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Completeness in Quasi-Pseudometric Spaces—A Survey
mathematics Article Completeness in Quasi-Pseudometric Spaces—A Survey ¸StefanCobzas Faculty of Mathematics and Computer Science, Babe¸s-BolyaiUniversity, 400 084 Cluj-Napoca, Romania; [email protected] Received:17 June 2020; Accepted: 24 July 2020; Published: 3 August 2020 Abstract: The aim of this paper is to discuss the relations between various notions of sequential completeness and the corresponding notions of completeness by nets or by filters in the setting of quasi-metric spaces. We propose a new definition of right K-Cauchy net in a quasi-metric space for which the corresponding completeness is equivalent to the sequential completeness. In this way we complete some results of R. A. Stoltenberg, Proc. London Math. Soc. 17 (1967), 226–240, and V. Gregori and J. Ferrer, Proc. Lond. Math. Soc., III Ser., 49 (1984), 36. A discussion on nets defined over ordered or pre-ordered directed sets is also included. Keywords: metric space; quasi-metric space; uniform space; quasi-uniform space; Cauchy sequence; Cauchy net; Cauchy filter; completeness MSC: 54E15; 54E25; 54E50; 46S99 1. Introduction It is well known that completeness is an essential tool in the study of metric spaces, particularly for fixed points results in such spaces. The study of completeness in quasi-metric spaces is considerably more involved, due to the lack of symmetry of the distance—there are several notions of completeness all agreing with the usual one in the metric case (see [1] or [2]). Again these notions are essential in proving fixed point results in quasi-metric spaces as it is shown by some papers on this topic as, for instance, [3–6] (see also the book [7]). -
"Mathematics for Computer Science" (MCS)
“mcs” — 2016/9/28 — 23:07 — page i — #1 Mathematics for Computer Science revised Wednesday 28th September, 2016, 23:07 Eric Lehman Google Inc. F Thomson Leighton Department of Mathematics and the Computer Science and AI Laboratory, Massachussetts Institute of Technology; Akamai Technologies Albert R Meyer Department of Electrical Engineering and Computer Science and the Computer Science and AI Laboratory, Massachussetts Institute of Technology 2016, Eric Lehman, F Tom Leighton, Albert R Meyer. This work is available under the terms of the Creative Commons Attribution-ShareAlike 3.0 license. “mcs” — 2016/9/28 — 23:07 — page ii — #2 “mcs” — 2016/9/28 — 23:07 — page iii — #3 Contents I Proofs Introduction 3 0.1 References4 1 What is a Proof? 5 1.1 Propositions5 1.2 Predicates8 1.3 The Axiomatic Method8 1.4 Our Axioms9 1.5 Proving an Implication 11 1.6 Proving an “If and Only If” 13 1.7 Proof by Cases 15 1.8 Proof by Contradiction 16 1.9 Good Proofs in Practice 17 1.10 References 19 2 The Well Ordering Principle 29 2.1 Well Ordering Proofs 29 2.2 Template for Well Ordering Proofs 30 2.3 Factoring into Primes 32 2.4 Well Ordered Sets 33 3 Logical Formulas 47 3.1 Propositions from Propositions 48 3.2 Propositional Logic in Computer Programs 51 3.3 Equivalence and Validity 54 3.4 The Algebra of Propositions 56 3.5 The SAT Problem 61 3.6 Predicate Formulas 62 3.7 References 67 4 Mathematical Data Types 93 4.1 Sets 93 4.2 Sequences 98 4.3 Functions 99 4.4 Binary Relations 101 4.5 Finite Cardinality 105 “mcs” — 2016/9/28 — 23:07 — page iv — #4 Contentsiv 5 Induction 125 5.1 Ordinary Induction 125 5.2 Strong Induction 134 5.3 Strong Induction vs. -
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). -
Continuity Properties and Sensitivity Analysis of Parameterized Fixed
Continuity properties and sensitivity analysis of parameterized fixed points and approximate fixed points Zachary Feinsteina Washington University in St. Louis August 2, 2016 Abstract In this paper we consider continuity of the set of fixed points and approximate fixed points for parameterized set-valued mappings. Continuity properties are provided for the fixed points of general multivalued mappings, with additional results shown for contraction mappings. Further analysis is provided for the continuity of the approxi- mate fixed points of set-valued functions. Additional results are provided on sensitivity of the fixed points via set-valued derivatives related to tangent cones. Key words: fixed point problems; approximate fixed point problems; set-valued continuity; data dependence; generalized differentiation MSC: 54H25; 54C60; 26E25; 47H04; 47H14 1 Introduction Fixed points are utilized in many applications. Often the parameters of such models are estimated from data. As such it is important to understand how the set of fixed points aZachary Feinstein, ESE, Washington University, St. Louis, MO 63130, USA, [email protected]. 1 changes with respect to the parameters. We motivate our general approach by consider applications from economics and finance. In particular, the solution set of a parameterized game (i.e., Nash equilibria) fall under this setting. Thus if the parameters of the individuals playing the game are not perfectly known, sensitivity analysis can be done in a general way even without unique equilibrium. For the author, the immediate motivation was from financial systemic risk models such as those in [10, 8, 4, 12] where methodology to estimate system parameters are studied in, e.g., [15]. -
Arxiv:1910.07913V4 [Math.LO]
REPRESENTATIONS AND THE FOUNDATIONS OF MATHEMATICS SAM SANDERS Abstract. The representation of mathematical objects in terms of (more) ba- sic ones is part and parcel of (the foundations of) mathematics. In the usual foundations of mathematics, i.e. ZFC set theory, all mathematical objects are represented by sets, while ordinary, i.e. non-set theoretic, mathematics is rep- resented in the more parsimonious language of second-order arithmetic. This paper deals with the latter representation for the rather basic case of continu- ous functions on the reals and Baire space. We show that the logical strength of basic theorems named after Tietze, Heine, and Weierstrass, changes signifi- cantly upon the replacement of ‘second-order representations’ by ‘third-order functions’. We discuss the implications and connections to the Reverse Math- ematics program and its foundational claims regarding predicativist mathe- matics and Hilbert’s program for the foundations of mathematics. Finally, we identify the problem caused by representations of continuous functions and formulate a criterion to avoid problematic codings within the bigger picture of representations. 1. Introduction Lest we be misunderstood, let our first order of business be to formulate the following blanket caveat: any formalisation of mathematics generally involves some kind of representation (aka coding) of mathematical objects in terms of others. Now, the goal of this paper is to critically examine the role of representations based on the language of second-order arithmetic; such an examination perhaps unsurpris- ingly involves the comparison of theorems based on second-order representations versus theorems formulated in third-order arithmetic. To be absolutely clear, we do not claim that the latter represent the ultimate mathematical truth, nor do we arXiv:1910.07913v5 [math.LO] 9 Aug 2021 (wish to) downplay the role of representations in third-order arithmetic. -
URYSOHN's THEOREM and TIETZE EXTENSION THEOREM Definition
URYSOHN'S THEOREM AND TIETZE EXTENSION THEOREM Tianlin Liu [email protected] Mathematics Department Jacobs University Bremen Campus Ring 6, 28759, Bremen, Germany Definition 0.1. Let x; y topological space X. We define the following properties of topological space X: ∈ T0: If x y, there is an open set containing x but not y or an open set containing y but not x. ≠ T1: If x y, there is an open set containing y but not x. T2: If x≠ y, there are disjoint open sets U; V with x U and y V . ≠ ∈ T3∈: X is a T1 space, and for any closed set A X and any x AC there are disjoint open sets U; V with x U and A V . ⊂ T4∈: X is a T1 space, and for any disjoint closed∈ sets A, ⊂B in X there are disjoint open sets U, V with A Uand B V . We say a T2 space is a Hausdorff space, a T⊂3 space is⊂ a regular space, a T4 space is a normal space. Definition 0.2. C X; a; b Space of all continuous a; b valued functions on X. ( [ ]) ∶= [ ] Date: February 11, 2016. 1 URYSOHN'S THEOREM AND TIETZE EXTENSION THEOREM 2 Theorem 0.3. (Urysohn's Lemma) Let X be a normal space. If A and B are disjoint closed sets in X, there exists f C X; 0; 1 such that f 0 on A and f 1 on B. Proof. ∈ ( [ ]) = = Step 1: Define a large collection of open sets in X (Lemma 4.14 in [1]) Let D be the set of dyadic rationals in 0; 1 , that is, D 1 1 3 1 3 7 1; 0; 2 ; 4 ; 4 ; 8 ; 8 ; 8 ::: . -
Inspector's Handbook: Subbase Construction
INSPECTOR'S HANDBOOK SUBBASE CONSTRUCTION IOWA STATE HIGHWAY COMMISSION AMES, IOWA 1969 r 17:_:H53 s~sl4 1969 \------ -- "- SUBBASE CONSTRUCTION Walter Schneider Jerry Rodibaugh INTRODUCTION This handbook is an inspector's aid. It was written by two inspectors to bring together a 11 of the most of ten-needed information involved in their work. Much care has been taken to detai I each phase of construction, with particular attention to the r.equire ments and limitations of specifications. All applicable specification interpretations in Instructions to Resident Engineers have been included. The beginning inspector should look to the hand book as a reference for standards of good practice. The Standard.Specifications and Special Provisions should not, however, be overlooked as the basic sources of information on requirements and restrictions concerning workmanship and materials. I . CONTENTS SUBBASE CONSTRUCTION Page 1 Definition 1 Types 1 Figure 1 - Typical Section 1 INSPECTION 1 Plans, Proposals, and Specifications · 1 Preconstruction Detai Is 2 I l. STAKING 2 Purpose 2 Techniques 2 -- SUBGRADE CORRECTION 3 Application 3 Profile and Cross-Section Requirements 4 Figure 2 - Flexible Base Construction 5 INSPECTING DIFFERENT TYPES OF ROLLERS USED IN SUBBASE CONSTRUCTION 5 Tamping Rollers . s· Self-Propelled, Steel-Tired Rollers 5 Self-Propelled and Pull-Type Pneumatic Tire Roi lers 6 Figure 3 - Pneumatic-Tired Roller 6 SPREAD RA TES AND PLAN QUANTITIES 6 Spread Chart 6 Fig.ure 4 - Typical Spr~ad chart 7 Figure 5 - scale Ti ck 8 et -- Granular -
Sufficient Generalities About Topological Vector Spaces
(November 28, 2016) Topological vector spaces Paul Garrett [email protected] http:=/www.math.umn.edu/egarrett/ [This document is http://www.math.umn.edu/~garrett/m/fun/notes 2016-17/tvss.pdf] 1. Banach spaces Ck[a; b] 2. Non-Banach limit C1[a; b] of Banach spaces Ck[a; b] 3. Sufficient notion of topological vector space 4. Unique vectorspace topology on Cn 5. Non-Fr´echet colimit C1 of Cn, quasi-completeness 6. Seminorms and locally convex topologies 7. Quasi-completeness theorem 1. Banach spaces Ck[a; b] We give the vector space Ck[a; b] of k-times continuously differentiable functions on an interval [a; b] a metric which makes it complete. Mere pointwise limits of continuous functions easily fail to be continuous. First recall the standard [1.1] Claim: The set Co(K) of complex-valued continuous functions on a compact set K is complete with o the metric jf − gjCo , with the C -norm jfjCo = supx2K jf(x)j. Proof: This is a typical three-epsilon argument. To show that a Cauchy sequence ffig of continuous functions has a pointwise limit which is a continuous function, first argue that fi has a pointwise limit at every x 2 K. Given " > 0, choose N large enough such that jfi − fjj < " for all i; j ≥ N. Then jfi(x) − fj(x)j < " for any x in K. Thus, the sequence of values fi(x) is a Cauchy sequence of complex numbers, so has a limit 0 0 f(x). Further, given " > 0 choose j ≥ N sufficiently large such that jfj(x) − f(x)j < " . -
Extension of Mappings and Pseudometrics
E extracta mathematicae Vol. 25, N´um.3, 277 { 308 (2010) Extension of Mappings and Pseudometrics M. Huˇsek∗ Charles University, Prague, Sokolovsk´a 83, Czech Republic, and University J.E.Purkynˇe, Ust´ınad´ Labem, Cesk´eml´adeˇzeˇ 8, Czech Republic, Miroslav.Husek@mff.cuni.cz, [email protected] Presented by Gino Tironi Received December 22, 2010 Abstract: The aim of this paper is to show a development of various methods of extensions of mappings and their interrelations. We shall point out some methods and relations entailing more general results than those originally stated. Key words: Extension of function, extension of pseudometric, metric space. AMS Subject Class. (2010): 54C20, 54E35. 1. Introduction 1.1. General remarks. We shall use two main looks at results con- cerning extensions of mappings. The first one is just historical { we shall go through the original methods and divide them into three almost disjoint pro- cedures. The second look goes into deeper analysis of the methods and tries to deduce from them as much as possible. In several cases we shall see that authors proved much more than they formulated and used (see, e.g. Theo- rems 3', 10', 2.6). We shall also describe some elementary relations among various extension results and some elementary procedures allowing to gener- alize extension results for metric spaces to more general ones (see 2.5-2.7, 3.4, Proposition 22, 4.1.3). We shall consider those situations when all (uniformly) continuous map- pings can be extended from closed subsets, excluding cases when extension exists for some of them only or from some more special subsets only. -
Seqgan: Sequence Generative Adversarial Nets with Policy Gradient
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17) SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient Lantao Yu,† Weinan Zhang,†∗ Jun Wang,‡ Yong Yu† †Shanghai Jiao Tong University, ‡University College London {yulantao,wnzhang,yyu}@apex.sjtu.edu.cn, [email protected] Abstract of sequence increases. To address this problem, (Bengio et al. 2015) proposed a training strategy called scheduled sampling As a new way of training generative models, Generative Ad- (SS), where the generative model is partially fed with its own versarial Net (GAN) that uses a discriminative model to guide synthetic data as prefix (observed tokens) rather than the true the training of the generative model has enjoyed considerable success in generating real-valued data. However, it has limi- data when deciding the next token in the training stage. Nev- tations when the goal is for generating sequences of discrete ertheless, (Huszar´ 2015) showed that SS is an inconsistent tokens. A major reason lies in that the discrete outputs from the training strategy and fails to address the problem fundamen- generative model make it difficult to pass the gradient update tally. Another possible solution of the training/inference dis- from the discriminative model to the generative model. Also, crepancy problem is to build the loss function on the entire the discriminative model can only assess a complete sequence, generated sequence instead of each transition. For instance, while for a partially generated sequence, it is non-trivial to in the application of machine translation, a task specific se- balance its current score and the future one once the entire quence score/loss, bilingual evaluation understudy (BLEU) sequence has been generated. -
Simultaneous Extension of Continuous and Uniformly Continuous Functions
SIMULTANEOUS EXTENSION OF CONTINUOUS AND UNIFORMLY CONTINUOUS FUNCTIONS VALENTIN GUTEV Abstract. The first known continuous extension result was obtained by Lebes- gue in 1907. In 1915, Tietze published his famous extension theorem generalis- ing Lebesgue’s result from the plane to general metric spaces. He constructed the extension by an explicit formula involving the distance function on the met- ric space. Thereafter, several authors contributed other explicit extension for- mulas. In the present paper, we show that all these extension constructions also preserve uniform continuity, which answers a question posed by St. Watson. In fact, such constructions are simultaneous for special bounded functions. Based on this, we also refine a result of Dugundji by constructing various continuous (nonlinear) extension operators which preserve uniform continuity as well. 1. Introduction In his 1907 paper [24] on Dirichlet’s problem, Lebesgue showed that for a closed subset A ⊂ R2 and a continuous function ϕ : A → R, there exists a continuous function f : R2 → R with f ↾ A = ϕ. Here, f is commonly called a continuous extension of ϕ, and we also say that ϕ can be extended continuously. In 1915, Tietze [30] generalised Lebesgue’s result for all metric spaces. Theorem 1.1 (Tietze, 1915). If (X,d) is a metric space and A ⊂ X is a closed set, then each bounded continuous function ϕ : A → R can be extended to a continuous function f : X → R. arXiv:2010.02955v1 [math.GN] 6 Oct 2020 Tietze gave two proofs of Theorem 1.1, the second of which was based on the following explicit construction of the extension.