Andrew McLennan January 19, Winter Lecture 5. A. Two of the most fundamental notions of the dierential calculus (recall that

Similar documents
Notes on metric spaces

Metric Spaces Joseph Muscat 2003 (Last revised May 2009)

Metric Spaces. Chapter Metrics

1. Prove that the empty set is a subset of every set.

THE BANACH CONTRACTION PRINCIPLE. Contents

Metric Spaces. Chapter 1

SOLUTIONS TO EXERCISES FOR. MATHEMATICS 205A Part 3. Spaces with special properties

1 if 1 x 0 1 if 0 x 1

Introduction to Topology

Basic Concepts of Point Set Topology Notes for OU course Math 4853 Spring 2011

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 5 9/17/2008 RANDOM VARIABLES

MA651 Topology. Lecture 6. Separation Axioms.

Mathematical Methods of Engineering Analysis

CHAPTER II THE LIMIT OF A SEQUENCE OF NUMBERS DEFINITION OF THE NUMBER e.

Follow links for Class Use and other Permissions. For more information send to:

x a x 2 (1 + x 2 ) n.

Math 4310 Handout - Quotient Vector Spaces

Fixed Point Theorems

No: Bilkent University. Monotonic Extension. Farhad Husseinov. Discussion Papers. Department of Economics

Some other convex-valued selection theorems 147 (2) Every lower semicontinuous mapping F : X! IR such that for every x 2 X, F (x) is either convex and

BANACH AND HILBERT SPACE REVIEW

Math 104: Introduction to Analysis

Chapter 7. Continuity

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

CHAPTER 1 BASIC TOPOLOGY

Georg Cantor ( ):

This chapter is all about cardinality of sets. At first this looks like a

n k=1 k=0 1/k! = e. Example 6.4. The series 1/k 2 converges in R. Indeed, if s n = n then k=1 1/k, then s 2n s n = 1 n

Continued Fractions and the Euclidean Algorithm

Cartesian Products and Relations

God created the integers and the rest is the work of man. (Leopold Kronecker, in an after-dinner speech at a conference, Berlin, 1886)

10.2 Series and Convergence

MEASURE AND INTEGRATION. Dietmar A. Salamon ETH Zürich

1 Norms and Vector Spaces

Mathematical Induction. Mary Barnes Sue Gordon

INTRODUCTORY SET THEORY

I. GROUPS: BASIC DEFINITIONS AND EXAMPLES

LECTURE NOTES IN MEASURE THEORY. Christer Borell Matematik Chalmers och Göteborgs universitet Göteborg (Version: January 12)

Undergraduate Notes in Mathematics. Arkansas Tech University Department of Mathematics

3. Mathematical Induction

ON SEQUENTIAL CONTINUITY OF COMPOSITION MAPPING. 0. Introduction

Separation Properties for Locally Convex Cones

The Banach-Tarski Paradox

Representation of functions as power series

Mathematics for Econometrics, Fourth Edition

4. Expanding dynamical systems

An example of a computable

Advanced Microeconomics

Cardinality. The set of all finite strings over the alphabet of lowercase letters is countable. The set of real numbers R is an uncountable set.

Chapter 3. Cartesian Products and Relations. 3.1 Cartesian Products

INCIDENCE-BETWEENNESS GEOMETRY

Finite dimensional topological vector spaces

About the inverse football pool problem for 9 games 1

LEARNING OBJECTIVES FOR THIS CHAPTER

INTRODUCTION TO TOPOLOGY

ALMOST COMMON PRIORS 1. INTRODUCTION

E3: PROBABILITY AND STATISTICS lecture notes

Practice with Proofs

CS 3719 (Theory of Computation and Algorithms) Lecture 4

PSEUDOARCS, PSEUDOCIRCLES, LAKES OF WADA AND GENERIC MAPS ON S 2

HOMEWORK 5 SOLUTIONS. n!f n (1) lim. ln x n! + xn x. 1 = G n 1 (x). (2) k + 1 n. (n 1)!

3. INNER PRODUCT SPACES

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

MATH 4330/5330, Fourier Analysis Section 11, The Discrete Fourier Transform

Chapter 3. Distribution Problems. 3.1 The idea of a distribution The twenty-fold way

WRITING PROOFS. Christopher Heil Georgia Institute of Technology

FUNCTIONAL ANALYSIS LECTURE NOTES: QUOTIENT SPACES

Lecture 16 : Relations and Functions DRAFT

Chapter 12 Modal Decomposition of State-Space Models 12.1 Introduction The solutions obtained in previous chapters, whether in time domain or transfor

How To Find Out How To Calculate A Premeasure On A Set Of Two-Dimensional Algebra

1. Let X and Y be normed spaces and let T B(X, Y ).

SOLUTIONS TO ASSIGNMENT 1 MATH 576

Set theory as a foundation for mathematics

BALANCED LOADS IN INFINITE NETWORKS. By Bruce Hajek. University of Illinois at Urbana-Champaign

0 <β 1 let u(x) u(y) kuk u := sup u(x) and [u] β := sup

8 Divisibility and prime numbers

SECTION 10-2 Mathematical Induction

Tiers, Preference Similarity, and the Limits on Stable Partners

Mathematical Induction

Section 1.3 P 1 = 1 2. = P n = 1 P 3 = Continuing in this fashion, it should seem reasonable that, for any n = 1, 2, 3,..., =

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

Lecture 2: Universality

Linear Algebra I. Ronald van Luijk, 2012

Logic, Algebra and Truth Degrees Siena. A characterization of rst order rational Pavelka's logic

Inner Product Spaces

Mathematics Review for MS Finance Students

The Henstock-Kurzweil-Stieltjes type integral for real functions on a fractal subset of the real line

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

FIRST YEAR CALCULUS. Chapter 7 CONTINUITY. It is a parabola, and we can draw this parabola without lifting our pencil from the paper.

Full and Complete Binary Trees

Point Set Topology. A. Topological Spaces and Continuous Maps

Quotient Rings and Field Extensions

CONTRIBUTIONS TO ZERO SUM PROBLEMS

CHAPTER 5. Number Theory. 1. Integers and Division. Discussion

INDISTINGUISHABILITY OF ABSOLUTELY CONTINUOUS AND SINGULAR DISTRIBUTIONS

Real Roots of Univariate Polynomials with Real Coefficients

4: EIGENVALUES, EIGENVECTORS, DIAGONALIZATION

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

Taylor and Maclaurin Series

SCORE SETS IN ORIENTED GRAPHS

Transcription:

Andrew McLennan January 19, 1999 Economics 5113 Introduction to Mathematical Economics Winter 1999 Lecture 5 Convergence, Continuity, Compactness I. Introduction A. Two of the most fundamental notions of the dierential calculus (recall that Leibniz and Newton are credited with being the creators of this) `limit' and `continuity,' were not successfully described with formal precision until the nineteenth century. 1. After a period of evolution, the mathematics profession settled on two principle frameworks, metric spaces and topological spaces, as the arenas in which these concepts are dened and discussed. 2. Let X be a set. A function d : X X! IR + is a metric if: a. for all x; y 2 X, d(x; y) = 0 if and only if x = y; b. for all x; y 2 X, d(x; y) =d(y; x); c. for all x; y; z 2 X, d(x; z) d(x; y) +d(y; z). A metric space is a pair (X; d) where X is a set and d is a metric on X. 3. The most fundamental geometric objects in X are the open balls: for x 2 X and >0, let B (x) =fx 0 2X:d(x; x 0 ) <g: 1

II. Sequences and Convergence A. Formally,asequence inxis a function from f1; 2; 3;:::g, or some similar index set, tox. 1. Informally, a sequence is akin to a set, and we denote it by fx n g (or sometimes x 1 ;x 2 ; :::) suggesting that we are concerned less with the particular ordering and more with the general tendency as n gets large. B. TRoughly speaking, the sequence fx n g converges to the limit x 2 X if it is eventually inside each B (x). 1. In more detail, for any >0 there is an integer N such that d(x n ;x) < for all n N. 2. The notation x n! x indicates that the sequence fx n g converges to x. 3. A sequence that converges to something is said to be convergent. Sequences that are not convergent are said to be divergent. 4. There are two ways that a sequence might be divergent: a. The sequence might just bounce around without ever settling down. This happens when, for some >0, it is possible, for any integer N, tondn; m N, such that d(x n ;x m ). b. A Cauchy sequence is a sequence such that for any >0there is an integer N such that d(x n ;x m ) <for all n; m N, i.e., a sequence that does not bounce around too much. c. If a Cauchy sequence fails to converge, the usual view is that this is not because the sequence is in any sense ill behaved. Instead, the fault lies with the given space. A metric space is said to be complete if every Cauchy sequence has a limit. C. Sequences in IR. 1. The basic reason that the real numbers are more useful than the rational numbers is as follows: 2

Theorem: IR is complete. Proof: Let ft n g be a Cauchy sequence in IR. Let S be the set of real numbers S such that t n >sfor all but at most nitely many n. It is straightforward to use the denition of a Cauchy sequence to show that S is nonempty and bounded above. Let s be the least uppoer bound of S. We claim that t n! s. In order to prove this by contradiction we suppose that it is not the case, which means that there is some >0 such that jt n, sj > for innitely many n. Suppose N is large enough that jt m, t n j <=2 for all n; m N, and choose M N such that jt M, sj >. Then jt n, t M j <=2 for all n M, soif t M >s+=2, then s + =2 is an element ofs, while if t M <s,=2, then s, =2 is an upper bound. In either case we have contradicted the assumption that s is the least uppoer bound of S. 2 It turns out that any metric space X can be \completed" by adding \the missing points." The formal procedure for doing this is to let X be the set of equivalence classes of Cauchy sequences in X, where two Cauchy sequences are equivalent if the distance between them goes toi zero in the limit. The details (dening the natural metric on X and showing that X is complete) are lengthy, so I will not discuss them, but that is not to say that it would be bad for you to think carefully about what is involved. 3 A sequence ft k )g in IR is increasing if t k t k+1 for all k, and it is strictly increasing if t k <t k+1 for all k. The sequence ft k g is bounded above if there is some t such that t k t for all k. Theorem: An increasing sequence ft k )g that is bounded above has a limit. Proof: We leave this as an exercise, since the ideas are very similar to those used to prove the last result. C. Sequences in IR n. 1. Unless we explicitly say otherwise, IR n is always endowed with the Eu- 3

clidean metric: d(x; y) = kx,yk. 2. Basically all you need to know about convergence in IR n is: Theorem: A sequence fx k =(x 1 k ;:::;xn k )gin IRn converges if and only if each component sequence fx i kg is convergent. Proof: If the sequence converges, say to x, then each component sequence converges because, for all i =1;:::n,jx i,x i k jkx,x kk. Conversely, suppose that each component sequence fx i k g converges to xi, and dene x to be the point (x 1 ;:::;x n ). For any y 2 IR n we have Consequently kyk 2 =jy 1 j 2 +:::+jy n j 2 (jy 1 j + :::+jy n j) 2 : kx k, xk jx 1 k,x 1 j+:::+jx n k,x 1 j!0: III. Open and Closed Sets A. A set C X is closed (or closed inxif some other containing space is possible) if it contains all its limit points. That is, whenever fx k g is a sequence in C that converges to some point x, x is an element ofc. 1. Example: IR n + is closed. B. A neighborhood of a point x 2 X is any set S X that contains B (x) for some >0. A set U X is open (or open in X) if it is a neighborhood of each of its points, so tha t for each x 2 U there is >0 for which B (x) U. 1. Example: IR n ++ is open. Theorem: For any x 2 X and >0, B (x) is open. Proof: For any y 2 B (x), the triangle inequality implies that B (,d(x;y)) (y) B (x). 3. Exercise: Prove that, for any x and, f y 2 X : d(x; y) >gis open. Theorem: A set U X is open if and only if its complement U c = XnU is closed. 4

Proof: Let C = U c. The assertion consists of two implications, the `if' and the `only if.' Suppose that C is closed, and x 2 U. If, for each natural number k, B 1=k (x) 6 U, we can choose a point inb 1=k (x) \ C, thereby constructing a sequence fx k g in C that converges to x. Since C is closed, this w ould imply that x 2 C, contrary to our assumption that x 2 U. Therefore B 1=k (x) U for large k, and since x was an arbitrary point ofu, wehave shown that U is open. Suppose that U is open, and that fx k g is a sequence in C that converges to x. If x 2 U, then B (x) U for some >0, and x k 2 B (x) for large k since x k! x, but this contradicts the a ssumption that x k 2 C. Thus C is closed. C. A topological space is a pair (T;) in which T is a set and is a collection of subsets of T, called the open sets of T, with the properties of the open subsets of X given by: Theorem: (a) ; and X are open sets. (b) The intersection of nitely many open sets is open. (c) The union of an arbitrary collection of open sets is open. Proof: This is all pretty obvious, so we will only mention that (b) is proved by noting that if U 1 ;:::;U p are open and B 1 (x) U 1 ;:::;B p (x) U p, then B minf1 ;::: p g(x) U 1 \ :::\U p : 1. Exercise: In a topological space a closed set is by denition a set whose complement is open. The collection of all closed subsets of T has properties that are similar to, and immediately derivable from, (a){(c). What are they? 2. The theory of topological spaces is much more complicated than the theory of metric spaces, essentially because any number of things can 5

go wrong. For starters, in a topological space there can be sets that contain all the limits of their convergent sequences, but are nonetheless not closed. 3. In the future, at least, I will try to be careful to give denitions that are valid for all topological spaces, not just metric spaces, and to be careful to indicate what properties of metric spaces might not be true more generally. However, we are basically going to just forget about general topological spaces. IV. Continuity A. Let (X; d X ) and (Y;d Y ) be metric spaces, and let f : X! Y be a function. 1. The function f is continuous if f,1 (V ):=fx2x:f(x)2vgis open whenever V Y is open. 2. This denition makes sense, and is correct, for general topological spaces, but has the unfortunate aspect of seeming strange on rst sight. Note, however, that if f is continuous, x 2 X, and >0, then f,1 (B (f(x))) is open, so that there exists some >0 such that B (x) f,1 (B (f(x))). 3. Exercise: The reverse implication also holds: (8x 2 X)(8 >0)(9 >0) B (x) f,1 (B (f(x))) implies that f is continuous. Prove this. B. The following test of continuity for functions between metric spaces is very useful. Theorem: f is continuous if and only if f(x n )! f(x) whenever fx n g is a sequence in X that converges to x. Proof: Suppose f is continuous. Let fx n g be a sequence in X with x n! x. For any > 0, f,1 (B (f(x))) is open and contains x, so there is some > 0 such that 6

B (x) f,1 (B (f(x))). Since x n! x, for all suciently large n we havex n 2B (x) and f(x n ) 2 B (f(x)). Since was arbitrary, wehave shown that f(x n )! f(x). Suppose that f(x n )! f(x) whenever x n! x. Let V Y be open. If f,1 (V )is not open, it must contain a point x such that for each natural number n we can choose x n 2 B 1=n (x) n f,1 (V ). Then x n! x. But V is open, so there is > 0 such that B (f(x)) V. For each n, f(x n ) =2 V, so that d(f(x n );f(x)) >and f(x n ) 6! f(x), contrary to assumption. Therefore f,1 (V ) is open, and since V was arbitrary, wehave shown that f is continuous. V. Compactness A. The concept of a compact set, developed in the rst part of this century, is now applied in most aspects of mathematics. B. The denition is far from intuitive. Let (X; d) be a metric space. 1. An open cover of a set C X is a collection fu g 2A of open sets that \covers" C in the sense that C 0 [ 2A U. 2. A subcover is a subset of fu g 2A that also covers C. 3 This set C is compact if every open cover has a nite subcover. C. It is easy to explain why such sets might be attractive from the point of view of optimization. Theorem: If f: X! IR is continuous, and C X is compact, then f attains its supremum on C (that is, arg max x2c f(x) is nonempty). Proof: For each x 2 C let U x = fy 2 X: f(y) <f(x)g=f,1 (,1;f(x)) Since f is continuous, U x is open. If f does not attain its maximum on C, then every point ofcis in some U x so fu x g x2c is a cover of C. Let U x1 ;:::;U xk be a nite subcover. 7

Reordering if we need to, suppose that f(x 1 ) f(x 2 ) f(x k ). Then U x1 U x2 U xk,sowehave CU x1 [[U xk = U xk : Since x k 2 C, this would yield f(x k ) <f(x k ). This contradiction completes the proof. D. What kinds of sets are compact? 1. Finite sets are obviously compact. Other examples are not so obvious. 2. A set C X is bounded if, for any x 2 C, there is M > 0 such that d(x; x 0 ) M for all x 0 2 C. Lemma: If C X is compact, it is bounded. Proof: For any x 2 C, the sets U k = fx 2 C: d(x; y) <kg (k=1;2;:::) constitute an open cover of C, and must have a nite subcover. Lemma: If C X is compact, it is closed. Proof: Suppose fx n g is a sequence in C that converges to x. Ifx=2C, the sets U k = y 2 C: d(x; y) > 1 (k =1;2;:::) k constitute an open cover of C which cannot have a nite subcover if there is a sequence in C converging to x. 3. A subsequence of a sequence fx n g is a sequence x n1 ;x n2 ;::: where n 1 < n 2 <. 4. An accumulation point of a sequence fx n g is a point x with the property that, for any >0, there are innitely many n such that x n 2 B (x). 8

a. Exercise: Prove that a sequence fx n g has a convergent subsequence if and only if it has an accumulation point. 5. A set C X is sequentially compact is every sequence in C has a convergent subsequence whose limit is in C. Theorem: A compact set C X is sequentially compact. Proof: By the exercise, it suces to show that a given sequence fx k g in C has an accumulation point. If not, for each x it is possible to nd an open set U x that contains x k for at most nitely many k. Since x 2 U x, for each x, fu x : x 2 Cg is an open cover of C. But the union of the elements of a nite subcover contains C, and contains x k for at most nitely many k. (The sum of nitely many nite numbers is nite.) Since this is impossible, the proof is complete. 6. The converse is also true for metric spaces (we will prove this shortly) but not for general topological spaces. E. Compact Subsets of Euclidean Space. Lemma: If fx k g is a bounded sequence in IR n, it has a convergent subsequence. Proof: We claim that it suces to prove this in the case n =1.Ifitisknown to be true for n = 1, then, in the general case, we can choose a subsequence such that the sequence of rst components is convergent, take a further subsequence of this subsequence for which the sequence of record components converges, and so on until, in the nth sequence, all sequences of components are convergent. From an earlier exercise we know that this is sucient for convergence. So, let ft k g be a bounded sequence in IR, with lower bound a 0 and upper bound b 0. We construct a sequence [a 1 ;b 1 ];[a 2 ;b 2 ];::: of closed intervals \inductively" by letting [a i ;b i ]= a i,1 ; a i,1+b i,1 2 9

if the right hand side contains t k for innitely many k. Otherwise we set [a i ;b i ]= ai,1 +b i,1 ;b i : 2 Arguing by induction, we can easily see that each [a i ;b i ] contains t k for innitely many k, since this is true for [a i,1 ;b i,1 ]. Now choose k 1 such that t k1 2 [a 1 ;b 1 ]. We construct fk i g inductively by choosing k i >k i,1 with t k 2 [a i ;b i ]. Since t ki ;t ki+1 ;::: are all contained in [a i ;b i ], which has length (b 0,a 0 ) 2 i, ft ki g is a Cauchy sequence, hence convergent since IR is complete. 1. All that remains is to bundle our ndings in a nice neat package. Theorem: A set C IR n is compact if and only if it is closed and bounded. Proof: We have already shown that a compact C is closed and bounded. Suppose that C is closed and bounded. Consider that any sequence in C has a convergent subsequence, by the limit, and the limit must be in C since C is closed. Thus C is sequentially compact, so it is compact since IR n is a metric space. V. Countable and Uncountable Sets A. Two sets X and Y have the same cardinality if there is a bijection f : X! Y. B. A set is said to be countable if it has the same cardinality as the natural numbers N := f1; 2; 3;:::g. 1. Some authors say that a set is countable if it is either nite or has the same cardinality asn, using the phrase \countably innite" to describe a set with exactly the same cardinality asn. 2. A set that is neither nite nor countable is said to be uncountable. C. Properties of Countable Sets. 1. Any innite subset of a countable set is countable. (Pf: If Y X = fx 1 ;x 2 ;:::g with Y innite, we map Y bijectively to N by letting f(x n ) be the number of i n such that x i 2 Y.) 10

2. The cartesian product of two countable sets is countable. 3. Examples: a. The rational numbers are countable since they can be put in one to one correspondence with a subset of N N. b. The real numbers are not countable. This is proved by producing a contradiction using Cantor's diagonalization procedure: if r 1 ;r 2 ;::: is an enumeration of the reals, create a number between 0 and 1 by choosing a rst digit (after the decimal point) that is dierent from the rst digit of r 1, a second digit dierent from the second digit of r 2, and so forth. All chosen digits can be dierent from 0 and 9, to avoid ambiguity about numbers such as 1 that may be written either 1:000 ::: of 0:999 :::.) The number constructed in this way is dierent from any number in the list. D. Let fu g 2A be a collection of open sets. Another collection of open sets fv g 2B is a renement of fu g 2A if S 2B V = S 2A U and, for each 2 B, there is some 2 A such that V U. Lemma: Any collection of fu g 2A of open subsets of IR n has a countable renement. Proof: Let B be the set of pairs (x; r) 2 IR n (0; 1) such that r and all components of x are rational numbers, and V (x;r) := B r (x) U for some. Then B is countable, by virtue of our remarks above, and we only need to show that S (x;r)2b V (x;r) = S 2A U. Choose 2 A and y 2 U arbitrarily. Then B (y) U for some >0. Choose a point x with rational coordinates in B =2 (y), and let r be a rational number between kx, yk and,kx,yk. Then y 2 B r (x) B (y) U : Lemma: If fv g 2B is a renement offu g 2A, and for some set S, nitely many elements of fv g 2B cover S, then there is a nite subcover of fu g 2A that covers S. 11

Proof: If V 1 ;:::;V K covers S, and for each k =1;:::;K, V K U k, then U 1 ;:::;U K covers S. Theorem: If C IR n is sequentially compact, then it is compact. Proof: Let fu g 2A be an open cover of C. This cover has a countable renement, and it suces to nd a nite subcover of the renement, which means that we only need to prove the result when A is countable, so we may assume that A = N. That is, we assume that the open cover is of the form fu 1 ;U 2 ;:::g. If there is no nite subcover, then for each k =1;2;::: wemaychoose x k 2 C n (U 1 [ :::[U k,1 ): By assumption fx k g 1 k=1 has a convergent subsequence whose limit x is in C. There is some K such that x 2 U K, and there is some >0 such that B (x) U K,sokx k,xkfor all but nitely many k, which is impossible if fx k g 1 k=1 has a subsequence converging to x. This contradiction completes the proof. 12