1.1.3 Reminder of Some Simple Topological Concepts Definition 1.1.17
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Basic Properties of Filter Convergence Spaces
Basic Properties of Filter Convergence Spaces Barbel¨ M. R. Stadlery, Peter F. Stadlery;z;∗ yInstitut fur¨ Theoretische Chemie, Universit¨at Wien, W¨ahringerstraße 17, A-1090 Wien, Austria zThe Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA ∗Address for corresponce Abstract. This technical report summarized facts from the basic theory of filter convergence spaces and gives detailed proofs for them. Many of the results collected here are well known for various types of spaces. We have made no attempt to find the original proofs. 1. Introduction Mathematical notions such as convergence, continuity, and separation are, at textbook level, usually associated with topological spaces. It is possible, however, to introduce them in a much more abstract way, based on axioms for convergence instead of neighborhood. This approach was explored in seminal work by Choquet [4], Hausdorff [12], Katˇetov [14], Kent [16], and others. Here we give a brief introduction to this line of reasoning. While the material is well known to specialists it does not seem to be easily accessible to non-topologists. In some cases we include proofs of elementary facts for two reasons: (i) The most basic facts are quoted without proofs in research papers, and (ii) the proofs may serve as examples to see the rather abstract formalism at work. 2. Sets and Filters Let X be a set, P(X) its power set, and H ⊆ P(X). The we define H∗ = fA ⊆ Xj(X n A) 2= Hg (1) H# = fA ⊆ Xj8Q 2 H : A \ Q =6 ;g The set systems H∗ and H# are called the conjugate and the grill of H, respectively. -
7.2 Binary Operators Closure
last edited April 19, 2016 7.2 Binary Operators A precise discussion of symmetry benefits from the development of what math- ematicians call a group, which is a special kind of set we have not yet explicitly considered. However, before we define a group and explore its properties, we reconsider several familiar sets and some of their most basic features. Over the last several sections, we have considered many di↵erent kinds of sets. We have considered sets of integers (natural numbers, even numbers, odd numbers), sets of rational numbers, sets of vertices, edges, colors, polyhedra and many others. In many of these examples – though certainly not in all of them – we are familiar with rules that tell us how to combine two elements to form another element. For example, if we are dealing with the natural numbers, we might considered the rules of addition, or the rules of multiplication, both of which tell us how to take two elements of N and combine them to give us a (possibly distinct) third element. This motivates the following definition. Definition 26. Given a set S,abinary operator ? is a rule that takes two elements a, b S and manipulates them to give us a third, not necessarily distinct, element2 a?b. Although the term binary operator might be new to us, we are already familiar with many examples. As hinted to earlier, the rule for adding two numbers to give us a third number is a binary operator on the set of integers, or on the set of rational numbers, or on the set of real numbers. -
3. Closed Sets, Closures, and Density
3. Closed sets, closures, and density 1 Motivation Up to this point, all we have done is define what topologies are, define a way of comparing two topologies, define a method for more easily specifying a topology (as a collection of sets generated by a basis), and investigated some simple properties of bases. At this point, we will start introducing some more interesting definitions and phenomena one might encounter in a topological space, starting with the notions of closed sets and closures. Thinking back to some of the motivational concepts from the first lecture, this section will start us on the road to exploring what it means for two sets to be \close" to one another, or what it means for a point to be \close" to a set. We will draw heavily on our intuition about n convergent sequences in R when discussing the basic definitions in this section, and so we begin by recalling that definition from calculus/analysis. 1 n Definition 1.1. A sequence fxngn=1 is said to converge to a point x 2 R if for every > 0 there is a number N 2 N such that xn 2 B(x) for all n > N. 1 Remark 1.2. It is common to refer to the portion of a sequence fxngn=1 after some index 1 N|that is, the sequence fxngn=N+1|as a tail of the sequence. In this language, one would phrase the above definition as \for every > 0 there is a tail of the sequence inside B(x)." n Given what we have established about the topological space Rusual and its standard basis of -balls, we can see that this is equivalent to saying that there is a tail of the sequence inside any open set containing x; this is because the collection of -balls forms a basis for the usual topology, and thus given any open set U containing x there is an such that x 2 B(x) ⊆ U. -
What Are Lyapunov Exponents, and Why Are They Interesting?
BULLETIN (New Series) OF THE AMERICAN MATHEMATICAL SOCIETY Volume 54, Number 1, January 2017, Pages 79–105 http://dx.doi.org/10.1090/bull/1552 Article electronically published on September 6, 2016 WHAT ARE LYAPUNOV EXPONENTS, AND WHY ARE THEY INTERESTING? AMIE WILKINSON Introduction At the 2014 International Congress of Mathematicians in Seoul, South Korea, Franco-Brazilian mathematician Artur Avila was awarded the Fields Medal for “his profound contributions to dynamical systems theory, which have changed the face of the field, using the powerful idea of renormalization as a unifying principle.”1 Although it is not explicitly mentioned in this citation, there is a second unify- ing concept in Avila’s work that is closely tied with renormalization: Lyapunov (or characteristic) exponents. Lyapunov exponents play a key role in three areas of Avila’s research: smooth ergodic theory, billiards and translation surfaces, and the spectral theory of 1-dimensional Schr¨odinger operators. Here we take the op- portunity to explore these areas and reveal some underlying themes connecting exponents, chaotic dynamics and renormalization. But first, what are Lyapunov exponents? Let’s begin by viewing them in one of their natural habitats: the iterated barycentric subdivision of a triangle. When the midpoint of each side of a triangle is connected to its opposite vertex by a line segment, the three resulting segments meet in a point in the interior of the triangle. The barycentric subdivision of a triangle is the collection of 6 smaller triangles determined by these segments and the edges of the original triangle: Figure 1. Barycentric subdivision. Received by the editors August 2, 2016. -
Continuous Closure, Natural Closure, and Axes Closure
CONTINUOUS CLOSURE, AXES CLOSURE, AND NATURAL CLOSURE NEIL EPSTEIN AND MELVIN HOCHSTER Abstract. Let R be a reduced affine C-algebra, with corresponding affine algebraic set X.Let (X)betheringofcontinuous(Euclideantopology)C- C valued functions on X.Brennerdefinedthecontinuous closure Icont of an ideal I as I (X) R.Healsointroducedanalgebraicnotionofaxes closure Iax that C \ always contains Icont,andaskedwhethertheycoincide.Weextendthenotion of axes closure to general Noetherian rings, defining f Iax if its image is in 2 IS for every homomorphism R S,whereS is a one-dimensional complete ! seminormal local ring. We also introduce the natural closure I\ of I.Oneof many characterizations is I\ = I+ f R : n>0withf n In+1 .Weshow { 2 9 2 } that I\ Iax,andthatwhencontinuousclosureisdefined,I\ Icont Iax. ✓ ✓ ✓ Under mild hypotheses on the ring, we show that I\ = Iax when I is primary to a maximal ideal, and that if I has no embedded primes, then I = I\ if and only if I = Iax,sothatIcont agrees as well. We deduce that in the @f polynomial ring [x1,...,xn], if f =0atallpointswhereallofthe are C @xi 0, then f ( @f ,..., @f )R.WecharacterizeIcont for monomial ideals in 2 @x1 @xn polynomial rings over C,butweshowthattheinequalitiesI\ Icont and ✓ Icont Iax can be strict for monomial ideals even in dimension 3. Thus, Icont ax✓ and I need not agree, although we prove they are equal in C[x1,x2]. Contents 1. Introduction 2 2. Properties of continuous closure 4 3. Seminormal rings 8 4. Axes closure and one-dimensional seminormal rings 13 5. Special and inner integral closure, and natural closure 18 6. -
Soft Somewhere Dense Sets on Soft Topological Spaces
Commun. Korean Math. Soc. 33 (2018), No. 4, pp. 1341{1356 https://doi.org/10.4134/CKMS.c170378 pISSN: 1225-1763 / eISSN: 2234-3024 SOFT SOMEWHERE DENSE SETS ON SOFT TOPOLOGICAL SPACES Tareq M. Al-shami Abstract. The author devotes this paper to defining a new class of generalized soft open sets, namely soft somewhere dense sets and to in- vestigating its main features. With the help of examples, we illustrate the relationships between soft somewhere dense sets and some celebrated generalizations of soft open sets, and point out that the soft somewhere dense subsets of a soft hyperconnected space coincide with the non-null soft β-open sets. Also, we give an equivalent condition for the soft cs- dense sets and verify that every soft set is soft somewhere dense or soft cs-dense. We show that a collection of all soft somewhere dense subsets of a strongly soft hyperconnected space forms a soft filter on the universe set, and this collection with a non-null soft set form a soft topology on the universe set as well. Moreover, we derive some important results such as the property of being a soft somewhere dense set is a soft topological property and the finite product of soft somewhere dense sets is soft some- where dense. In the end, we point out that the number of soft somewhere dense subsets of infinite soft topological space is infinite, and we present some results which associate soft somewhere dense sets with some soft topological concepts such as soft compact spaces and soft subspaces. -
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 . -
CLOSURES of EXPONENTIAL FAMILIES 3 the (Common) Convex Support of the P.M.’S in E Has Positive Probability Under These P.M.’S, by [7], Remark 3
The Annals of Probability 2005, Vol. 33, No. 2, 582–600 DOI: 10.1214/009117904000000766 c Institute of Mathematical Statistics, 2005 CLOSURES OF EXPONENTIAL FAMILIES1 By Imre Csiszar´ and Frantiˇsek Matu´ˇs Hungarian Academy of Sciences and Academy of Sciences of the Czech Republic The variation distance closure of an exponential family with a convex set of canonical parameters is described, assuming no regu- larity conditions. The tools are the concepts of convex core of a mea- sure and extension of an exponential family, introduced previously by the authors, and a new concept of accessible faces of a convex set. Two other closures related to the information divergence are also characterized. 1. Introduction. Exponential families of probability measures (p.m.’s) include many of the parametric families frequently used in statistics, proba- bility and information theory. Their mathematical theory has been worked out to a considerable extent [1, 2, 3, 11]. Although limiting considerations are important and do appear in the literature, less attention has been paid to determining closures of exponential families. For families supported by a finite or countable set, closures were consid- ered in [1], pages 154–156, and [2], pages 191–201, respectively, the latter with regularity conditions. In the general case, different closure concepts come into account. Our main result, Theorem 2 in Section 3, determines the closure in variation distance (variation closure) of a full exponential fam- ily and, more generally, of any subfamily with a convex set of canonical parameters. Weak closures appear much harder to describe in general, but Theorem 1 in Section 3 is a step in that direction. -
Topology Via Higher-Order Intuitionistic Logic
Topology via higher-order intuitionistic logic Working version of 18th March 2004 These evolving notes will eventually be used to write a paper Mart´ınEscard´o Abstract When excluded middle fails, one can define a non-trivial topology on the one-point set, provided one doesn’t require all unions of open sets to be open. Technically, one obtains a subset of the subobject classifier, known as a dom- inance, which is to be thought of as a “Sierpinski set” that behaves as the Sierpinski space in classical topology. This induces topologies on all sets, rendering all functions continuous. Because virtually all theorems of classical topology require excluded middle (and even choice), it would be useless to reduce other topological notions to the notion of open set in the usual way. So, for example, our synthetic definition of compactness for a set X says that, for any Sierpinski-valued predicate p on X, the truth value of the statement “for all x ∈ X, p(x)” lives in the Sierpinski set. Similarly, other topological notions are defined by logical statements. We show that the proposed synthetic notions interact in the expected way. Moreover, we show that they coincide with the usual notions in cer- tain classical topological models, and we look at their interpretation in some computational models. 1 Introduction For suitable topologies, computable functions are continuous, and semidecidable properties of their inputs/outputs are open, but the converses of these two state- ments fail. Moreover, although semidecidable sets are closed under the formation of finite intersections and recursively enumerable unions, they fail to form the open sets of a topology in a literal sense. -
Arxiv:2011.11669V2 [Math.LO] 18 Jul 2021 on Piecewise Hyperdefinable Groups
On piecewise hyperdefinable groups Arturo Rodr´ıguez Fanlo July 20, 2021 Abstract The aim of this paper is to generalize and improve two of the main model-theoretic results of “Stable group theory and approximate sub- groups” [11] to the context of piecewise hyperdefinable sets. The first one is the existence of Lie models. The second one is the Stabilizer The- orem. In the process, a systematic study of the structure of piecewise hyperdefinable sets is developed. In particular, we show the most signifi- cant properties of their logic topologies. Acknowledgements I want to specially thank my supervisor, Prof. Ehud Hrushovski, for all his help, ideas and suggestions: without him it would not have been possible to do this. I also want to thank my PhD mate, Alex Chevalier, for his helpful reviews and comments. Introduction Various enlightening results were found in [11] with significant consequences on arXiv:2011.11669v2 [math.LO] 18 Jul 2021 model theory and additive combinatorics. Two of them are particularly relevant: the Stabilizer Theorem and the existence of Lie models. The Stabilizer Theorem [11, Theorem 3.5], already improved in [16, Theorem 2.12], was originally itself a generalization of the classical Stabilizer Theorem for stable and simple groups (see for example [21, Section 4.5]) changing the stability and simplicity hypotheses by some kind of measure-theoretic ones. Here, we extend that theorem to piecewise hyperdefinable groups in section 3. All this is easily done once we define properly dividing and forking for piecewise hyperdefinable sets. The proof we give is not new: it is essentially a combination of the original proof of [11] and the improved version of [16], adapted to the context of piecewise hyperdefinable groups. -
Nonlinear Spectral Theory Henning Wunderlich
Nonlinear Spectral Theory Henning Wunderlich Dr. Henning Wunderlich, Frankfurt, Germany E-mail address: [email protected] 1991 Mathematics Subject Classification. 46-XX,47-XX I would like to sincerely thank Prof. Dr. Delio Mugnolo for his valuable advice and support during the preparation of this thesis. Contents Introduction 5 Chapter 1. Spaces 7 1. Topological Spaces 7 2. Uniform Spaces 11 3. Metric Spaces 14 4. Vector Spaces 15 5. Topological Vector Spaces 18 6. Locally-Convex Spaces 21 7. Bornological Spaces 23 8. Barreled Spaces 23 9. Metric Vector Spaces 29 10. Normed Vector Spaces 30 11. Inner-Product Vector Spaces 31 12. Examples 31 Chapter 2. Fixed Points 39 1. Schauder-Tychonoff 39 2. Monotonic Operators 43 3. Dugundji and Quasi-Extensions 51 4. Measures of Noncompactness 53 5. Michael Selection 58 Chapter 3. Existing Spectra 59 1. Linear Spectrum and Properties 59 2. Spectra Under Consideration 63 3. Restriction to Linear Operators 76 4. Nonemptyness 77 5. Closedness 78 6. Boundedness 82 7. Upper Semicontinuity 84 Chapter 4. Applications 87 1. Nemyckii Operator 87 2. p-Laplace Operator 88 3. Navier-Stokes Equations 92 Bibliography 97 Index 101 3 Introduction The term Spectral Theory was coined by David Hilbert in his studies of qua- dratic forms in infinitely-many variables. This theory evolved into a beautiful blend of Linear Algebra and Analysis, with striking applications in different fields of sci- ence. For example, the formulation of the calculus of Quantum Mechanics (e.g., POVM) would not have been possible without such a (Linear) Spectral Theory at hand.