Compactness in Metric Spaces
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Metric Geometry in a Tame Setting
University of California Los Angeles Metric Geometry in a Tame Setting A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Mathematics by Erik Walsberg 2015 c Copyright by Erik Walsberg 2015 Abstract of the Dissertation Metric Geometry in a Tame Setting by Erik Walsberg Doctor of Philosophy in Mathematics University of California, Los Angeles, 2015 Professor Matthias J. Aschenbrenner, Chair We prove basic results about the topology and metric geometry of metric spaces which are definable in o-minimal expansions of ordered fields. ii The dissertation of Erik Walsberg is approved. Yiannis N. Moschovakis Chandrashekhar Khare David Kaplan Matthias J. Aschenbrenner, Committee Chair University of California, Los Angeles 2015 iii To Sam. iv Table of Contents 1 Introduction :::::::::::::::::::::::::::::::::::::: 1 2 Conventions :::::::::::::::::::::::::::::::::::::: 5 3 Metric Geometry ::::::::::::::::::::::::::::::::::: 7 3.1 Metric Spaces . 7 3.2 Maps Between Metric Spaces . 8 3.3 Covers and Packing Inequalities . 9 3.3.1 The 5r-covering Lemma . 9 3.3.2 Doubling Metrics . 10 3.4 Hausdorff Measures and Dimension . 11 3.4.1 Hausdorff Measures . 11 3.4.2 Hausdorff Dimension . 13 3.5 Topological Dimension . 15 3.6 Left-Invariant Metrics on Groups . 15 3.7 Reductions, Ultralimits and Limits of Metric Spaces . 16 3.7.1 Reductions of Λ-valued Metric Spaces . 16 3.7.2 Ultralimits . 17 3.7.3 GH-Convergence and GH-Ultralimits . 18 3.7.4 Asymptotic Cones . 19 3.7.5 Tangent Cones . 22 3.7.6 Conical Metric Spaces . 22 3.8 Normed Spaces . 23 4 T-Convexity :::::::::::::::::::::::::::::::::::::: 24 4.1 T-convex Structures . -
Boundedness in Linear Topological Spaces
BOUNDEDNESS IN LINEAR TOPOLOGICAL SPACES BY S. SIMONS Introduction. Throughout this paper we use the symbol X for a (real or complex) linear space, and the symbol F to represent the basic field in question. We write R+ for the set of positive (i.e., ^ 0) real numbers. We use the term linear topological space with its usual meaning (not necessarily Tx), but we exclude the case where the space has the indiscrete topology (see [1, 3.3, pp. 123-127]). A linear topological space is said to be a locally bounded space if there is a bounded neighbourhood of 0—which comes to the same thing as saying that there is a neighbourhood U of 0 such that the sets {(1/n) U} (n = 1,2,—) form a base at 0. In §1 we give a necessary and sufficient condition, in terms of invariant pseudo- metrics, for a linear topological space to be locally bounded. In §2 we discuss the relationship of our results with other results known on the subject. In §3 we introduce two ways of classifying the locally bounded spaces into types in such a way that each type contains exactly one of the F spaces (0 < p ^ 1), and show that these two methods of classification turn out to be identical. Also in §3 we prove a metrization theorem for locally bounded spaces, which is related to the normal metrization theorem for uniform spaces, but which uses a different induction procedure. In §4 we introduce a large class of linear topological spaces which includes the locally convex spaces and the locally bounded spaces, and for which one of the more important results on boundedness in locally convex spaces is valid. -
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). -
1 Definition
Math 125B, Winter 2015. Multidimensional integration: definitions and main theorems 1 Definition A(closed) rectangle R ⊆ Rn is a set of the form R = [a1; b1] × · · · × [an; bn] = f(x1; : : : ; xn): a1 ≤ x1 ≤ b1; : : : ; an ≤ x1 ≤ bng: Here ak ≤ bk, for k = 1; : : : ; n. The (n-dimensional) volume of R is Vol(R) = Voln(R) = (b1 − a1) ··· (bn − an): We say that the rectangle is nondegenerate if Vol(R) > 0. A partition P of R is induced by partition of each of the n intervals in each dimension. We identify the partition with the resulting set of closed subrectangles of R. Assume R ⊆ Rn and f : R ! R is a bounded function. Then we define upper sum of f with respect to a partition P, and upper integral of f on R, X Z U(f; P) = sup f · Vol(B); (U) f = inf U(f; P); P B2P B R and analogously lower sum of f with respect to a partition P, and lower integral of f on R, X Z L(f; P) = inf f · Vol(B); (L) f = sup L(f; P): B P B2P R R R RWe callR f integrable on R if (U) R f = (L) R f and in this case denote the common value by R f = R f(x) dx. As in one-dimensional case, f is integrable on R if and only if Cauchy condition is satisfied. The Cauchy condition states that, for every ϵ > 0, there exists a partition P so that U(f; P) − L(f; P) < ϵ. -
Discrete Geometric Homotopy Theory and Critical Values of Metric Spaces Leonard Duane Wilkins [email protected]
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by University of Tennessee, Knoxville: Trace University of Tennessee, Knoxville Trace: Tennessee Research and Creative Exchange Doctoral Dissertations Graduate School 5-2011 Discrete Geometric Homotopy Theory and Critical Values of Metric Spaces Leonard Duane Wilkins [email protected] Recommended Citation Wilkins, Leonard Duane, "Discrete Geometric Homotopy Theory and Critical Values of Metric Spaces. " PhD diss., University of Tennessee, 2011. https://trace.tennessee.edu/utk_graddiss/1039 This Dissertation is brought to you for free and open access by the Graduate School at Trace: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of Trace: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. To the Graduate Council: I am submitting herewith a dissertation written by Leonard Duane Wilkins entitled "Discrete Geometric Homotopy Theory and Critical Values of Metric Spaces." I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy, with a major in Mathematics. Conrad P. Plaut, Major Professor We have read this dissertation and recommend its acceptance: James Conant, Fernando Schwartz, Michael Guidry Accepted for the Council: Dixie L. Thompson Vice Provost and Dean of the Graduate School (Original signatures are on file with official student records.) To the Graduate Council: I am submitting herewith a dissertation written by Leonard Duane Wilkins entitled \Discrete Geometric Homotopy Theory and Critical Values of Metric Spaces." I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy, with a major in Mathematics. -
A Representation of Partially Ordered Preferences Teddy Seidenfeld
A Representation of Partially Ordered Preferences Teddy Seidenfeld; Mark J. Schervish; Joseph B. Kadane The Annals of Statistics, Vol. 23, No. 6. (Dec., 1995), pp. 2168-2217. Stable URL: http://links.jstor.org/sici?sici=0090-5364%28199512%2923%3A6%3C2168%3AAROPOP%3E2.0.CO%3B2-C The Annals of Statistics is currently published by Institute of Mathematical Statistics. Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at http://www.jstor.org/journals/ims.html. Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission. The JSTOR Archive is a trusted digital repository providing for long-term preservation and access to leading academic journals and scholarly literature from around the world. The Archive is supported by libraries, scholarly societies, publishers, and foundations. It is an initiative of JSTOR, a not-for-profit organization with a mission to help the scholarly community take advantage of advances in technology. For more information regarding JSTOR, please contact [email protected]. http://www.jstor.org Tue Mar 4 10:44:11 2008 The Annals of Statistics 1995, Vol. -
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. -
Compact and Totally Bounded Metric Spaces
Appendix A Compact and Totally Bounded Metric Spaces Definition A.0.1 (Diameter of a set) The diameter of a subset E in a metric space (X, d) is the quantity diam(E) = sup d(x, y). x,y∈E ♦ Definition A.0.2 (Bounded and totally bounded sets)A subset E in a metric space (X, d) is said to be 1. bounded, if there exists a number M such that E ⊆ B(x, M) for every x ∈ E, where B(x, M) is the ball centred at x of radius M. Equivalently, d(x1, x2) ≤ M for every x1, x2 ∈ E and some M ∈ R; > { ,..., } 2. totally bounded, if for any 0 there exists a finite subset x1 xn of X ( , ) = ,..., = such that the (open or closed) balls B xi , i 1 n cover E, i.e. E n ( , ) i=1 B xi . ♦ Clearly a set is bounded if its diameter is finite. We will see later that a bounded set may not be totally bounded. Now we prove the converse is always true. Proposition A.0.1 (Total boundedness implies boundedness) In a metric space (X, d) any totally bounded set E is bounded. Proof Total boundedness means we may choose = 1 and find A ={x1,...,xn} in X such that for every x ∈ E © The Editor(s) (if applicable) and The Author(s), under exclusive 429 license to Springer Nature Switzerland AG 2020 S. Gentili, Measure, Integration and a Primer on Probability Theory, UNITEXT 125, https://doi.org/10.1007/978-3-030-54940-4 430 Appendix A: Compact and Totally Bounded Metric Spaces inf d(xi , x) ≤ 1. -
Magnitude Homology
Magnitude homology Tom Leinster Edinburgh A theme of this conference so far When introducing a piece of category theory during a talk: 1. apologize; 2. blame John Baez. A theme of this conference so far When introducing a piece of category theory during a talk: 1. apologize; 2. blame John Baez. 1. apologize; 2. blame John Baez. A theme of this conference so far When introducing a piece of category theory during a talk: Plan 1. The idea of magnitude 2. The magnitude of a metric space 3. The idea of magnitude homology 4. The magnitude homology of a metric space 1. The idea of magnitude Size For many types of mathematical object, there is a canonical notion of size. • Sets have cardinality. It satisfies jX [ Y j = jX j + jY j − jX \ Y j jX × Y j = jX j × jY j : n • Subsets of R have volume. It satisfies vol(X [ Y ) = vol(X ) + vol(Y ) − vol(X \ Y ) vol(X × Y ) = vol(X ) × vol(Y ): • Topological spaces have Euler characteristic. It satisfies χ(X [ Y ) = χ(X ) + χ(Y ) − χ(X \ Y ) (under hypotheses) χ(X × Y ) = χ(X ) × χ(Y ): Challenge Find a general definition of `size', including these and other examples. One answer The magnitude of an enriched category. Enriched categories A monoidal category is a category V equipped with a product operation. A category X enriched in V is like an ordinary category, but each HomX(X ; Y ) is now an object of V (instead of a set). linear categories metric spaces categories enriched posets categories monoidal • Vect • Set • ([0; 1]; ≥) categories • (0 ! 1) The magnitude of an enriched category There is a general definition of the magnitude jXj of an enriched category. -
Math 118: Topology in Metric Spaces
Math 118: Topology in Metric Spaces You may recall having seen definitions of open and closed sets, as well as other topological concepts such as limit and isolated points and density, for sets of real numbers (or even sets of points in Euclidean space). A lot of these concepts help us define in analysis what we mean by points being “close” to one another so that we can discuss convergence and continuity. We are going to re-examine all of these concepts for subsets of more general spaces: One on which we have a nicely defined distance. Definition A metric space (X, d) is a set X, whose elements we call points, along with a function d : X × X → R that satisfies (a) (b) (c) For any two points p, q ∈ X, d(p, q) is called the distance from p to q. Notes: We clearly want non-negative distances, as well as symmetry. If we think of points in the Euclidean space R2, or the complex plane, property (c) is the usual triangle inequality. Also note that for any Y ⊆ X, (Y, d) is a metric space. Examples You may have noticed that in a lot of these examples, the distance is of a special form. Consider a normed vector space (a vector space, as you learned in linear algebra, allows for the addition of the elements, as well as multiplication by a scalar. A norm is a real-valued function on the vector space that satisfies the triangle inequality, is 0 only for the 0 vector, and multiplying a vector by a scalar α just increases its norm by the factor |α|.) The norm always allows us to define a distance: for two elements v, w in the space, d(v, w) = kv −wk. -
Chapter 7. Complete Metric Spaces and Function Spaces
43. Complete Metric Spaces 1 Chapter 7. Complete Metric Spaces and Function Spaces Note. Recall from your Analysis 1 (MATH 4217/5217) class that the real numbers R are a “complete ordered field” (in fact, up to isomorphism there is only one such structure; see my online notes at http://faculty.etsu.edu/gardnerr/4217/ notes/1-3.pdf). The Axiom of Completeness in this setting requires that ev- ery set of real numbers with an upper bound have a least upper bound. But this idea (which dates from the mid 19th century and the work of Richard Dedekind) depends on the ordering of R (as evidenced by the use of the terms “upper” and “least”). In a metric space, there is no such ordering and so the completeness idea (which is fundamental to all of analysis) must be dealt with in an alternate way. Munkres makes a nice comment on page 263 declaring that“completeness is a metric property rather than a topological one.” Section 43. Complete Metric Spaces Note. In this section, we define Cauchy sequences and use them to define complete- ness. The motivation for these ideas comes from the fact that a sequence of real numbers is Cauchy if and only if it is convergent (see my online notes for Analysis 1 [MATH 4217/5217] http://faculty.etsu.edu/gardnerr/4217/notes/2-3.pdf; notice Exercise 2.3.13). 43. Complete Metric Spaces 2 Definition. Let (X, d) be a metric space. A sequence (xn) of points of X is a Cauchy sequence on (X, d) if for all ε > 0 there is N N such that if m, n N ∈ ≥ then d(xn, xm) < ε. -
Topology Proceedings
Topology Proceedings Web: http://topology.auburn.edu/tp/ Mail: Topology Proceedings Department of Mathematics & Statistics Auburn University, Alabama 36849, USA E-mail: [email protected] ISSN: 0146-4124 COPYRIGHT °c by Topology Proceedings. All rights reserved. Topology Proceedings Vol 18, 1993 H-BOUNDED SETS DOUGLAS D. MOONEY ABSTRACT. H-bounded sets were introduced by Lam brinos in 1976. Recently they have proven to be useful in the study of extensions of Hausdorff spaces. In this con text the question has arisen: Is every H-bounded set the subset of an H-set? This was originally asked by Lam brinos along with the questions: Is every H-bounded set the subset of a countably H-set? and Is every countably H-bounded set the subset of a countably H-set? In this paper, an example is presented which gives a no answer to each of these questions. Some new characterizations and properties of H-bounded sets are also examined. 1. INTRODUCTION The concept of an H-bounded set was defined by Lambrinos in [1] as a weakening of the notion of a bounded set which he defined in [2]. H-bounded sets have arisen naturally in the au thor's study of extensions of Hausdorff spaces. In [3] it is shown that the notions of S- and O-equivalence defined by Porter and Votaw [6] on the upper-semilattice of H-closed extensions of a Hausdorff space are equivalent if and only if every closed nowhere dense set is an H-bounded set. As a consequence, it is shown that a Hausdorff space has a unique Hausdorff exten sion if and only if the space is non-H-closed, almost H-closed, and every closed nowhere dense set is H-bounded.