤ m(E2). -
Julia's Efficient Algorithm for Subtyping Unions and Covariant
Julia’s Efficient Algorithm for Subtyping Unions and Covariant Tuples Benjamin Chung Northeastern University, Boston, MA, USA [email protected] Francesco Zappa Nardelli Inria of Paris, Paris, France [email protected] Jan Vitek Northeastern University, Boston, MA, USA Czech Technical University in Prague, Czech Republic [email protected] Abstract The Julia programming language supports multiple dispatch and provides a rich type annotation language to specify method applicability. When multiple methods are applicable for a given call, Julia relies on subtyping between method signatures to pick the correct method to invoke. Julia’s subtyping algorithm is surprisingly complex, and determining whether it is correct remains an open question. In this paper, we focus on one piece of this problem: the interaction between union types and covariant tuples. Previous work normalized unions inside tuples to disjunctive normal form. However, this strategy has two drawbacks: complex type signatures induce space explosion, and interference between normalization and other features of Julia’s type system. In this paper, we describe the algorithm that Julia uses to compute subtyping between tuples and unions – an algorithm that is immune to space explosion and plays well with other features of the language. We prove this algorithm correct and complete against a semantic-subtyping denotational model in Coq. 2012 ACM Subject Classification Theory of computation → Type theory Keywords and phrases Type systems, Subtyping, Union types Digital Object Identifier 10.4230/LIPIcs.ECOOP.2019.24 Category Pearl Supplement Material ECOOP 2019 Artifact Evaluation approved artifact available at https://dx.doi.org/10.4230/DARTS.5.2.8 Acknowledgements The authors thank Jiahao Chen for starting us down the path of understanding Julia, and Jeff Bezanson for coming up with Julia’s subtyping algorithm. -
Set Difference and Symmetric Difference of Fuzzy Sets
Preliminaries Symmetric Dierence Set Dierence and Symmetric Dierence of Fuzzy Sets N.R. Vemuri A.S. Hareesh M.S. Srinath Department of Mathematics Indian Institute of Technology, Hyderabad and Department of Mathematics and Computer Science Sri Sathya Sai Institute of Higher Learning, India Fuzzy Sets Theory and Applications 2014, Liptovský Ján, Slovak Republic Vemuri, Sai Hareesh & Srinath Symmetric Dierence Preliminaries Introduction Symmetric Dierence Earlier work Outline 1 Preliminaries Introduction Earlier work 2 Symmetric Dierence Denition Examples Properties Applications Future Work References Vemuri, Sai Hareesh & Srinath Symmetric Dierence Preliminaries Introduction Symmetric Dierence Earlier work Classical set theory Set operations Union- [ Intersection - \ Complement - c Dierence -n Symmetric dierence - ∆ .... Vemuri, Sai Hareesh & Srinath Symmetric Dierence Preliminaries Introduction Symmetric Dierence Earlier work Classical set theory Set operations Union- [ Intersection - \ Complement - c Dierence -n Symmetric dierence - ∆ .... Vemuri, Sai Hareesh & Srinath Symmetric Dierence Preliminaries Introduction Symmetric Dierence Earlier work Classical set theory Set operations Union- [ Intersection - \ Complement - c Dierence -n Symmetric dierence - ∆ .... Vemuri, Sai Hareesh & Srinath Symmetric Dierence Preliminaries Introduction Symmetric Dierence Earlier work Classical set theory Set operations Union- [ Intersection - \ Complement - c Dierence -n Symmetric dierence - ∆ .... Vemuri, Sai Hareesh & Srinath Symmetric Dierence Preliminaries -
E. A. Emerson and C. S. Jutla, Tree Automata, Mu-Calculus, And
Tree Automata MuCalculus and Determinacy Extended Abstract EA Emerson and CS Jutla The University of Texas at Austin IBM TJ Watson Research Center Abstract that tree automata are closed under disjunction pro jection and complementation While the rst two We show that the prop ositional MuCalculus is eq are rather easy the pro of of Rabins Complemen uivalent in expressivepower to nite automata on in tation Lemma is extraordinarily complex and di nite trees Since complementation is trivial in the Mu cult Because of the imp ortance of the Complemen Calculus our equivalence provides a radically sim tation Lemma a numb er of authors have endeavored plied alternative pro of of Rabins complementation and continue to endeavor to simplify the argument lemma for tree automata which is the heart of one HR GH MS Mu Perhaps the b est of the deep est decidability results We also showhow known of these is the imp ortant work of Gurevich MuCalculus can b e used to establish determinacy of and Harrington GH which attacks the problem innite games used in earlier pro ofs of complementa from the standp oint of determinacy of innite games tion lemma and certain games used in the theory of While the presentation is brief the argument is still online algorithms extremely dicult and is probably b est appreciated Intro duction y the page supplementofMonk when accompanied b Mon We prop ose the prop ositional Mucalculus as a uniform framework for understanding and simplify In this pap er we present a new enormously sim ing the imp ortant and technically challenging -
Naïve Set Theory Basic Definitions Naïve Set Theory Is the Non-Axiomatic Treatment of Set Theory
Naïve Set Theory Basic Definitions Naïve set theory is the non-axiomatic treatment of set theory. In the axiomatic treatment, which we will only allude to at times, a set is an undefined term. For us however, a set will be thought of as a collection of some (possibly none) objects. These objects are called the members (or elements) of the set. We use the symbol "∈" to indicate membership in a set. Thus, if A is a set and x is one of its members, we write x ∈ A and say "x is an element of A" or "x is in A" or "x is a member of A". Note that "∈" is not the same as the Greek letter "ε" epsilon. Basic Definitions Sets can be described notationally in many ways, but always using the set brackets "{" and "}". If possible, one can just list the elements of the set: A = {1,3, oranges, lions, an old wad of gum} or give an indication of the elements: ℕ = {1,2,3, ... } ℤ = {..., -2,-1,0,1,2, ...} or (most frequently in mathematics) using set-builder notation: S = {x ∈ ℝ | 1 < x ≤ 7 } or {x ∈ ℝ : 1 < x ≤ 7 } which is read as "S is the set of real numbers x, such that x is greater than 1 and less than or equal to 7". In some areas of mathematics sets may be denoted by special notations. For instance, in analysis S would be written (1,7]. Basic Definitions Note that sets do not contain repeated elements. An element is either in or not in a set, never "in the set 5 times" for instance.