METRIC SPACES and SOME BASIC TOPOLOGY
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Interval Notation and Linear Inequalities
CHAPTER 1 Introductory Information and Review Section 1.7: Interval Notation and Linear Inequalities Linear Inequalities Linear Inequalities Rules for Solving Inequalities: 86 University of Houston Department of Mathematics SECTION 1.7 Interval Notation and Linear Inequalities Interval Notation: Example: Solution: MATH 1300 Fundamentals of Mathematics 87 CHAPTER 1 Introductory Information and Review Example: Solution: Example: 88 University of Houston Department of Mathematics SECTION 1.7 Interval Notation and Linear Inequalities Solution: Additional Example 1: Solution: MATH 1300 Fundamentals of Mathematics 89 CHAPTER 1 Introductory Information and Review Additional Example 2: Solution: 90 University of Houston Department of Mathematics SECTION 1.7 Interval Notation and Linear Inequalities Additional Example 3: Solution: Additional Example 4: Solution: MATH 1300 Fundamentals of Mathematics 91 CHAPTER 1 Introductory Information and Review Additional Example 5: Solution: Additional Example 6: Solution: 92 University of Houston Department of Mathematics SECTION 1.7 Interval Notation and Linear Inequalities Additional Example 7: Solution: MATH 1300 Fundamentals of Mathematics 93 Exercise Set 1.7: Interval Notation and Linear Inequalities For each of the following inequalities: Write each of the following inequalities in interval (a) Write the inequality algebraically. notation. (b) Graph the inequality on the real number line. (c) Write the inequality in interval notation. 23. 1. x is greater than 5. 2. x is less than 4. 24. 3. x is less than or equal to 3. 4. x is greater than or equal to 7. 25. 5. x is not equal to 2. 6. x is not equal to 5 . 26. 7. x is less than 1. 8. -
Interval Computations: Introduction, Uses, and Resources
Interval Computations: Introduction, Uses, and Resources R. B. Kearfott Department of Mathematics University of Southwestern Louisiana U.S.L. Box 4-1010, Lafayette, LA 70504-1010 USA email: [email protected] Abstract Interval analysis is a broad field in which rigorous mathematics is as- sociated with with scientific computing. A number of researchers world- wide have produced a voluminous literature on the subject. This article introduces interval arithmetic and its interaction with established math- ematical theory. The article provides pointers to traditional literature collections, as well as electronic resources. Some successful scientific and engineering applications are listed. 1 What is Interval Arithmetic, and Why is it Considered? Interval arithmetic is an arithmetic defined on sets of intervals, rather than sets of real numbers. A form of interval arithmetic perhaps first appeared in 1924 and 1931 in [8, 104], then later in [98]. Modern development of interval arithmetic began with R. E. Moore’s dissertation [64]. Since then, thousands of research articles and numerous books have appeared on the subject. Periodic conferences, as well as special meetings, are held on the subject. There is an increasing amount of software support for interval computations, and more resources concerning interval computations are becoming available through the Internet. In this paper, boldface will denote intervals, lower case will denote scalar quantities, and upper case will denote vectors and matrices. Brackets “[ ]” will delimit intervals while parentheses “( )” will delimit vectors and matrices.· Un- derscores will denote lower bounds of· intervals and overscores will denote upper bounds of intervals. Corresponding lower case letters will denote components of vectors. -
Chapter 7 Separation Properties
Chapter VII Separation Axioms 1. Introduction “Separation” refers here to whether or not objects like points or disjoint closed sets can be enclosed in disjoint open sets; “separation properties” have nothing to do with the idea of “separated sets” that appeared in our discussion of connectedness in Chapter 5 in spite of the similarity of terminology.. We have already met some simple separation properties of spaces: the XßX!"and X # (Hausdorff) properties. In this chapter, we look at these and others in more depth. As “more separation” is added to spaces, they generally become nicer and nicer especially when “separation” is combined with other properties. For example, we will see that “enough separation” and “a nice base” guarantees that a space is metrizable. “Separation axioms” translates the German term Trennungsaxiome used in the older literature. Therefore the standard separation axioms were historically named XXXX!"#$, , , , and X %, each stronger than its predecessors in the list. Once these were common terminology, another separation axiom was discovered to be useful and “interpolated” into the list: XÞ"" It turns out that the X spaces (also called $$## Tychonoff spaces) are an extremely well-behaved class of spaces with some very nice properties. 2. The Basics Definition 2.1 A topological space \ is called a 1) X! space if, whenever BÁC−\, there either exists an open set Y with B−Y, CÂY or there exists an open set ZC−ZBÂZwith , 2) X" space if, whenever BÁC−\, there exists an open set Ywith B−YßCÂZ and there exists an open set ZBÂYßC−Zwith 3) XBÁC−\Y# space (or, Hausdorff space) if, whenever , there exist disjoint open sets and Z\ in such that B−YC−Z and . -
General Topology
General Topology Tom Leinster 2014{15 Contents A Topological spaces2 A1 Review of metric spaces.......................2 A2 The definition of topological space.................8 A3 Metrics versus topologies....................... 13 A4 Continuous maps........................... 17 A5 When are two spaces homeomorphic?................ 22 A6 Topological properties........................ 26 A7 Bases................................. 28 A8 Closure and interior......................... 31 A9 Subspaces (new spaces from old, 1)................. 35 A10 Products (new spaces from old, 2)................. 39 A11 Quotients (new spaces from old, 3)................. 43 A12 Review of ChapterA......................... 48 B Compactness 51 B1 The definition of compactness.................... 51 B2 Closed bounded intervals are compact............... 55 B3 Compactness and subspaces..................... 56 B4 Compactness and products..................... 58 B5 The compact subsets of Rn ..................... 59 B6 Compactness and quotients (and images)............. 61 B7 Compact metric spaces........................ 64 C Connectedness 68 C1 The definition of connectedness................... 68 C2 Connected subsets of the real line.................. 72 C3 Path-connectedness.......................... 76 C4 Connected-components and path-components........... 80 1 Chapter A Topological spaces A1 Review of metric spaces For the lecture of Thursday, 18 September 2014 Almost everything in this section should have been covered in Honours Analysis, with the possible exception of some of the examples. For that reason, this lecture is longer than usual. Definition A1.1 Let X be a set. A metric on X is a function d: X × X ! [0; 1) with the following three properties: • d(x; y) = 0 () x = y, for x; y 2 X; • d(x; y) + d(y; z) ≥ d(x; z) for all x; y; z 2 X (triangle inequality); • d(x; y) = d(y; x) for all x; y 2 X (symmetry). -
Multidisciplinary Design Project Engineering Dictionary Version 0.0.2
Multidisciplinary Design Project Engineering Dictionary Version 0.0.2 February 15, 2006 . DRAFT Cambridge-MIT Institute Multidisciplinary Design Project This Dictionary/Glossary of Engineering terms has been compiled to compliment the work developed as part of the Multi-disciplinary Design Project (MDP), which is a programme to develop teaching material and kits to aid the running of mechtronics projects in Universities and Schools. The project is being carried out with support from the Cambridge-MIT Institute undergraduate teaching programe. For more information about the project please visit the MDP website at http://www-mdp.eng.cam.ac.uk or contact Dr. Peter Long Prof. Alex Slocum Cambridge University Engineering Department Massachusetts Institute of Technology Trumpington Street, 77 Massachusetts Ave. Cambridge. Cambridge MA 02139-4307 CB2 1PZ. USA e-mail: [email protected] e-mail: [email protected] tel: +44 (0) 1223 332779 tel: +1 617 253 0012 For information about the CMI initiative please see Cambridge-MIT Institute website :- http://www.cambridge-mit.org CMI CMI, University of Cambridge Massachusetts Institute of Technology 10 Miller’s Yard, 77 Massachusetts Ave. Mill Lane, Cambridge MA 02139-4307 Cambridge. CB2 1RQ. USA tel: +44 (0) 1223 327207 tel. +1 617 253 7732 fax: +44 (0) 1223 765891 fax. +1 617 258 8539 . DRAFT 2 CMI-MDP Programme 1 Introduction This dictionary/glossary has not been developed as a definative work but as a useful reference book for engi- neering students to search when looking for the meaning of a word/phrase. It has been compiled from a number of existing glossaries together with a number of local additions. -
Calculus Terminology
AP Calculus BC Calculus Terminology Absolute Convergence Asymptote Continued Sum Absolute Maximum Average Rate of Change Continuous Function Absolute Minimum Average Value of a Function Continuously Differentiable Function Absolutely Convergent Axis of Rotation Converge Acceleration Boundary Value Problem Converge Absolutely Alternating Series Bounded Function Converge Conditionally Alternating Series Remainder Bounded Sequence Convergence Tests Alternating Series Test Bounds of Integration Convergent Sequence Analytic Methods Calculus Convergent Series Annulus Cartesian Form Critical Number Antiderivative of a Function Cavalieri’s Principle Critical Point Approximation by Differentials Center of Mass Formula Critical Value Arc Length of a Curve Centroid Curly d Area below a Curve Chain Rule Curve Area between Curves Comparison Test Curve Sketching Area of an Ellipse Concave Cusp Area of a Parabolic Segment Concave Down Cylindrical Shell Method Area under a Curve Concave Up Decreasing Function Area Using Parametric Equations Conditional Convergence Definite Integral Area Using Polar Coordinates Constant Term Definite Integral Rules Degenerate Divergent Series Function Operations Del Operator e Fundamental Theorem of Calculus Deleted Neighborhood Ellipsoid GLB Derivative End Behavior Global Maximum Derivative of a Power Series Essential Discontinuity Global Minimum Derivative Rules Explicit Differentiation Golden Spiral Difference Quotient Explicit Function Graphic Methods Differentiable Exponential Decay Greatest Lower Bound Differential -
Calculus Formulas and Theorems
Formulas and Theorems for Reference I. Tbigonometric Formulas l. sin2d+c,cis2d:1 sec2d l*cot20:<:sc:20 +.I sin(-d) : -sitt0 t,rs(-//) = t r1sl/ : -tallH 7. sin(A* B) :sitrAcosB*silBcosA 8. : siri A cos B - siu B <:os,;l 9. cos(A+ B) - cos,4cos B - siuA siriB 10. cos(A- B) : cosA cosB + silrA sirrB 11. 2 sirrd t:osd 12. <'os20- coS2(i - siu20 : 2<'os2o - I - 1 - 2sin20 I 13. tan d : <.rft0 (:ost/ I 14. <:ol0 : sirrd tattH 1 15. (:OS I/ 1 16. cscd - ri" 6i /F tl r(. cos[I ^ -el : sitt d \l 18. -01 : COSA 215 216 Formulas and Theorems II. Differentiation Formulas !(r") - trr:"-1 Q,:I' ]tra-fg'+gf' gJ'-,f g' - * (i) ,l' ,I - (tt(.r))9'(.,') ,i;.[tyt.rt) l'' d, \ (sttt rrJ .* ('oqI' .7, tJ, \ . ./ stll lr dr. l('os J { 1a,,,t,:r) - .,' o.t "11'2 1(<,ot.r') - (,.(,2.r' Q:T rl , (sc'c:.r'J: sPl'.r tall 11 ,7, d, - (<:s<t.r,; - (ls(].]'(rot;.r fr("'),t -.'' ,1 - fr(u") o,'ltrc ,l ,, 1 ' tlll ri - (l.t' .f d,^ --: I -iAl'CSllLl'l t!.r' J1 - rz 1(Arcsi' r) : oT Il12 Formulas and Theorems 2I7 III. Integration Formulas 1. ,f "or:artC 2. [\0,-trrlrl *(' .t "r 3. [,' ,t.,: r^x| (' ,I 4. In' a,,: lL , ,' .l 111Q 5. In., a.r: .rhr.r' .r r (' ,l f 6. sirr.r d.r' - ( os.r'-t C ./ 7. /.,,.r' dr : sitr.i'| (' .t 8. tl:r:hr sec,rl+ C or ln Jccrsrl+ C ,f'r^rr f 9. -
Assignment 5
Differential Topology Assignment 5 Exercise 1: Classification of smooth 1-manifolds Let M be a compact, smooth and connected 1−manifold with boundary. Show that M is either diffeomorphic to a circle or to a closed interval [a; b] ⊂ R. This then shows that every compact smooth 1−manifold is build up from connected components each diffeomorphic to a circle or a closed interval. To prove the result, show the following steps: 1. Consider a Morse function f : M ! R. Let B denote the set of boundary points of M, and C the set of critical points of f. Show that M n (B[C) consists of a finite number of connected 1−manifolds, which we will call L1;:::;LN ⊂ M. 2. Prove that fk = fjLk : Lk ! f(Lk) is a diffeomorphism from Lk to an open interval f(Lk) ⊂ R. 3. Let L ⊂ M be a subset of a smooth 1−dimensional manifold M. Show that if L is diffeomorphic to an open interval in R, then there are at most two points a, b 2 L such that a, b2 = L. Here, L is the closure of L. 4. Consider a sequence (Li1 ;:::;Lik ) 2 fL1;:::;LN g. Such a sequence is called a chain if for every j 2 f1; : : : ; k − 1g the closures Lij and Lij+1 have a common boundary point. Show that a chain of maximal length m contains all of the 1−manifolds L1;:::;LN defined above. 5. To finish the proof, we will need a technical lemma. Let g :[a; b] ! R be a smooth function with positive derivative on [a; b] n fcg for c 2 (a; b). -
DISTANCE SETS in METRIC SPACES 2.2. a = I>1
DISTANCE SETS IN METRIC SPACES BY L. M. KELLY AND E. A. NORDHAUS 1. Introduction. In 1920 Steinhaus [lOjO) observed that the distance set of a linear set of positive Lebesgue measure contains an interval with end point zero (an initial interval). Subsequently considerable attention was de- voted to this and related ideas by Sierpinski [9], Ruziewicz [8], and others. S. Piccard in 1940 gathered together many of these results and carried out an extensive investigation of her own concerning distance sets of euclidean subsets, particularly linear subsets, in a tract entitled Sur les ensembles de distances (des ensembles de points d'un espace euclidien) [7]. Two principal problems suggest themselves in such investigations. First, what can be said of the distance set of a given space, and secondly, what can be said of the possible realizations, in a specified class of distance spaces, of a given distance set (that is, of a set of non-negative numbers including zero). The writers mentioned above have been principally concerned with the first problem, although Miss Piccard devotes some space to the second. In [4] efforts were made to improve and extend some of her results concerning the latter problem. By way of completing a survey of the literature known to us, we observe that Besicovitch and Miller [l ] have examined the influence of positive Carathéodory linear measure on distance sets of subsets of E2, while Coxeter and Todd [2] have considered the possibility of realizing finite distance sets in spaces with indefinite metrics. Numerous papers of Erdös, for example [3], contain isolated but interesting pertinent results. -
Notes on Metric Spaces
Notes on Metric Spaces These notes introduce the concept of a metric space, which will be an essential notion throughout this course and in others that follow. Some of this material is contained in optional sections of the book, but I will assume none of that and start from scratch. Still, you should check the corresponding sections in the book for a possibly different point of view on a few things. The main idea to have in mind is that a metric space is some kind of generalization of R in the sense that it is some kind of \space" which has a notion of \distance". Having such a \distance" function will allow us to phrase many concepts from real analysis|such as the notions of convergence and continuity|in a more general setting, which (somewhat) surprisingly makes many things actually easier to understand. Metric Spaces Definition 1. A metric on a set X is a function d : X × X ! R such that • d(x; y) ≥ 0 for all x; y 2 X; moreover, d(x; y) = 0 if and only if x = y, • d(x; y) = d(y; x) for all x; y 2 X, and • d(x; y) ≤ d(x; z) + d(z; y) for all x; y; z 2 X. A metric space is a set X together with a metric d on it, and we will use the notation (X; d) for a metric space. Often, if the metric d is clear from context, we will simply denote the metric space (X; d) by X itself. -
Lecture 3 Vector Spaces of Functions
12 Lecture 3 Vector Spaces of Functions 3.1 Space of Continuous Functions C(I) Let I ⊆ R be an interval. Then I is of the form (for some a < b) 8 > fx 2 Rj a < x < bg; an open interval; <> fx 2 Rj a · x · bg; a closed interval; I = > fx 2 Rj a · x < bg :> fx 2 Rj a < x · bg: Recall that the space of all functions f : I ¡! R is a vector space. We will now list some important subspaces: Example (1). Let C(I) be the space of all continuous functions on I. If f and g are continuous, so are the functions f + g and rf (r 2 R). Hence C(I) is a vector space. Recall, that a function is continuous, if the graph has no gaps. This can be formulated in di®erent ways: a) Let x0 2 I and let ² > 0 . Then there exists a ± > 0 such that for all x 2 I \ (x0 ¡ ±; x0 + ±) we have jf(x) ¡ f(x0)j < ² This tells us that the value of f at nearby points is arbitrarily close to the value of f at x0. 13 14 LECTURE 3. VECTOR SPACES OF FUNCTIONS b) A reformulation of (a) is: lim f(x) = f(x0) x!x0 3.2 Space of continuously di®erentiable func- tions C1(I) Example (2). The space C1(I). Here we assume that I is open. Recall that f is di®erentiable at x0 if f(x) ¡ f(x0) f(x0 + h) ¡ f(x0) 0 lim = lim =: f (x0) x!x0 x ¡ x0 h!0 h 0 exists. -
Topology: the Journey Into Separation Axioms
TOPOLOGY: THE JOURNEY INTO SEPARATION AXIOMS VIPUL NAIK Abstract. In this journey, we are going to explore the so called “separation axioms” in greater detail. We shall try to understand how these axioms are affected on going to subspaces, taking products, and looking at small open neighbourhoods. 1. What this journey entails 1.1. Prerequisites. Familiarity with definitions of these basic terms is expected: • Topological space • Open sets, closed sets, and limit points • Basis and subbasis • Continuous function and homeomorphism • Product topology • Subspace topology The target audience for this article are students doing a first course in topology. 1.2. The explicit promise. At the end of this journey, the learner should be able to: • Define the following: T0, T1, T2 (Hausdorff), T3 (regular), T4 (normal) • Understand how these properties are affected on taking subspaces, products and other similar constructions 2. What are separation axioms? 2.1. The idea behind separation. The defining attributes of a topological space (in terms of a family of open subsets) does little to guarantee that the points in the topological space are somehow distinct or far apart. The topological spaces that we would like to study, on the other hand, usually have these two features: • Any two points can be separated, that is, they are, to an extent, far apart. • Every point can be approached very closely from other points, and if we take a sequence of points, they will usually get to cluster around a point. On the face of it, the above two statements do not seem to reconcile each other. In fact, topological spaces become interesting precisely because they are nice in both the above ways.