A Class of Symmetric Difference-Closed Sets Related to Commuting Involutions John Campbell
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Natural Numerosities of Sets of Tuples
TRANSACTIONS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 367, Number 1, January 2015, Pages 275–292 S 0002-9947(2014)06136-9 Article electronically published on July 2, 2014 NATURAL NUMEROSITIES OF SETS OF TUPLES MARCO FORTI AND GIUSEPPE MORANA ROCCASALVO Abstract. We consider a notion of “numerosity” for sets of tuples of natural numbers that satisfies the five common notions of Euclid’s Elements, so it can agree with cardinality only for finite sets. By suitably axiomatizing such a no- tion, we show that, contrasting to cardinal arithmetic, the natural “Cantorian” definitions of order relation and arithmetical operations provide a very good algebraic structure. In fact, numerosities can be taken as the non-negative part of a discretely ordered ring, namely the quotient of a formal power series ring modulo a suitable (“gauge”) ideal. In particular, special numerosities, called “natural”, can be identified with the semiring of hypernatural numbers of appropriate ultrapowers of N. Introduction In this paper we consider a notion of “equinumerosity” on sets of tuples of natural numbers, i.e. an equivalence relation, finer than equicardinality, that satisfies the celebrated five Euclidean common notions about magnitudines ([8]), including the principle that “the whole is greater than the part”. This notion preserves the basic properties of equipotency for finite sets. In particular, the natural Cantorian definitions endow the corresponding numerosities with a structure of a discretely ordered semiring, where 0 is the size of the emptyset, 1 is the size of every singleton, and greater numerosity corresponds to supersets. This idea of “Euclidean” numerosity has been recently investigated by V. -
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). -
MTH 304: General Topology Semester 2, 2017-2018
MTH 304: General Topology Semester 2, 2017-2018 Dr. Prahlad Vaidyanathan Contents I. Continuous Functions3 1. First Definitions................................3 2. Open Sets...................................4 3. Continuity by Open Sets...........................6 II. Topological Spaces8 1. Definition and Examples...........................8 2. Metric Spaces................................. 11 3. Basis for a topology.............................. 16 4. The Product Topology on X × Y ...................... 18 Q 5. The Product Topology on Xα ....................... 20 6. Closed Sets.................................. 22 7. Continuous Functions............................. 27 8. The Quotient Topology............................ 30 III.Properties of Topological Spaces 36 1. The Hausdorff property............................ 36 2. Connectedness................................. 37 3. Path Connectedness............................. 41 4. Local Connectedness............................. 44 5. Compactness................................. 46 6. Compact Subsets of Rn ............................ 50 7. Continuous Functions on Compact Sets................... 52 8. Compactness in Metric Spaces........................ 56 9. Local Compactness.............................. 59 IV.Separation Axioms 62 1. Regular Spaces................................ 62 2. Normal Spaces................................ 64 3. Tietze's extension Theorem......................... 67 4. Urysohn Metrization Theorem........................ 71 5. Imbedding of Manifolds.......................... -
Old Notes from Warwick, Part 1
Measure Theory, MA 359 Handout 1 Valeriy Slastikov Autumn, 2005 1 Measure theory 1.1 General construction of Lebesgue measure In this section we will do the general construction of σ-additive complete measure by extending initial σ-additive measure on a semi-ring to a measure on σ-algebra generated by this semi-ring and then completing this measure by adding to the σ-algebra all the null sets. This section provides you with the essentials of the construction and make some parallels with the construction on the plane. Throughout these section we will deal with some collection of sets whose elements are subsets of some fixed abstract set X. It is not necessary to assume any topology on X but for simplicity you may imagine X = Rn. We start with some important definitions: Definition 1.1 A nonempty collection of sets S is a semi-ring if 1. Empty set ? 2 S; 2. If A 2 S; B 2 S then A \ B 2 S; n 3. If A 2 S; A ⊃ A1 2 S then A = [k=1Ak, where Ak 2 S for all 1 ≤ k ≤ n and Ak are disjoint sets. If the set X 2 S then S is called semi-algebra, the set X is called a unit of the collection of sets S. Example 1.1 The collection S of intervals [a; b) for all a; b 2 R form a semi-ring since 1. empty set ? = [a; a) 2 S; 2. if A 2 S and B 2 S then A = [a; b) and B = [c; d). -
Equivalents to the Axiom of Choice and Their Uses A
EQUIVALENTS TO THE AXIOM OF CHOICE AND THEIR USES A Thesis Presented to The Faculty of the Department of Mathematics California State University, Los Angeles In Partial Fulfillment of the Requirements for the Degree Master of Science in Mathematics By James Szufu Yang c 2015 James Szufu Yang ALL RIGHTS RESERVED ii The thesis of James Szufu Yang is approved. Mike Krebs, Ph.D. Kristin Webster, Ph.D. Michael Hoffman, Ph.D., Committee Chair Grant Fraser, Ph.D., Department Chair California State University, Los Angeles June 2015 iii ABSTRACT Equivalents to the Axiom of Choice and Their Uses By James Szufu Yang In set theory, the Axiom of Choice (AC) was formulated in 1904 by Ernst Zermelo. It is an addition to the older Zermelo-Fraenkel (ZF) set theory. We call it Zermelo-Fraenkel set theory with the Axiom of Choice and abbreviate it as ZFC. This paper starts with an introduction to the foundations of ZFC set the- ory, which includes the Zermelo-Fraenkel axioms, partially ordered sets (posets), the Cartesian product, the Axiom of Choice, and their related proofs. It then intro- duces several equivalent forms of the Axiom of Choice and proves that they are all equivalent. In the end, equivalents to the Axiom of Choice are used to prove a few fundamental theorems in set theory, linear analysis, and abstract algebra. This paper is concluded by a brief review of the work in it, followed by a few points of interest for further study in mathematics and/or set theory. iv ACKNOWLEDGMENTS Between the two department requirements to complete a master's degree in mathematics − the comprehensive exams and a thesis, I really wanted to experience doing a research and writing a serious academic paper. -
Regularity Properties and Determinacy
Regularity Properties and Determinacy MSc Thesis (Afstudeerscriptie) written by Yurii Khomskii (born September 5, 1980 in Moscow, Russia) under the supervision of Dr. Benedikt L¨owe, and submitted to the Board of Examiners in partial fulfillment of the requirements for the degree of MSc in Logic at the Universiteit van Amsterdam. Date of the public defense: Members of the Thesis Committee: August 14, 2007 Dr. Benedikt L¨owe Prof. Dr. Jouko V¨a¨an¨anen Prof. Dr. Joel David Hamkins Prof. Dr. Peter van Emde Boas Brian Semmes i Contents 0. Introduction............................ 1 1. Preliminaries ........................... 4 1.1 Notation. ........................... 4 1.2 The Real Numbers. ...................... 5 1.3 Trees. ............................. 6 1.4 The Forcing Notions. ..................... 7 2. ClasswiseConsequencesofDeterminacy . 11 2.1 Regularity Properties. .................... 11 2.2 Infinite Games. ........................ 14 2.3 Classwise Implications. .................... 16 3. The Marczewski-Burstin Algebra and the Baire Property . 20 3.1 MB and BP. ......................... 20 3.2 Fusion Sequences. ...................... 23 3.3 Counter-examples. ...................... 26 4. DeterminacyandtheBaireProperty.. 29 4.1 Generalized MB-algebras. .................. 29 4.2 Determinacy and BP(P). ................... 31 4.3 Determinacy and wBP(P). .................. 34 5. Determinacy andAsymmetric Properties. 39 5.1 The Asymmetric Properties. ................. 39 5.2 The General Definition of Asym(P). ............. 43 5.3 Determinacy and Asym(P). ................. 46 ii iii 0. Introduction One of the most intriguing developments of modern set theory is the investi- gation of two-player infinite games of perfect information. Of course, it is clear that applied game theory, as any other branch of mathematics, can be modeled in set theory. But we are talking about the converse: the use of infinite games as a tool to study fundamental set theoretic questions. -
Set Theory in Computer Science a Gentle Introduction to Mathematical Modeling I
Set Theory in Computer Science A Gentle Introduction to Mathematical Modeling I Jose´ Meseguer University of Illinois at Urbana-Champaign Urbana, IL 61801, USA c Jose´ Meseguer, 2008–2010; all rights reserved. February 28, 2011 2 Contents 1 Motivation 7 2 Set Theory as an Axiomatic Theory 11 3 The Empty Set, Extensionality, and Separation 15 3.1 The Empty Set . 15 3.2 Extensionality . 15 3.3 The Failed Attempt of Comprehension . 16 3.4 Separation . 17 4 Pairing, Unions, Powersets, and Infinity 19 4.1 Pairing . 19 4.2 Unions . 21 4.3 Powersets . 24 4.4 Infinity . 26 5 Case Study: A Computable Model of Hereditarily Finite Sets 29 5.1 HF-Sets in Maude . 30 5.2 Terms, Equations, and Term Rewriting . 33 5.3 Confluence, Termination, and Sufficient Completeness . 36 5.4 A Computable Model of HF-Sets . 39 5.5 HF-Sets as a Universe for Finitary Mathematics . 43 5.6 HF-Sets with Atoms . 47 6 Relations, Functions, and Function Sets 51 6.1 Relations and Functions . 51 6.2 Formula, Assignment, and Lambda Notations . 52 6.3 Images . 54 6.4 Composing Relations and Functions . 56 6.5 Abstract Products and Disjoint Unions . 59 6.6 Relating Function Sets . 62 7 Simple and Primitive Recursion, and the Peano Axioms 65 7.1 Simple Recursion . 65 7.2 Primitive Recursion . 67 7.3 The Peano Axioms . 69 8 Case Study: The Peano Language 71 9 Binary Relations on a Set 73 9.1 Directed and Undirected Graphs . 73 9.2 Transition Systems and Automata . -
Measures 1 Introduction
Measures These preliminary lecture notes are partly based on textbooks by Athreya and Lahiri, Capinski and Kopp, and Folland. 1 Introduction Our motivation for studying measure theory is to lay a foundation for mod- eling probabilities. I want to give a bit of motivation for the structure of measures that has developed by providing a sort of history of thought of measurement. Not really history of thought, this is more like a fictionaliza- tion (i.e. with the story but minus the proofs of assertions) of a standard treatment of Lebesgue measure that you can find, for example, in Capin- ski and Kopp, Chapter 2, or in Royden, books that begin by first studying Lebesgue measure on <. For studying probability, we have to study measures more generally; that will follow the introduction. People were interested in measuring length, area, volume etc. Let's stick to length. How was one to extend the notion of the length of an interval (a; b), l(a; b) = b − a to more general subsets of <? Given an interval I of any type (closed, open, left-open-right-closed, etc.), let l(I) be its length (the difference between its larger and smaller endpoints). Then the notion of Lebesgue outer measure (LOM) of a set A 2 < was defined as follows. Cover A with a countable collection of intervals, and measure the sum of lengths of these intervals. Take the smallest such sum, over all countable collections of ∗ intervals that cover A, to be the LOM of A. That is, m (A) = inf ZA, where 1 1 X 1 ZA = f l(In): A ⊆ [n=1Ing n=1 ∗ (the In referring to intervals). -
Disjoint Sets and Minimum Spanning Trees Introduction Disjoint-Set Data
COMS21103 Introduction I In this lecture we will start by discussing a data structure used for maintaining disjoint subsets of some bigger set. Disjoint sets and minimum spanning trees I This has a number of applications, including to maintaining connected Ashley Montanaro components of a graph, and to finding minimum spanning trees in [email protected] undirected graphs. Department of Computer Science, University of Bristol I We will then discuss two algorithms for finding minimum spanning Bristol, UK trees: an algorithm by Kruskal based on disjoint-set structures, and an algorithm by Prim which is similar to Dijkstra’s algorithm. 11 November 2013 I In both cases, we will see that efficient implementations of data structures give us efficient algorithms. Ashley Montanaro Ashley Montanaro [email protected] [email protected] COMS21103: Disjoint sets and MSTs Slide 1/48 COMS21103: Disjoint sets and MSTs Slide 2/48 Disjoint-set data structure Example A disjoint-set data structure maintains a collection = S ,..., S of S { 1 k } disjoint subsets of some larger “universe” U. Operation Returns S The data structure supports the following operations: (start) (empty) MakeSet(a) a { } 1. MakeSet(x): create a new set whose only member is x. As the sets MakeSet(b) a , b { } { } are disjoint, we require that x is not contained in any of the other sets. FindSet(b) b a , b { } { } 2. Union(x, y): combine the sets containing x and y (call these S , S ) to Union(a, b) a, b x y { } replace them with a new set S S . -
The Axiom of Choice
THE AXIOM OF CHOICE THOMAS J. JECH State University of New York at Bufalo and The Institute for Advanced Study Princeton, New Jersey 1973 NORTH-HOLLAND PUBLISHING COMPANY - AMSTERDAM LONDON AMERICAN ELSEVIER PUBLISHING COMPANY, INC. - NEW YORK 0 NORTH-HOLLAND PUBLISHING COMPANY - 1973 AN Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior permission of the Copyright owner. Library of Congress Catalog Card Number 73-15535 North-Holland ISBN for the series 0 7204 2200 0 for this volume 0 1204 2215 2 American Elsevier ISBN 0 444 10484 4 Published by: North-Holland Publishing Company - Amsterdam North-Holland Publishing Company, Ltd. - London Sole distributors for the U.S.A. and Canada: American Elsevier Publishing Company, Inc. 52 Vanderbilt Avenue New York, N.Y. 10017 PRINTED IN THE NETHERLANDS To my parents PREFACE The book was written in the long Buffalo winter of 1971-72. It is an attempt to show the place of the Axiom of Choice in contemporary mathe- matics. Most of the material covered in the book deals with independence and relative strength of various weaker versions and consequences of the Axiom of Choice. Also included are some other results that I found relevant to the subject. The selection of the topics and results is fairly comprehensive, nevertheless it is a selection and as such reflects the personal taste of the author. So does the treatment of the subject. The main tool used throughout the text is Cohen’s method of forcing. -
The Duality of Similarity and Metric Spaces
applied sciences Article The Duality of Similarity and Metric Spaces OndˇrejRozinek 1,2,* and Jan Mareš 1,3,* 1 Department of Process Control, Faculty of Electrical Engineering and Informatics, University of Pardubice, 530 02 Pardubice, Czech Republic 2 CEO at Rozinet s.r.o., 533 52 Srch, Czech Republic 3 Department of Computing and Control Engineering, Faculty of Chemical Engineering, University of Chemistry and Technology, 166 28 Prague, Czech Republic * Correspondence: [email protected] (O.R.); [email protected] (J.M.) Abstract: We introduce a new mathematical basis for similarity space. For the first time, we describe the relationship between distance and similarity from set theory. Then, we derive generally valid relations for the conversion between similarity and a metric and vice versa. We present a general solution for the normalization of a given similarity space or metric space. The derived solutions lead to many already used similarity and distance functions, and combine them into a unified theory. The Jaccard coefficient, Tanimoto coefficient, Steinhaus distance, Ruzicka similarity, Gaussian similarity, edit distance and edit similarity satisfy this relationship, which verifies our fundamental theory. Keywords: similarity metric; similarity space; distance metric; metric space; normalized similarity metric; normalized distance metric; edit distance; edit similarity; Jaccard coefficient; Gaussian similarity 1. Introduction Mathematical spaces have been studied for centuries and belong to the basic math- ematical theories, which are used in various real-world applications [1]. In general, a mathematical space is a set of mathematical objects with an associated structure. This Citation: Rozinek, O.; Mareš, J. The structure can be specified by a number of operations on the objects of the set. -
The Associativity of the Symmetric Difference
VOL. 79, NO. 5, DECEMBER 2006 391 REFERENCES 1. R. Laatsch, Measuring the abundance of integers, this MAGAZINE 59 (1986), 84–92. 2. K. H. Rosen, Elementary Number Theory and its Applications, 5th ed., Pearson Addison Wesley, Boston, 2005. 3. R. F. Ryan, A simpler dense proof regarding the abundancy index, this MAGAZINE 76 (2003), 299–301. 4. P. A. Weiner, The abundancy index, a measure of perfection, this MAGAZINE 73 (2000), 307–310. The Associativity of the Symmetric Difference MAJID HOSSEINI State University of New York at New Paltz New Paltz, NY 12561–2443 [email protected] The symmetric difference of two sets A and B is defined by AB = (A \ B) ∪ (B \ A). It is easy to verify that is commutative. However, associativity of is not as straightforward to establish, and usually it is given as a challenging exercise to students learning set operations (see [1, p. 32, exercise 15], [3, p. 34, exercise 2(a)], and [2,p.18]). In this note we provide a short proof of the associativity of . This proof is not new. A slightly different version appears in Yousefnia [4]. However, the proof is not readily accessible to anyone unfamiliar with Persian. Consider three sets A, B,andC. We define our universe to be X = A ∪ B ∪ C.For any subset U of X, define the characteristic function of U by 1, if x ∈ U; χ (x) = U 0, if x ∈ X \ U. Two subsets U and V of X are equal if and only if χU = χV . The following lemma is the key to our proof.