Chapter 1: Sets and Functions
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Mathematics I Basic Mathematics
Mathematics I Basic Mathematics Prepared by Prof. Jairus. Khalagai African Virtual university Université Virtuelle Africaine Universidade Virtual Africana African Virtual University NOTICE This document is published under the conditions of the Creative Commons http://en.wikipedia.org/wiki/Creative_Commons Attribution http://creativecommons.org/licenses/by/2.5/ License (abbreviated “cc-by”), Version 2.5. African Virtual University Table of ConTenTs I. Mathematics 1, Basic Mathematics _____________________________ 3 II. Prerequisite Course or Knowledge _____________________________ 3 III. Time ____________________________________________________ 3 IV. Materials _________________________________________________ 3 V. Module Rationale __________________________________________ 4 VI. Content __________________________________________________ 5 6.1 Overview ____________________________________________ 5 6.2 Outline _____________________________________________ 6 VII. General Objective(s) ________________________________________ 8 VIII. Specific Learning Objectives __________________________________ 8 IX. Teaching and Learning Activities ______________________________ 10 X. Key Concepts (Glossary) ____________________________________ 16 XI. Compulsory Readings ______________________________________ 18 XII. Compulsory Resources _____________________________________ 19 XIII. Useful Links _____________________________________________ 20 XIV. Learning Activities _________________________________________ 23 XV. Synthesis Of The Module ___________________________________ -
A Proof of Cantor's Theorem
Cantor’s Theorem Joe Roussos 1 Preliminary ideas Two sets have the same number of elements (are equinumerous, or have the same cardinality) iff there is a bijection between the two sets. Mappings: A mapping, or function, is a rule that associates elements of one set with elements of another set. We write this f : X ! Y , f is called the function/mapping, the set X is called the domain, and Y is called the codomain. We specify what the rule is by writing f(x) = y or f : x 7! y. e.g. X = f1; 2; 3g;Y = f2; 4; 6g, the map f(x) = 2x associates each element x 2 X with the element in Y that is double it. A bijection is a mapping that is injective and surjective.1 • Injective (one-to-one): A function is injective if it takes each element of the do- main onto at most one element of the codomain. It never maps more than one element in the domain onto the same element in the codomain. Formally, if f is a function between set X and set Y , then f is injective iff 8a; b 2 X; f(a) = f(b) ! a = b • Surjective (onto): A function is surjective if it maps something onto every element of the codomain. It can map more than one thing onto the same element in the codomain, but it needs to hit everything in the codomain. Formally, if f is a function between set X and set Y , then f is surjective iff 8y 2 Y; 9x 2 X; f(x) = y Figure 1: Injective map. -
1 Elementary Set Theory
1 Elementary Set Theory Notation: fg enclose a set. f1; 2; 3g = f3; 2; 2; 1; 3g because a set is not defined by order or multiplicity. f0; 2; 4;:::g = fxjx is an even natural numberg because two ways of writing a set are equivalent. ; is the empty set. x 2 A denotes x is an element of A. N = f0; 1; 2;:::g are the natural numbers. Z = f:::; −2; −1; 0; 1; 2;:::g are the integers. m Q = f n jm; n 2 Z and n 6= 0g are the rational numbers. R are the real numbers. Axiom 1.1. Axiom of Extensionality Let A; B be sets. If (8x)x 2 A iff x 2 B then A = B. Definition 1.1 (Subset). Let A; B be sets. Then A is a subset of B, written A ⊆ B iff (8x) if x 2 A then x 2 B. Theorem 1.1. If A ⊆ B and B ⊆ A then A = B. Proof. Let x be arbitrary. Because A ⊆ B if x 2 A then x 2 B Because B ⊆ A if x 2 B then x 2 A Hence, x 2 A iff x 2 B, thus A = B. Definition 1.2 (Union). Let A; B be sets. The Union A [ B of A and B is defined by x 2 A [ B if x 2 A or x 2 B. Theorem 1.2. A [ (B [ C) = (A [ B) [ C Proof. Let x be arbitrary. x 2 A [ (B [ C) iff x 2 A or x 2 B [ C iff x 2 A or (x 2 B or x 2 C) iff x 2 A or x 2 B or x 2 C iff (x 2 A or x 2 B) or x 2 C iff x 2 A [ B or x 2 C iff x 2 (A [ B) [ C Definition 1.3 (Intersection). -
Linear Transformation (Sections 1.8, 1.9) General View: Given an Input, the Transformation Produces an Output
Linear Transformation (Sections 1.8, 1.9) General view: Given an input, the transformation produces an output. In this sense, a function is also a transformation. 1 4 3 1 3 Example. Let A = and x = 1 . Describe matrix-vector multiplication Ax 2 0 5 1 1 1 in the language of transformation. 1 4 3 1 31 5 Ax b 2 0 5 11 8 1 Vector x is transformed into vector b by left matrix multiplication Definition and terminologies. Transformation (or function or mapping) T from Rn to Rm is a rule that assigns to each vector x in Rn a vector T(x) in Rm. • Notation: T: Rn → Rm • Rn is the domain of T • Rm is the codomain of T • T(x) is the image of vector x • The set of all images T(x) is the range of T • When T(x) = Ax, A is a m×n size matrix. Range of T = Span{ column vectors of A} (HW1.8.7) See class notes for other examples. Linear Transformation --- Existence and Uniqueness Questions (Section 1.9) Definition 1: T: Rn → Rm is onto if each b in Rm is the image of at least one x in Rn. • i.e. codomain Rm = range of T • When solve T(x) = b for x (or Ax=b, A is the standard matrix), there exists at least one solution (Existence question). Definition 2: T: Rn → Rm is one-to-one if each b in Rm is the image of at most one x in Rn. • i.e. When solve T(x) = b for x (or Ax=b, A is the standard matrix), there exists either a unique solution or none at all (Uniqueness question). -
Algebra I Chapter 1. Basic Facts from Set Theory 1.1 Glossary of Abbreviations
Notes: c F.P. Greenleaf, 2000-2014 v43-s14sets.tex (version 1/1/2014) Algebra I Chapter 1. Basic Facts from Set Theory 1.1 Glossary of abbreviations. Below we list some standard math symbols that will be used as shorthand abbreviations throughout this course. means “for all; for every” • ∀ means “there exists (at least one)” • ∃ ! means “there exists exactly one” • ∃ s.t. means “such that” • = means “implies” • ⇒ means “if and only if” • ⇐⇒ x A means “the point x belongs to a set A;” x / A means “x is not in A” • ∈ ∈ N denotes the set of natural numbers (counting numbers) 1, 2, 3, • · · · Z denotes the set of all integers (positive, negative or zero) • Q denotes the set of rational numbers • R denotes the set of real numbers • C denotes the set of complex numbers • x A : P (x) If A is a set, this denotes the subset of elements x in A such that •statement { ∈ P (x)} is true. As examples of the last notation for specifying subsets: x R : x2 +1 2 = ( , 1] [1, ) { ∈ ≥ } −∞ − ∪ ∞ x R : x2 +1=0 = { ∈ } ∅ z C : z2 +1=0 = +i, i where i = √ 1 { ∈ } { − } − 1.2 Basic facts from set theory. Next we review the basic definitions and notations of set theory, which will be used throughout our discussions of algebra. denotes the empty set, the set with nothing in it • ∅ x A means that the point x belongs to a set A, or that x is an element of A. • ∈ A B means A is a subset of B – i.e. -
MATH 361 Homework 9
MATH 361 Homework 9 Royden 3.3.9 First, we show that for any subset E of the real numbers, Ec + y = (E + y)c (translating the complement is equivalent to the complement of the translated set). Without loss of generality, assume E can be written as c an open interval (e1; e2), so that E + y is represented by the set fxjx 2 (−∞; e1 + y) [ (e2 + y; +1)g. This c is equal to the set fxjx2 = (e1 + y; e2 + y)g, which is equivalent to the set (E + y) . Second, Let B = A − y. From Homework 8, we know that outer measure is invariant under translations. Using this along with the fact that E is measurable: m∗(A) = m∗(B) = m∗(B \ E) + m∗(B \ Ec) = m∗((B \ E) + y) + m∗((B \ Ec) + y) = m∗(((A − y) \ E) + y) + m∗(((A − y) \ Ec) + y) = m∗(A \ (E + y)) + m∗(A \ (Ec + y)) = m∗(A \ (E + y)) + m∗(A \ (E + y)c) The last line follows from Ec + y = (E + y)c. Royden 3.3.10 First, since E1;E2 2 M and M is a σ-algebra, E1 [ E2;E1 \ E2 2 M. By the measurability of E1 and E2: ∗ ∗ ∗ c m (E1) = m (E1 \ E2) + m (E1 \ E2) ∗ ∗ ∗ c m (E2) = m (E2 \ E1) + m (E2 \ E1) ∗ ∗ ∗ ∗ c ∗ c m (E1) + m (E2) = 2m (E1 \ E2) + m (E1 \ E2) + m (E1 \ E2) ∗ ∗ ∗ c ∗ c = m (E1 \ E2) + [m (E1 \ E2) + m (E1 \ E2) + m (E1 \ E2)] c c Second, E1 \ E2, E1 \ E2, and E1 \ E2 are disjoint sets whose union is equal to E1 [ E2. -
Axioms and Algebraic Systems*
Axioms and algebraic systems* Leong Yu Kiang Department of Mathematics National University of Singapore In this talk, we introduce the important concept of a group, mention some equivalent sets of axioms for groups, and point out the relationship between the individual axioms. We also mention briefly the definitions of a ring and a field. Definition 1. A binary operation on a non-empty set S is a rule which associates to each ordered pair (a, b) of elements of S a unique element, denoted by a* b, in S. The binary relation itself is often denoted by *· It may also be considered as a mapping from S x S to S, i.e., * : S X S ~ S, where (a, b) ~a* b, a, bE S. Example 1. Ordinary addition and multiplication of real numbers are binary operations on the set IR of real numbers. We write a+ b, a· b respectively. Ordinary division -;- is a binary relation on the set IR* of non-zero real numbers. We write a -;- b. Definition 2. A binary relation * on S is associative if for every a, b, c in s, (a* b) * c =a* (b *c). Example 2. The binary operations + and · on IR (Example 1) are as sociative. The binary relation -;- on IR* (Example 1) is not associative smce 1 3 1 ~ 2) ~ 3 _J_ - 1 ~ (2 ~ 3). ( • • = -6 I 2 = • • * Talk given at the Workshop on Algebraic Structures organized by the Singapore Mathemat- ical Society for school teachers on 5 September 1988. 11 Definition 3. A semi-group is a non-€mpty set S together with an asso ciative binary operation *, and is denoted by (S, *). -
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. -
A Note on Indicator-Functions
proceedings of the american mathematical society Volume 39, Number 1, June 1973 A NOTE ON INDICATOR-FUNCTIONS J. MYHILL Abstract. A system has the existence-property for abstracts (existence property for numbers, disjunction-property) if when- ever V(1x)A(x), M(t) for some abstract (t) (M(n) for some numeral n; if whenever VAVB, VAor hß. (3x)A(x), A,Bare closed). We show that the existence-property for numbers and the disjunc- tion property are never provable in the system itself; more strongly, the (classically) recursive functions that encode these properties are not provably recursive functions of the system. It is however possible for a system (e.g., ZF+K=L) to prove the existence- property for abstracts for itself. In [1], I presented an intuitionistic form Z of Zermelo-Frankel set- theory (without choice and with weakened regularity) and proved for it the disfunction-property (if VAvB (closed), then YA or YB), the existence- property (if \-(3x)A(x) (closed), then h4(t) for a (closed) comprehension term t) and the existence-property for numerals (if Y(3x G m)A(x) (closed), then YA(n) for a numeral n). In the appendix to [1], I enquired if these results could be proved constructively; in particular if we could find primitive recursively from the proof of AwB whether it was A or B that was provable, and likewise in the other two cases. Discussion of this question is facilitated by introducing the notion of indicator-functions in the sense of the following Definition. Let T be a consistent theory which contains Heyting arithmetic (possibly by relativization of quantifiers). -
Soft Set Theory First Results
An Inlum~md computers & mathematics PERGAMON Computers and Mathematics with Applications 37 (1999) 19-31 Soft Set Theory First Results D. MOLODTSOV Computing Center of the R~msian Academy of Sciences 40 Vavilova Street, Moscow 117967, Russia Abstract--The soft set theory offers a general mathematical tool for dealing with uncertain, fuzzy, not clearly defined objects. The main purpose of this paper is to introduce the basic notions of the theory of soft sets, to present the first results of the theory, and to discuss some problems of the future. (~) 1999 Elsevier Science Ltd. All rights reserved. Keywords--Soft set, Soft function, Soft game, Soft equilibrium, Soft integral, Internal regular- ization. 1. INTRODUCTION To solve complicated problems in economics, engineering, and environment, we cannot success- fully use classical methods because of various uncertainties typical for those problems. There are three theories: theory of probability, theory of fuzzy sets, and the interval mathematics which we can consider as mathematical tools for dealing with uncertainties. But all these theories have their own difficulties. Theory of probabilities can deal only with stochastically stable phenomena. Without going into mathematical details, we can say, e.g., that for a stochastically stable phenomenon there should exist a limit of the sample mean #n in a long series of trials. The sample mean #n is defined by 1 n IZn = -- ~ Xi, n i=l where x~ is equal to 1 if the phenomenon occurs in the trial, and x~ is equal to 0 if the phenomenon does not occur. To test the existence of the limit, we must perform a large number of trials. -
Primitive Recursive Functions Are Recursively Enumerable
AN ENUMERATION OF THE PRIMITIVE RECURSIVE FUNCTIONS WITHOUT REPETITION SHIH-CHAO LIU (Received April 15,1900) In a theorem and its corollary [1] Friedberg gave an enumeration of all the recursively enumerable sets without repetition and an enumeration of all the partial recursive functions without repetition. This note is to prove a similar theorem for the primitive recursive functions. The proof is only a classical one. We shall show that the theorem is intuitionistically unprovable in the sense of Kleene [2]. For similar reason the theorem by Friedberg is also intuitionistical- ly unprovable, which is not stated in his paper. THEOREM. There is a general recursive function ψ(n, a) such that the sequence ψ(0, a), ψ(l, α), is an enumeration of all the primitive recursive functions of one variable without repetition. PROOF. Let φ(n9 a) be an enumerating function of all the primitive recursive functions of one variable, (See [3].) We define a general recursive function v(a) as follows. v(0) = 0, v(n + 1) = μy, where μy is the least y such that for each j < n + 1, φ(y, a) =[= φ(v(j), a) for some a < n + 1. It is noted that the value v(n + 1) can be found by a constructive method, for obviously there exists some number y such that the primitive recursive function <p(y> a) takes a value greater than all the numbers φ(v(0), 0), φ(y(Ϋ), 0), , φ(v(n\ 0) f or a = 0 Put ψ(n, a) = φ{v{n), a). -
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.