Lecture 1. Basic Concepts of Set Theory, Functions and Relations



Similar documents
Basic Concepts of Set Theory, Functions and Relations

Basic Set Theory. 1. Motivation. Fido Sue. Fred Aristotle Bob. LX Semantics I September 11, 2008

A Little Set Theory (Never Hurt Anybody)

Set Theory Basic Concepts and Definitions

Automata and Formal Languages

THE LANGUAGE OF SETS AND SET NOTATION

INCIDENCE-BETWEENNESS GEOMETRY

Formal Languages and Automata Theory - Regular Expressions and Finite Automata -

Grade 6 Math Circles. Binary and Beyond

Set operations and Venn Diagrams. COPYRIGHT 2006 by LAVON B. PAGE

Chapter 3. Cartesian Products and Relations. 3.1 Cartesian Products

CS 3719 (Theory of Computation and Algorithms) Lecture 4

CONTENTS 1. Peter Kahn. Spring 2007

All of mathematics can be described with sets. This becomes more and

Grade 7/8 Math Circles Sequences and Series

This asserts two sets are equal iff they have the same elements, that is, a set is determined by its elements.

Math/Stats 425 Introduction to Probability. 1. Uncertainty and the axioms of probability

Cartesian Products and Relations

So let us begin our quest to find the holy grail of real analysis.

Set Theory: Shading Venn Diagrams

Math Week in Review #4. A proposition, or statement, is a declarative sentence that can be classified as either true or false, but not both.

The Set Data Model CHAPTER What This Chapter Is About

Section 4.1 Rules of Exponents

Lecture Note 1 Set and Probability Theory. MIT Spring 2006 Herman Bennett

Automata Theory. Şubat 2006 Tuğrul Yılmaz Ankara Üniversitesi

Reading 13 : Finite State Automata and Regular Expressions

Discrete Mathematics

6.045: Automata, Computability, and Complexity Or, Great Ideas in Theoretical Computer Science Spring, Class 4 Nancy Lynch

C H A P T E R Regular Expressions regular expression

Mathematics for Computer Science/Software Engineering. Notes for the course MSM1F3 Dr. R. A. Wilson

Regular Languages and Finite Automata

Solving Rational Equations

CHAPTER 3. Methods of Proofs. 1. Logical Arguments and Formal Proofs

Regular Expressions and Automata using Haskell

Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 2

6.4 Normal Distribution

Lecture 16 : Relations and Functions DRAFT

Book of Proof. Richard Hammack Virginia Commonwealth University

OA3-10 Patterns in Addition Tables

Sudoku puzzles and how to solve them

Course Syllabus. MATH 1350-Mathematics for Teachers I. Revision Date: 8/15/2016

arrays C Programming Language - Arrays

Georg Cantor and Set Theory

Probability: Terminology and Examples Class 2, 18.05, Spring 2014 Jeremy Orloff and Jonathan Bloom

INTRODUCTORY SET THEORY

CMPSCI 250: Introduction to Computation. Lecture #19: Regular Expressions and Their Languages David Mix Barrington 11 April 2013

Discrete Maths. Philippa Gardner. These lecture notes are based on previous notes by Iain Phillips.

3. Mathematical Induction

WRITING PROOFS. Christopher Heil Georgia Institute of Technology

Turing Machines: An Introduction

Order of Operations More Essential Practice

1.4 Compound Inequalities

Regular Languages and Finite State Machines

1.6 The Order of Operations

Writing Thesis Defense Papers

Colored Hats and Logic Puzzles

Inequalities - Absolute Value Inequalities

Access The Mathematics of Internet Search Engines

MATH 140 Lab 4: Probability and the Standard Normal Distribution

Algebra I Notes Relations and Functions Unit 03a

CHAPTER 7 GENERAL PROOF SYSTEMS

Mathematical Conventions Large Print (18 point) Edition

Computational Geometry Lab: FEM BASIS FUNCTIONS FOR A TETRAHEDRON

CHAPTER 2. Logic. 1. Logic Definitions. Notation: Variables are used to represent propositions. The most common variables used are p, q, and r.

ER modelling, Weak Entities, Class Hierarchies, Aggregation

Overview. Essential Questions. Precalculus, Quarter 4, Unit 4.5 Build Arithmetic and Geometric Sequences and Series

Relational Databases

2010/9/19. Binary number system. Binary numbers. Outline. Binary to decimal

A Few Basics of Probability

Math 3000 Section 003 Intro to Abstract Math Homework 2

An Introduction to Computer Science and Computer Organization Comp 150 Fall 2008

Factorizations: Searching for Factor Strings

Logic in Computer Science: Logic Gates

Creating, Solving, and Graphing Systems of Linear Equations and Linear Inequalities

Decomposing Numbers (Operations and Algebraic Thinking)

Basic Probability Concepts

25 Integers: Addition and Subtraction

Decision Making under Uncertainty

Handout #1: Mathematical Reasoning

No Solution Equations Let s look at the following equation: 2 +3=2 +7

Mathematical Conventions. for the Quantitative Reasoning Measure of the GRE revised General Test

The theory of the six stages of learning with integers (Published in Mathematics in Schools, Volume 29, Number 2, March 2000) Stage 1

CSC4510 AUTOMATA 2.1 Finite Automata: Examples and D efinitions Definitions

Linear Programming Notes V Problem Transformations

LINEAR INEQUALITIES. Mathematics is the art of saying many things in many different ways. MAXWELL

MATH 13150: Freshman Seminar Unit 10

Georg Cantor ( ):

Testing LTL Formula Translation into Büchi Automata

Useful Mathematical Symbols

Permutation Groups. Tom Davis April 2, 2003

Unit 1 Number Sense. In this unit, students will study repeating decimals, percents, fractions, decimals, and proportions.

7 Relations and Functions

chapter >> Consumer and Producer Surplus Section 3: Consumer Surplus, Producer Surplus, and the Gains from Trade

Semantics of UML class diagrams

Notes on Complexity Theory Last updated: August, Lecture 1

3515ICT Theory of Computation Turing Machines

United States Naval Academy Electrical and Computer Engineering Department. EC262 Exam 1

Numeracy and mathematics Experiences and outcomes

Grade 7 Mathematics. Unit 2. Integers. Estimated Time: 15 Hours

Numerical Matrix Analysis

Transcription:

September 7, 2005 p. 1 Lecture 1. Basic Concepts of Set Theory, Functions and Relations 0. Preliminaries...1 1. Basic Concepts of Set Theory...1 1.1. Sets and elements...1 1.2. Specification of sets...2 1.3. Identity and cardinality...3 1.4. Subsets...4 1.5. Power sets...4 1.6. Operations on sets: union, intersection...4 1.7 More operations on sets: difference, complement...5 Homework 1 notes...6 0. Preliminaries Aims of the course. Emphasis on math appreciation, aiming to be USEFUL, interesting, may or may not be fun (hope so.). Everything we cover should be doable if you work conscientiously. Creativity is important for really BEING a mathematician, and we ll include some optional problems that ask for creativity, but we ll try to avoid any questions that demand mathematical creativity. Course description: Introduction to some basic mathematical concepts and techniques central to linguistic theory. Set theory, logic and formal systems, modern algebra, automata theory, and model theory. Applications to syntax, phonology, semantics. No prior mathematics assumed. Not open to math majors. Prerequisite: LING 201 or 401 or consent of instructor. [See Syllabus for more.] Textbook: available from instructor, cost $26.00 (Partee s cost direct from publisher with author s discount.) Payment due by next Wednesday, September 14. (Cash or check to Barbara H. Partee.) Sign for book on attendance sheet. Florian or I will give you a receipt when I receive payment. Student questionnaire, to be collected at end of class or next time. Introductions. Florian Schwarz will give some guest lectures when I m away. Reading: Chapter 1 (1.1 1.7) of Partee-ter Meulen-Wall (MMiL), pp.3 17. Also Preliminaries from Partee 1979, Fundamentals of Mathematics for Linguistics (xeroxed). 1. Basic Concepts of Set Theory. 1.1. Sets and elements Set theory is a basis of modern mathematics, and notions of set theory are used in all formal descriptions. The notion of set is taken as undefined, primitive, or basic, so we don t try to define what a set is, but we can give an informal description, describe important properties of sets, and give examples. All other notions of mathematics can be built up based on the notion of set. Similar (but informal) words: collection, group, aggregate. Description: a set is a collection of objects which are called the members or elements of that set. If we have a set we say that some objects belong (or do not belong) to this set, are (or are not) in the set. We say also that sets consist of their elements.

September 7, 2005 p. 2 Examples: the set of students in this room; the English alphabet may be viewed as the set of letters of the English language; the set of natural numbers 1 ; etc. So sets can consist of elements of various natures: people, physical objects, numbers, signs, other sets, etc. (We will use the words object or entity in a very broad way to include all these different kinds of things.) A set is an ABSTRACT object; its members do not have to be physically collected together for them to constitute a set. The membership criteria for a set must in principle be well-defined, and not vague. If we have a set and an object, it is possible that we do not know whether this object belongs to the set or not, because of our lack of information or knowledge. (E.g. The set of students in this room over the age of 21 : a well-defined set but we may not know who is in it.) But the answer should exist, at any rate in principle. It could be unknown, but it should not be vague. If the answer is vague for some collection, we cannot consider that collection as a set. Another thing: If we have a set, then for any two elements of it, x and y, it should not be vague whether x = y, or they are different. (If they are identical, then they are not actually two elements of it; the issue really arises when we have two descriptions of elements, and we want to know whether those descriptions describe the same element, or two different elements.) For example: is the letter q the same thing as the letter Q? Well, it depends on what set we are considering. If we take the set of the 26 letters of the English alphabet, then q and Q are the same element. If we take the set of 52 upper-case and lower-case letters of the English alphabet, then q and Q are two distinct elements. Either is possible, but we have to make it clear what set we are talking about, so that we know whether or not q = Q. Sometimes we simply assume for the sake of examples that a description is not vague when perhaps for other purposes it would be vague e.g., the set of all red objects. Sets can be finite or infinite. There is exactly one set, the empty set, or null set, which has no members at all. A set with only one member is called a singleton or a singleton set. ( Singleton of a ) Notation: A, B, C, for sets; a, b, c, or x, y, z, for members. b A if b belongs to A (B A if both A and B are sets and B is a member of A) and c A, if c doesn t belong to A. is used for the empty set. 1.2. Specification of sets There are three main ways to specify a set: (1) by listing all its members (list notation); (2) by stating a property of its elements (predicate notation); (3) by defining a set of rules which generates (defines) its members (recursive rules). 1 Natural numbers: 0,1,2,3,4,5,.... No notion of positive or negative. The numbers used for counting. Integers: positive, negative, and 0. See xeroxed section Preliminaries from Partee 1979.

September 7, 2005 p. 3 List notation. The first way is suitable only for finite sets. In this case we list names of elements of a set, separate them by commas and enclose them in braces: Examples: {1, 12, 45}, {George Washington, Bill Clinton}, {a,b,d,m}. Three-dot abbreviation : {1,2,..., 100}. (See xeroxed preliminaries, pp xxii-xxiii) {1,2,3,4, } this is not a real list notation, it is not a finite list, but it s common practice as long as the continuation is clear. Note that we do not care about the order of elements of the list, and elements can be listed several times. {1, 12, 45}, {12, 1, 45,1} and {45,12, 45,1} are different representations of the same set (see below the notion of identity of sets). Predicate notation. Example: {x x is a natural number and x < 8} Reading: the set of all x such that x is a natural number and is less than 8 So the second part of this notation is a property the members of the set share (a condition or a predicate which holds for members of this set). Other examples: { x x is a letter of Russian alphabet} {y y is a student of UMass and y is older than 25} General form: { x P(x)}, where P is some predicate (condition, property). The language to describe these predicates is not usually fixed in a strict way. But it is known that unrestricted language can result in paradoxes. Example: { x x x}. ( Russell s paradox ) -- see the historical notes about it on pp 7-8. The moral: not everything that looks on the surface like a predicate can actually be considered to be a good defining condition for a set. Solutions type theory, other solutions; we won t go into them. (If you re interested, see Chapter 8, Sec 2.) Recursive rules. (Always safe.) Example the set E of even numbers greater than 3: a) 4 E b) if x E, then x + 2 E c) nothing else belongs to E. The first rule is the basis of recursion, the second one generates new elements from the elements defined before and the third rule restricts the defined set to the elements generated by rules a and b. (The third rule should always be there; sometimes in practice it is left implicit. It s best when you re a beginner to make it explicit.) 1.3. Identity and cardinality Two sets are identical if and only if 2 they have exactly the same members. So A = B iff for every x, x A x B. For example, {0,2,4} = {x x is an even natural number less than 5} 2 Be careful about if and only if ; its abbreviation is iff. See Preliminaries, p. xxiii.

September 7, 2005 p. 4 From the definition of identity follows that there exists only one empty set; its identity is fully determined by its absence of members. Note that empty list notation {} is not usually used for the empty set, we have a special symbol for it. The number of elements in a set A is called the cardinality of A, written A. The cardinality of a finite set is a natural number. Infinite sets also have cardinalities but they are not natural numbers. We will discuss cardinalities of infinite sets a little later (Chapter 4). 1.4. Subsets A set A is a subset of a set B iff every element of A is also an element of B. Such a relation between sets is denoted by A B. If A B and A B we call A a proper subset of B and write A B. (Caution: sometimes is used the way we are using.) Both signs can be negated using the slash / through the sign. Examples: {a,b} {d,a,b,e} and {a,b} {d,a,b,e}, {a,b} {a,b}, but {a,b} {a,b}. Note that the empty set is a subset of every set. A for every set A. Why? Be careful about the difference between member of and subset of ; the homework gives lots of chance for practice. 1.5. Power sets The set of all subsets of a set A is called the power set of A and denoted as (A) or sometimes as 2 A. For example, if A = {a,b}, (A) = {, {a}, {b}, {a,b}}. From the example above: a A; {a} A; {a} (A) A; A; (A); (A) Be careful with exercise 4 in Homework 1! Pay close attention to the definitions and it should come out all right. But if you don t pay close attention to the definitions, it s easy to make mistakes. Make sure you understand these examples before you try it. (But do try; and if you don t get something right the first time, we give you a chance to redo it.) 1.6. Operations on sets: union, intersection. We define several operations on sets. Let A and B be arbitrary sets. The union of A and B, written A B, is the set whose elements are just the elements of A or B or of both. In the predicate notation the definition is A B = def { x x A or x B} Examples. Let K = {a,b}, L = {c,d} and M = {b,d}, then K L = {a,b,c,d} K M = {a,b,d} L M = {b,c,d}

September 7, 2005 p. 5 (K L) M = K (L M) = {a,b,c,d} K K = K K = K = K = {a,b}. There is a nice method for visually representing sets and set-theoretic operations, called Venn diagrams. Each set is drawn as a circle and its members represented by points within it. The diagrams for two arbitrarily chosen sets are represented as partially intersecting the most general case as in Figure 1 1 p.13. The region designated 1 contains elements which are members of A but not of B; region 2, those members in B but not in A; and region 3, members of both B and A. Points in region 4 outside the diagram represent elements in neither set. The Venn diagram for the union of A and B is shown in Figure 1 2, p.13 The results of operations in this and other diagrams are shown by shading areas. --- The intersection of A and B, written A B, is the set whose elements are just the elements of both A and B. In the predicate notation the definition is A B = def { x x A and x B} Examples: K L = K M = {b} L M = {d} (K L) M = K (L M) = K K = K K = K =. The general case of intersection of arbitrary sets A and B is represented by the Venn diagram of Figure 1 3, p.14. The intersection of three arbitrary sets A,B and C is shown in the Venn diagram of Figure 1 4, p.15 1.7 More operations on sets: difference, complement Another binary operation on arbitrary sets is the difference A minus B, written A B, which subtracts from A all elements which are in B. [Also called relative complement: the complement of B relative to A.] The predicate notation defines this operation as follows: A B = def { x x A and x B} Examples: (using the previous K, L, M) K L = {a,b} K M = {a} L M = {c} K K = K = K K =.

September 7, 2005 p. 6 The Venn diagram for the set-theoretic difference is shown in Figure 1 5, p.16. A B is also called the relative complement of B relative to A. This operation is to be distinguished from the complement of a set A, written A, which is the set consisting of everything not in A. In predicate notation A = def { x x A} It is natural to ask, where do these objects come from which do not belong to A? In this case it is presupposed that there exists a universe of discourse and all other sets are subsets of this set. The universe of discourse is conventionally denoted by the symbol U. Then we have A = def U A The Venn diagram with a shaded section for the complement of A is shown in Figure1 6, p.16 Homework 1 notes. 1. Be careful with problem 4! 2. Strategy for problem 5e, p. 25. In general, when any problem is confusing or difficult, try to break it into smaller steps and SHOW the STEPS. In fact more generally, if it CAN be broken into steps, it usually SHOULD be. So to compute Pow(Pow({a,b})), FIRST compute Pow({a,b}). (It should have 4 members.) Then figure out some systematic way to write down all the subsets of Pow({a,b}), which will be the members of Pow(Pow({a,b})). I think it works best to group them by numbers of members: all the 0-member subsets, all the 1-member subsets, all the 2-member subsets, then 3, then 4. (Hint: there should be 16 in all, including 6 2-member subsets.)