The Notion of Space in Mathematics
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Surfaces and Fundamental Groups
HOMEWORK 5 — SURFACES AND FUNDAMENTAL GROUPS DANNY CALEGARI This homework is due Wednesday November 22nd at the start of class. Remember that the notation e1; e2; : : : ; en w1; w2; : : : ; wm h j i denotes the group whose generators are equivalence classes of words in the generators ei 1 and ei− , where two words are equivalent if one can be obtained from the other by inserting 1 1 or deleting eiei− or ei− ei or by inserting or deleting a word wj or its inverse. Problem 1. Let Sn be a surface obtained from a regular 4n–gon by identifying opposite sides by a translation. What is the Euler characteristic of Sn? What surface is it? Find a presentation for its fundamental group. Problem 2. Let S be the surface obtained from a hexagon by identifying opposite faces by translation. Then the fundamental group of S should be given by the generators a; b; c 1 1 1 corresponding to the three pairs of edges, with the relation abca− b− c− coming from the boundary of the polygonal region. What is wrong with this argument? Write down an actual presentation for π1(S; p). What surface is S? Problem 3. The four–color theorem of Appel and Haken says that any map in the plane can be colored with at most four distinct colors so that two regions which share a com- mon boundary segment have distinct colors. Find a cell decomposition of the torus into 7 polygons such that each two polygons share at least one side in common. Remark. -
THE DIMENSION of a VECTOR SPACE 1. Introduction This Handout
THE DIMENSION OF A VECTOR SPACE KEITH CONRAD 1. Introduction This handout is a supplementary discussion leading up to the definition of dimension of a vector space and some of its properties. We start by defining the span of a finite set of vectors and linear independence of a finite set of vectors, which are combined to define the all-important concept of a basis. Definition 1.1. Let V be a vector space over a field F . For any finite subset fv1; : : : ; vng of V , its span is the set of all of its linear combinations: Span(v1; : : : ; vn) = fc1v1 + ··· + cnvn : ci 2 F g: Example 1.2. In F 3, Span((1; 0; 0); (0; 1; 0)) is the xy-plane in F 3. Example 1.3. If v is a single vector in V then Span(v) = fcv : c 2 F g = F v is the set of scalar multiples of v, which for nonzero v should be thought of geometrically as a line (through the origin, since it includes 0 · v = 0). Since sums of linear combinations are linear combinations and the scalar multiple of a linear combination is a linear combination, Span(v1; : : : ; vn) is a subspace of V . It may not be all of V , of course. Definition 1.4. If fv1; : : : ; vng satisfies Span(fv1; : : : ; vng) = V , that is, if every vector in V is a linear combination from fv1; : : : ; vng, then we say this set spans V or it is a spanning set for V . Example 1.5. In F 2, the set f(1; 0); (0; 1); (1; 1)g is a spanning set of F 2. -
I-Sequential Topological Spaces∗
Applied Mathematics E-Notes, 14(2014), 236-241 c ISSN 1607-2510 Available free at mirror sites of http://www.math.nthu.edu.tw/ amen/ -Sequential Topological Spaces I Sudip Kumar Pal y Received 10 June 2014 Abstract In this paper a new notion of topological spaces namely, I-sequential topo- logical spaces is introduced and investigated. This new space is a strictly weaker notion than the first countable space. Also I-sequential topological space is a quotient of a metric space. 1 Introduction The idea of convergence of real sequence have been extended to statistical convergence by [2, 14, 15] as follows: If N denotes the set of natural numbers and K N then Kn denotes the set k K : k n and Kn stands for the cardinality of the set Kn. The natural densityf of the2 subset≤ Kg is definedj j by Kn d(K) = lim j j, n n !1 provided the limit exists. A sequence xn n N of points in a metric space (X, ) is said to be statistically f g 2 convergent to l if for arbitrary " > 0, the set K(") = k N : (xk, l) " has natural density zero. A lot of investigation has been donef on2 this convergence≥ andg its topological consequences after initial works by [5, 13]. It is easy to check that the family Id = A N : d(A) = 0 forms a non-trivial f g admissible ideal of N (recall that I 2N is called an ideal if (i) A, B I implies A B I and (ii) A I,B A implies B I. -
MTH 304: General Topology Semester 2, 2017-2018
MTH 304: General Topology Semester 2, 2017-2018 Dr. Prahlad Vaidyanathan Contents I. Continuous Functions3 1. First Definitions................................3 2. Open Sets...................................4 3. Continuity by Open Sets...........................6 II. Topological Spaces8 1. Definition and Examples...........................8 2. Metric Spaces................................. 11 3. Basis for a topology.............................. 16 4. The Product Topology on X × Y ...................... 18 Q 5. The Product Topology on Xα ....................... 20 6. Closed Sets.................................. 22 7. Continuous Functions............................. 27 8. The Quotient Topology............................ 30 III.Properties of Topological Spaces 36 1. The Hausdorff property............................ 36 2. Connectedness................................. 37 3. Path Connectedness............................. 41 4. Local Connectedness............................. 44 5. Compactness................................. 46 6. Compact Subsets of Rn ............................ 50 7. Continuous Functions on Compact Sets................... 52 8. Compactness in Metric Spaces........................ 56 9. Local Compactness.............................. 59 IV.Separation Axioms 62 1. Regular Spaces................................ 62 2. Normal Spaces................................ 64 3. Tietze's extension Theorem......................... 67 4. Urysohn Metrization Theorem........................ 71 5. Imbedding of Manifolds.......................... -
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). -
DEFINITIONS and THEOREMS in GENERAL TOPOLOGY 1. Basic
DEFINITIONS AND THEOREMS IN GENERAL TOPOLOGY 1. Basic definitions. A topology on a set X is defined by a family O of subsets of X, the open sets of the topology, satisfying the axioms: (i) ; and X are in O; (ii) the intersection of finitely many sets in O is in O; (iii) arbitrary unions of sets in O are in O. Alternatively, a topology may be defined by the neighborhoods U(p) of an arbitrary point p 2 X, where p 2 U(p) and, in addition: (i) If U1;U2 are neighborhoods of p, there exists U3 neighborhood of p, such that U3 ⊂ U1 \ U2; (ii) If U is a neighborhood of p and q 2 U, there exists a neighborhood V of q so that V ⊂ U. A topology is Hausdorff if any distinct points p 6= q admit disjoint neigh- borhoods. This is almost always assumed. A set C ⊂ X is closed if its complement is open. The closure A¯ of a set A ⊂ X is the intersection of all closed sets containing X. A subset A ⊂ X is dense in X if A¯ = X. A point x 2 X is a cluster point of a subset A ⊂ X if any neighborhood of x contains a point of A distinct from x. If A0 denotes the set of cluster points, then A¯ = A [ A0: A map f : X ! Y of topological spaces is continuous at p 2 X if for any open neighborhood V ⊂ Y of f(p), there exists an open neighborhood U ⊂ X of p so that f(U) ⊂ V . -
Inner Product Spaces
CHAPTER 6 Woman teaching geometry, from a fourteenth-century edition of Euclid’s geometry book. Inner Product Spaces In making the definition of a vector space, we generalized the linear structure (addition and scalar multiplication) of R2 and R3. We ignored other important features, such as the notions of length and angle. These ideas are embedded in the concept we now investigate, inner products. Our standing assumptions are as follows: 6.1 Notation F, V F denotes R or C. V denotes a vector space over F. LEARNING OBJECTIVES FOR THIS CHAPTER Cauchy–Schwarz Inequality Gram–Schmidt Procedure linear functionals on inner product spaces calculating minimum distance to a subspace Linear Algebra Done Right, third edition, by Sheldon Axler 164 CHAPTER 6 Inner Product Spaces 6.A Inner Products and Norms Inner Products To motivate the concept of inner prod- 2 3 x1 , x 2 uct, think of vectors in R and R as x arrows with initial point at the origin. x R2 R3 H L The length of a vector in or is called the norm of x, denoted x . 2 k k Thus for x .x1; x2/ R , we have The length of this vector x is p D2 2 2 x x1 x2 . p 2 2 x1 x2 . k k D C 3 C Similarly, if x .x1; x2; x3/ R , p 2D 2 2 2 then x x1 x2 x3 . k k D C C Even though we cannot draw pictures in higher dimensions, the gener- n n alization to R is obvious: we define the norm of x .x1; : : : ; xn/ R D 2 by p 2 2 x x1 xn : k k D C C The norm is not linear on Rn. -
Problem Set 10 ETH Zürich FS 2020
d-math Topology ETH Zürich Prof. A. Carlotto Solutions - Problem set 10 FS 2020 10. Computing the fundamental group - Part I Chef’s table This week we start computing fundamental group, mainly using Van Kampen’s Theorem (but possibly in combination with other tools). The first two problems are sort of basic warm-up exercises to get a feeling for the subject. Problems 10.3 - 10.4 - 10.5 are almost identical, and they all build on the same trick (think in terms of the planar models!); you can write down the solution to just one of them, but make sure you give some thought about all. From there (so building on these results), using Van Kampen you can compute the fundamental group of the torus (which we knew anyway, but through a different method), of the Klein bottle and of higher-genus surfaces. Problem 10.8 is the most important in this series, and learning this trick will trivialise half of the problems on this subject (see Problem 10.9 for a first, striking, application); writing down all homotopies of 10.8 explicitly might be a bit tedious, so just make sure to have a clear picture (and keep in mind this result for the future). 10.1. Topological manifold minus a point L. Let X be a connected topological ∼ manifold, of dimension n ≥ 3. Prove that for every x ∈ X one has π1(X) = π1(X \{x}). 10.2. Plane without the circle L. Show that there is no homeomorphism f : R2 \S1 → R2 \ S1 such that f(0, 0) = (2, 0). -
On the Classification of Heegaard Splittings
Manuscript (Revised) ON THE CLASSIFICATION OF HEEGAARD SPLITTINGS TOBIAS HOLCK COLDING, DAVID GABAI, AND DANIEL KETOVER Abstract. The long standing classification problem in the theory of Heegaard splittings of 3-manifolds is to exhibit for each closed 3-manifold a complete list, without duplication, of all its irreducible Heegaard surfaces, up to isotopy. We solve this problem for non Haken hyperbolic 3-manifolds. 0. Introduction The main result of this paper is Theorem 0.1. Let N be a closed non Haken hyperbolic 3-manifold. There exists an effec- tively constructible set S0;S1; ··· ;Sn such that if S is an irreducible Heegaard splitting, then S is isotopic to exactly one Si. Remarks 0.2. Given g 2 N Tao Li [Li3] shows how to construct a finite list of genus-g Heegaard surfaces such that, up to isotopy, every genus-g Heegaard surface appears in that list. By [CG] there exists an effectively computable C(N) such that one need only consider g ≤ C(N), hence there exists an effectively constructible set of Heegaard surfaces that contains every irreducible Heegaard surface. (The methods of [CG] also effectively constructs these surfaces.) However, this list may contain reducible splittings and duplications. The main goal of this paper is to give an effective algorithm that weeds out the duplications and reducible splittings. Idea of Proof. We first prove the Thick Isotopy Lemma which implies that if Si is isotopic to Sj, then there exists a smooth isotopy by surfaces of area uniformly bounded above and diametric soul uniformly bounded below. (The diametric soul of a surface T ⊂ N is the infimal diameter in N of the essential closed curves in T .) The proof of this lemma uses a 2-parameter sweepout argument that may be of independent interest. -
Lecture 1 Rachel Roberts
Lecture 1 Rachel Roberts Joan Licata May 13, 2008 The following notes are a rush transcript of Rachel’s lecture. Any mistakes or typos are mine, and please point these out so that I can post a corrected copy online. Outline 1. Three-manifolds: Presentations and basic structures 2. Foliations, especially codimension 1 foliations 3. Non-trivial examples of three-manifolds, generalizations of foliations to laminations 1 Three-manifolds Unless otherwise noted, let M denote a closed (compact) and orientable three manifold with empty boundary. (Note that these restrictions are largely for convenience.) We’ll start by collecting some useful facts about three manifolds. Definition 1. For our purposes, a triangulation of M is a decomposition of M into a finite colleciton of tetrahedra which meet only along shared faces. Fact 1. Every M admits a triangulation. This allows us to realize any M as three dimensional simplicial complex. Fact 2. M admits a C ∞ structure which is unique up to diffeomorphism. We’ll work in either the C∞ or PL category, which are equivalent for three manifolds. In partuclar, we’ll rule out wild (i.e. pathological) embeddings. 2 Examples 1. S3 (Of course!) 2. S1 × S2 3. More generally, for F a closed orientable surface, F × S1 1 Figure 1: A Heegaard diagram for the genus four splitting of S3. Figure 2: Left: Genus one Heegaard decomposition of S3. Right: Genus one Heegaard decomposi- tion of S2 × S1. We’ll begin by studying S3 carefully. If we view S3 as compactification of R3, we can take a neighborhood of the origin, which is a solid ball. -
Introduction to Topology
Introduction to Topology Randall R. Holmes Auburn University Typeset by AMS-TEX 1 Chapter 1. Metric Spaces 1. De¯nition and Examples. As the course progresses we will need to review some basic notions about sets and functions. We begin with a little set theory. Let S be a set. For A; B ⊆ S, put A [ B := fs 2 S j s 2 A or s 2 Bg A \ B := fs 2 S j s 2 A and s 2 Bg S ¡ A := fs 2 S j s2 = Ag 1.1 Theorem. Let A; B ⊆ S. Then S ¡ (A [ B) = (S ¡ A) \ (S ¡ B). Exercise 1. Let A ⊆ S. Prove that S ¡ (S ¡ A) = A. Exercise 2. Let A; B ⊆ S. Prove that S ¡ (A \ B) = (S ¡ A) [ (S ¡ B). (Hint: Either prove this directly as in the proof of Theorem 1.1, or just use the statement of Theorem 1.1 together with Exercise 1.) Exercise 3. Let A; B; C ⊆ S. Prove that A M C ⊆ (A M B) [ (B M C), where A M B := (A [ B) ¡ (A \ B). 1.2 De¯nition. A metric space is a pair (X; d) where X is a non-empty set, and d is a function d : X £ X ! R such that for all x; y; z 2 X (1) d(x; y) ¸ 0, (2) d(x; y) = 0 if and only if x = y, (3) d(x; y) = d(y; x), and (4) d(x; z) · d(x; y) + d(y; z) (\triangle inequality"). In the de¯nition, d is called the distance function (or metric) and X is called the underlying set. -
Vector Space Theory
Vector Space Theory A course for second year students by Robert Howlett typesetting by TEX Contents Copyright University of Sydney Chapter 1: Preliminaries 1 §1a Logic and commonSchool sense of Mathematics and Statistics 1 §1b Sets and functions 3 §1c Relations 7 §1d Fields 10 Chapter 2: Matrices, row vectors and column vectors 18 §2a Matrix operations 18 §2b Simultaneous equations 24 §2c Partial pivoting 29 §2d Elementary matrices 32 §2e Determinants 35 §2f Introduction to eigenvalues 38 Chapter 3: Introduction to vector spaces 49 §3a Linearity 49 §3b Vector axioms 52 §3c Trivial consequences of the axioms 61 §3d Subspaces 63 §3e Linear combinations 71 Chapter 4: The structure of abstract vector spaces 81 §4a Preliminary lemmas 81 §4b Basis theorems 85 §4c The Replacement Lemma 86 §4d Two properties of linear transformations 91 §4e Coordinates relative to a basis 93 Chapter 5: Inner Product Spaces 99 §5a The inner product axioms 99 §5b Orthogonal projection 106 §5c Orthogonal and unitary transformations 116 §5d Quadratic forms 121 iii Chapter 6: Relationships between spaces 129 §6a Isomorphism 129 §6b Direct sums Copyright University of Sydney 134 §6c Quotient spaces 139 §6d The dual spaceSchool of Mathematics and Statistics 142 Chapter 7: Matrices and Linear Transformations 148 §7a The matrix of a linear transformation 148 §7b Multiplication of transformations and matrices 153 §7c The Main Theorem on Linear Transformations 157 §7d Rank and nullity of matrices 161 Chapter 8: Permutations and determinants 171 §8a Permutations 171 §8b Determinants