Compact Sets. James K. Peterson. September 15, Department of Biological Sciences and Department of Mathematical Sciences Clemson University

Similar documents
Homework Set #2 Math 440 Topology Topology by J. Munkres

Walheer Barnabé. Topics in Mathematics Practical Session 2 - Topology & Convex

Topology and Topological Spaces

REVIEW OF FUZZY SETS

M3P1/M4P1 (2005) Dr M Ruzhansky Metric and Topological Spaces Summary of the course: definitions, examples, statements.

THREE LECTURES ON BASIC TOPOLOGY. 1. Basic notions.

Notes on point set topology, Fall 2010

Math 5593 Linear Programming Lecture Notes

Open and Closed Sets

Final Test in MAT 410: Introduction to Topology Answers to the Test Questions

EC 521 MATHEMATICAL METHODS FOR ECONOMICS. Lecture 2: Convex Sets

Chapter 4 Concepts from Geometry

Non-context-Free Languages. CS215, Lecture 5 c

Lecture 2. Topology of Sets in R n. August 27, 2008

In this chapter, we define limits of functions and describe some of their properties.

4. Definition: topological space, open set, topology, trivial topology, discrete topology.

1.7 The Heine-Borel Covering Theorem; open sets, compact sets

Bounded subsets of topological vector spaces

Real Analysis, 2nd Edition, G.B.Folland

Lecture 15: The subspace topology, Closed sets

ORIE 6300 Mathematical Programming I September 2, Lecture 3

Division of the Humanities and Social Sciences. Convex Analysis and Economic Theory Winter Separation theorems

Point-Set Topology 1. TOPOLOGICAL SPACES AND CONTINUOUS FUNCTIONS

Numerical Optimization

Lecture 2 September 3

Lecture 19 Subgradient Methods. November 5, 2008

THE GROWTH DEGREE OF VERTEX REPLACEMENT RULES

Lecture 19: Convex Non-Smooth Optimization. April 2, 2007

Lecture 6: Faces, Facets

d(γ(a i 1 ), γ(a i )) i=1

Optimality certificates for convex minimization and Helly numbers

INTRODUCTION TO GRAPH THEORY. 1. Definitions

AXIOMS FOR THE INTEGERS

LECTURE 7 LECTURE OUTLINE. Review of hyperplane separation Nonvertical hyperplanes Convex conjugate functions Conjugacy theorem Examples

Lecture-12: Closed Sets

Convexity and Optimization

DISTRIBUTIVE LATTICES

1 Counting triangles and cliques

A Tour of General Topology Chris Rogers June 29, 2010

A greedy, partially optimal proof of the Heine-Borel Theorem

Finite Math Linear Programming 1 May / 7

Surfaces and Partial Derivatives

COUNTING THE NUMBER OF WINNING BINARY STRINGS IN THE 1-DIMENSIONAL SAME GAME. Department of Mathematics Oberlin College Oberlin OH 44074

Topology 550A Homework 3, Week 3 (Corrections: February 22, 2012)

Convexity and Optimization

ISSN X (print) COMPACTNESS OF S(n)-CLOSED SPACES

H = {(1,0,0,...),(0,1,0,0,...),(0,0,1,0,0,...),...}.

MA651 Topology. Lecture 4. Topological spaces 2

TOPOLOGY, DR. BLOCK, FALL 2015, NOTES, PART 3.

TOPOLOGY CHECKLIST - SPRING 2010

Optimality certificates for convex minimization and Helly numbers

FACES OF CONVEX SETS

On Soft Topological Linear Spaces

Topology Homework 3. Section Section 3.3. Samuel Otten

Topological properties of convex sets

GENERALIZED METRIC SPACES AND TOPOLOGICAL STRUCTURE I

However, this is not always true! For example, this fails if both A and B are closed and unbounded (find an example).

5. THE ISOPERIMETRIC PROBLEM

Lecture 5: Duality Theory

Coloring. Radhika Gupta. Problem 1. What is the chromatic number of the arc graph of a polygonal disc of N sides?

1. Chapter 1, # 1: Prove that for all sets A, B, C, the formula

arxiv: v1 [math.co] 28 Sep 2010

EXTERNAL VISIBILITY. 1. Definitions and notation. The boundary and interior of

Topology - I. Michael Shulman WOMP 2004

Surfaces and Partial Derivatives

BROUWER S FIXED POINT THEOREM. Contents

Lecture 2 - Introduction to Polytopes

or else take their intersection. Now define

ON BINARY TOPOLOGICAL SPACES

Section 26. Compact Sets

Saturated Sets in Fuzzy Topological Spaces

The Set-Open topology

Pebble Sets in Convex Polygons

Algebra of Sets (Mathematics & Logic A)

Introduction to Rational Billiards II. Talk by John Smillie. August 21, 2007

Advanced Operations Research Techniques IE316. Quiz 1 Review. Dr. Ted Ralphs

11.1 Facility Location

Characterization of Boolean Topological Logics

Review of Sets. Review. Philippe B. Laval. Current Semester. Kennesaw State University. Philippe B. Laval (KSU) Sets Current Semester 1 / 16

Theorem 3.1 (Berge) A matching M in G is maximum if and only if there is no M- augmenting path.

Matching Algorithms. Proof. If a bipartite graph has a perfect matching, then it is easy to see that the right hand side is a necessary condition.

Table 1 below illustrates the construction for the case of 11 integers selected from 20.

arxiv: v1 [math.co] 12 Dec 2017

Chapter 2 Topological Spaces and Continuity

AM 221: Advanced Optimization Spring 2016

Bounds on the signed domination number of a graph.

Linear Programming in Small Dimensions

Topology I Test 1 Solutions October 13, 2008

CS 372: Computational Geometry Lecture 10 Linear Programming in Fixed Dimension

Initial Assumptions. Modern Distributed Computing. Network Topology. Initial Input

COMPUTABILITY THEORY AND RECURSIVELY ENUMERABLE SETS

Rigidity, connectivity and graph decompositions

Lecture : Topological Space

9.5 Equivalence Relations

Multiple coverings with closed polygons

On Unbounded Tolerable Solution Sets

Lecture 1. 1 Notation

Exploring Domains of Approximation in R 2 : Expository Essay

A logical view on Tao s finitizations in analysis

15 212: Principles of Programming. Some Notes on Induction

Transcription:

Compact Sets James K. Peterson Department of Biological Sciences and Department of Mathematical Sciences Clemson University September 15, 2017

Outline 1 Closed Sets 2 Compactness 3 Homework

Closed Sets Recall a set S is closed if its complement is open. We can characterize closed sets using the closure too. Theorem Let S be a set of real numbers. Then S is a closed set S = S. ( ): We assume S is a closed set. Then S C is open. We wish to show S = S. Now S contains S already, so we must show all boundary points of S are in S. Let s assume x is a boundary point of S which is in S C. Then, since S C is open, x is an interior point and there is an r > 0 so that B r (x) S C. But B r (x) must contains points of S and S C since x is a boundary point. This is impossible. So our assumption that x was in S C is wrong. Hence, S S. This shows S = S.

Closed Sets ( ): We assume S = S and show S is closed. To do this, we show S C is open. It is easy to see the complement of the complement gives you back the set you started with, so if S C is open, by definition, (S C ) C = S is closed. We will show any x in S C is an interior point. That will show S C is open. Let s do this by contradiction. To show S C is open, we show that for any x in S C, we can find a positive r so that B r (x) S C. So let s assume we can t find such an r. Then every B r (x) contains points of S and points of S C. Hence, x must be a boundary point of S. Since S = S, this means S S which implies x S. But we assumed x was in S C. So our assumption is wrong and we must be able to find an r > 0 with B r (x) S C. Hence S C is open which means S is closed.

Closed Sets Let s look at some set based proofs. Theorem If A and B are open, so is A B. Let p A B. Then p A or p B or both. If p A, p is an interior point because A is open. So there is an r > 0 so that B r (p) A. This says B r (p) A B too. A similar argument works for the case p B. Since p is arbitary, all points in A B are interior points and we have shown A B is open.

Closed Sets Theorem (A B) C = A C B C and (A B) C = A C B C. (A B) C A C B C : If p (A B) C, p A B. Thus p A and p B implying p A C and p B C ; i.e. p A C B C. Since p is arbitary, this shows (A B) C A C B C A C B C (A B) C : If p A C B C, p A C and p B C. So p A and p B. Thus p A B telling us p (A B) C. Since p is arbitrary, this shows A C B C (A B) C. The other one is left for you as a homework.

Closed Sets Let s look at some set based proofs. Theorem If A and B are closed, so is A B. Let s show A B is closed by showing (A B) C is open. Let p (A B) C = A C B C. Then p A C and p B C. So there is a radius r 1 so that B r1 (p) A C and there is a radius r 2 so that B r2 (p) B C. So if r = min{r 1, r 2 }, B r (p) A C B C. Thus, p is an interior point of (A B) C. Since p is arbitary, all points in (A B) C are interior points and we have shown (A B) C is open.

Compactness Definition Let S be a set of real numbers. We say S is sequentially compact or simply compact if every sequence (x n ) in S has at least one subsequence which converges to an element of S. In other words Given (x n ) S, (x nk ) (x n ) and an x S so that x nk x. Theorem A set S is sequentially compact S is closed and bounded. ( ): We assume S is sequentially compact and we show S is both closed and bounded. First, we show S is closed. Let x S. If we show S = S, by the previous theorem, we know S is closed. Since S S, this means we have to show all x S are also in S.

Compactness Now if x S, x is a boundary point of S. Hence, for all B 1/n (x), there is a point x n S and a point y n S C. This gives a sequence (x n ) satisfying x n x < 1/n for all n which tells us x n x. If this sequence is the constant sequence, x n = x, then we have x S. But if (x n ) satisfies x n x for all n, we have to argue differently. In this case, since S is sequentially compact, (x n ) has a subsequence (x nk ) which converges to some y S. Since limits of a sequence are unique, this says x = y. Since y is in S, this shows x is in S too. Hence, since x was arbitrarily chosen, we have S S and so S = S and S is closed.

Compactness Next, we show S is bounded by contradiction. Let s assume it is not bounded. Then given any positive integer n, there is an x n in S so that x n > n. This defines a subsequence (x n ) in S. Since S is sequentially compact, (x n ) has a convergent subsequence (x nk ) which converges to an element y in S. But since this subsequence converges, this tells us (x nk ) is bounded; i.e. there is a positive number B so that x nk B for all n k. But we also know x nk > n k and so n k < x nk B for all n k. This is impossible as n k and B is a finite number. Thus, our assumption that S was unbounded is wrong and so S must be bounded.

Compactness ( ): We assume S is closed and bounded and we want to show S is sequentially compact. Let (x n ) be any sequence in S. Since S is a bounded set, by the Bolzano Weierstrass Theorem for Sequences, since (x n ) is bounded, there is at least one subsequence (x nk ) of (x n ) which converges to a value y. If this sequence is a constant sequence, then x nk = y always which tells us y is in S too. On the other hand, if it is not a constant sequence, this tells us y is an accumulation point of S. Now if y was not in S, then y would be in S C and then since x nk y, every B r (y) would have to contain points of S and points of S C. This says y has to be a boundary point. But since S is closed, S contains all its boundary points. Thus the assumption y S C can t be right and we know y S. This shows the arbitrary sequence (x n ) in S has a subsequence which converges to an element of S which shows S is sequentially compact.

Compactness Example Let S = {2} (3, 4). Note S is not closed as 3 and 4 are boundary points not in S. Hence, S is not sequentially compact either. Example Let S = {2} [5, 7]. Then S is closed as it contains all of its boundary points and so S is also sequentially compact. Example Let S = [1, ). Then S is not bounded so it is not sequentially compact.

Compactness Theorem If S is sequentially compact, then any sequence (x n ) in S which converges, converges to a point in S. Since S is sequentially compact, such a sequence does have a convergent subsequence which converges to a point in S. Since the limit of the subsequence must be the same as the limit of this convergent sequence, this tells us the limit of the sequence must be in S.

Compactness Example Let S be a nonempty and bounded set of numbers. Then α = inf(s) and β = sup(s) are both finite numbers. Given any r n = 1/n, the Supremum Tolerance Lemma tells us there is a x n S so that β 1/n < x n β for all n. Thus, rearranging this inequality, we have 1/n < x n β 0 < 1/n which says x n β < 1/n for all n. This tells us x n β. We can do a similar argument for the infimum α and so there is a sequence (y n ) in S so that y n α. Now in general, we don t know if α or β are the minimum and maximum of S, respectively. But if we also knew S was sequentially compact, we can say more. By the Theorem above, since x n β and (x n ) S, we know β S. Hence β is a maximum. A similar argument shows α is a minimum.

Compactness Definition Let S be a nonempty and bounded set of numbers. Then α = inf(s) and β = sup(s) are both finite numbers. A sequence (y n ) S which converges to inf(s) is called a minimizing sequence and a sequence (x n ) S which converges to sup(s) is called a maximizing sequence. What we want are conditions that force minimizing and maximizing sequences to converge to points inside S; i.e. make sure the minimum and maximum of S exist. One way to make this happen is to prove S is sequentially compact.

Homework Homework 10 10.1 Let S = {3} (0, 3). Explain why S is or is not sequentially compact. 10.2 Let S = (, 3]. Explain why S is or is not sequentially compact. 10.3 Let S = (x n ) = (sin(n)) n 1. Since (x n ) [ 1, 1], why do you know that (x n ) has a subsequence which converges to a point x in [ 1, 1]? Can you see why it is hard to decide if S is a closed set? This question is just to show you deciding if a set is closed or open can be difficult. 10.4 Prove for any sets A and B, (A B) C = A C B C. 10.5 If A and B are open, prove A B is open also.