TOPOLOGY and ITS APPLICATIONS the Number of Complements in The
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The Topology of Subsets of Rn
Lecture 1 The topology of subsets of Rn The basic material of this lecture should be familiar to you from Advanced Calculus courses, but we shall revise it in detail to ensure that you are comfortable with its main notions (the notions of open set and continuous map) and know how to work with them. 1.1. Continuous maps “Topology is the mathematics of continuity” Let R be the set of real numbers. A function f : R −→ R is called continuous at the point x0 ∈ R if for any ε> 0 there exists a δ> 0 such that the inequality |f(x0) − f(x)| < ε holds for all x ∈ R whenever |x0 − x| <δ. The function f is called contin- uous if it is continuous at all points x ∈ R. This is basic one-variable calculus. n Let R be n-dimensional space. By Or(p) denote the open ball of radius r> 0 and center p ∈ Rn, i.e., the set n Or(p) := {q ∈ R : d(p, q) < r}, where d is the distance in Rn. A function f : Rn −→ R is called contin- n uous at the point p0 ∈ R if for any ε> 0 there exists a δ> 0 such that f(p) ∈ Oε(f(p0)) for all p ∈ Oδ(p0). The function f is called continuous if it is continuous at all points p ∈ Rn. This is (more advanced) calculus in several variables. A set G ⊂ Rn is called open in Rn if for any point g ∈ G there exists a n δ> 0 such that Oδ(g) ⊂ G. -
Determinacy in Linear Rational Expectations Models
Journal of Mathematical Economics 40 (2004) 815–830 Determinacy in linear rational expectations models Stéphane Gauthier∗ CREST, Laboratoire de Macroéconomie (Timbre J-360), 15 bd Gabriel Péri, 92245 Malakoff Cedex, France Received 15 February 2002; received in revised form 5 June 2003; accepted 17 July 2003 Available online 21 January 2004 Abstract The purpose of this paper is to assess the relevance of rational expectations solutions to the class of linear univariate models where both the number of leads in expectations and the number of lags in predetermined variables are arbitrary. It recommends to rule out all the solutions that would fail to be locally unique, or equivalently, locally determinate. So far, this determinacy criterion has been applied to particular solutions, in general some steady state or periodic cycle. However solutions to linear models with rational expectations typically do not conform to such simple dynamic patterns but express instead the current state of the economic system as a linear difference equation of lagged states. The innovation of this paper is to apply the determinacy criterion to the sets of coefficients of these linear difference equations. Its main result shows that only one set of such coefficients, or the corresponding solution, is locally determinate. This solution is commonly referred to as the fundamental one in the literature. In particular, in the saddle point configuration, it coincides with the saddle stable (pure forward) equilibrium trajectory. © 2004 Published by Elsevier B.V. JEL classification: C32; E32 Keywords: Rational expectations; Selection; Determinacy; Saddle point property 1. Introduction The rational expectations hypothesis is commonly justified by the fact that individual forecasts are based on the relevant theory of the economic system. -
180: Counting Techniques
180: Counting Techniques In the following exercise we demonstrate the use of a few fundamental counting principles, namely the addition, multiplication, complementary, and inclusion-exclusion principles. While none of the principles are particular complicated in their own right, it does take some practice to become familiar with them, and recognise when they are applicable. I have attempted to indicate where alternate approaches are possible (and reasonable). Problem: Assume n ≥ 2 and m ≥ 1. Count the number of functions f :[n] ! [m] (i) in total. (ii) such that f(1) = 1 or f(2) = 1. (iii) with minx2[n] f(x) ≤ 5. (iv) such that f(1) ≥ f(2). (v) that are strictly increasing; that is, whenever x < y, f(x) < f(y). (vi) that are one-to-one (injective). (vii) that are onto (surjective). (viii) that are bijections. (ix) such that f(x) + f(y) is even for every x; y 2 [n]. (x) with maxx2[n] f(x) = minx2[n] f(x) + 1. Solution: (i) A function f :[n] ! [m] assigns for every integer 1 ≤ x ≤ n an integer 1 ≤ f(x) ≤ m. For each integer x, we have m options. As we make n such choices (independently), the total number of functions is m × m × : : : m = mn. (ii) (We assume n ≥ 2.) Let A1 be the set of functions with f(1) = 1, and A2 the set of functions with f(2) = 1. Then A1 [ A2 represents those functions with f(1) = 1 or f(2) = 1, which is precisely what we need to count. We have jA1 [ A2j = jA1j + jA2j − jA1 \ A2j. -
Math 501. Homework 2 September 15, 2009 ¶ 1. Prove Or
Math 501. Homework 2 September 15, 2009 ¶ 1. Prove or provide a counterexample: (a) If A ⊂ B, then A ⊂ B. (b) A ∪ B = A ∪ B (c) A ∩ B = A ∩ B S S (d) i∈I Ai = i∈I Ai T T (e) i∈I Ai = i∈I Ai Solution. (a) True. (b) True. Let x be in A ∪ B. Then x is in A or in B, or in both. If x is in A, then B(x, r) ∩ A , ∅ for all r > 0, and so B(x, r) ∩ (A ∪ B) , ∅ for all r > 0; that is, x is in A ∪ B. For the reverse containment, note that A ∪ B is a closed set that contains A ∪ B, there fore, it must contain the closure of A ∪ B. (c) False. Take A = (0, 1), B = (1, 2) in R. (d) False. Take An = [1/n, 1 − 1/n], n = 2, 3, ··· , in R. (e) False. ¶ 2. Let Y be a subset of a metric space X. Prove that for any subset S of Y, the closure of S in Y coincides with S ∩ Y, where S is the closure of S in X. ¶ 3. (a) If x1, x2, x3, ··· and y1, y2, y3, ··· both converge to x, then the sequence x1, y1, x2, y2, x3, y3, ··· also converges to x. (b) If x1, x2, x3, ··· converges to x and σ : N → N is a bijection, then xσ(1), xσ(2), xσ(3), ··· also converges to x. (c) If {xn} is a sequence that does not converge to y, then there is an open ball B(y, r) and a subsequence of {xn} outside B(y, r). -
Isolated Points, Duality and Residues
JOURNAL OF PURE AND APPLIED ALGEBRA Journal of Pure and Applied Algebra 117 % 118 (1997) 469-493 Isolated points, duality and residues B . Mourrain* INDIA, Projet SAFIR, 2004 routes des Lucioles, BP 93, 06902 Sophia-Antipolir, France This work is dedicated to the memory of J. Morgenstem. Abstract In this paper, we are interested in the use of duality in effective computations on polynomials. We represent the elements of the dual of the algebra R of polynomials over the field K as formal series E K[[a]] in differential operators. We use the correspondence between ideals of R and vector spaces of K[[a]], stable by derivation and closed for the (a)-adic topology, in order to construct the local inverse system of an isolated point. We propose an algorithm, which computes the orthogonal D of the primary component of this isolated point, by integration of polynomials in the dual space K[a], with good complexity bounds. Then we apply this algorithm to the computation of local residues, the analysis of real branches of a locally complete intersection curve, the computation of resultants of homogeneous polynomials. 0 1997 Elsevier Science B.V. 1991 Math. Subj. Class.: 14B10, 14Q20, 15A99 1. Introduction Considering polynomials as algorithms (that compute a value at a given point) in- stead of lists of terms, has lead to recent interesting developments, both from a practical and a theoretical point of view. In these approaches, evaluations at points play an im- portant role. We would like to pursue this idea further and to study evaluations by themselves. -
Math 3283W: Solutions to Skills Problems Due 11/6 (13.7(Abc)) All
Math 3283W: solutions to skills problems due 11/6 (13.7(abc)) All three statements can be proven false using counterexamples that you studied on last week's skills homework. 1 (a) Let S = f n jn 2 Ng. Every point of S is an isolated point, since given 1 1 1 n 2 S, there exists a neighborhood N( n ; ") of n that contains no point of S 1 1 1 different from n itself. (We could, for example, take " = n − n+1 .) Thus the set P of isolated points of S is just S. But P = S is not closed, since 0 is a boundary point of S which nevertheless does not belong to S (every neighbor- 1 hood of 0 contains not only 0 but, by the Archimedean property, some n , thus intersects both S and its complement); if S were closed, it would contain all of its boundary points. (b) Let S = N. Then (as you saw on the last skills homework) int(S) = ;. But ; is closed, so its closure is itself; that is, cl(int(S)) = cl(;) = ;, which is certainly not the same thing as S. (c) Let S = R n N. The closure of S is all of R, since every natural number n is a boundary point of R n N and thus belongs to its closure. But R is open, so its interior is itself; that is, int(cl(S)) = int(R) = R, which is certainly not the same thing as S. (13.10) Suppose that x is an isolated point of S, and let N = (x − "; x + ") (for some " > 0) be an arbitrary neighborhood of x. -
Set Difference and Symmetric Difference of Fuzzy Sets
Preliminaries Symmetric Dierence Set Dierence and Symmetric Dierence of Fuzzy Sets N.R. Vemuri A.S. Hareesh M.S. Srinath Department of Mathematics Indian Institute of Technology, Hyderabad and Department of Mathematics and Computer Science Sri Sathya Sai Institute of Higher Learning, India Fuzzy Sets Theory and Applications 2014, Liptovský Ján, Slovak Republic Vemuri, Sai Hareesh & Srinath Symmetric Dierence Preliminaries Introduction Symmetric Dierence Earlier work Outline 1 Preliminaries Introduction Earlier work 2 Symmetric Dierence Denition Examples Properties Applications Future Work References Vemuri, Sai Hareesh & Srinath Symmetric Dierence Preliminaries Introduction Symmetric Dierence Earlier work Classical set theory Set operations Union- [ Intersection - \ Complement - c Dierence -n Symmetric dierence - ∆ .... Vemuri, Sai Hareesh & Srinath Symmetric Dierence Preliminaries Introduction Symmetric Dierence Earlier work Classical set theory Set operations Union- [ Intersection - \ Complement - c Dierence -n Symmetric dierence - ∆ .... Vemuri, Sai Hareesh & Srinath Symmetric Dierence Preliminaries Introduction Symmetric Dierence Earlier work Classical set theory Set operations Union- [ Intersection - \ Complement - c Dierence -n Symmetric dierence - ∆ .... Vemuri, Sai Hareesh & Srinath Symmetric Dierence Preliminaries Introduction Symmetric Dierence Earlier work Classical set theory Set operations Union- [ Intersection - \ Complement - c Dierence -n Symmetric dierence - ∆ .... Vemuri, Sai Hareesh & Srinath Symmetric Dierence Preliminaries -
E. A. Emerson and C. S. Jutla, Tree Automata, Mu-Calculus, And
Tree Automata MuCalculus and Determinacy Extended Abstract EA Emerson and CS Jutla The University of Texas at Austin IBM TJ Watson Research Center Abstract that tree automata are closed under disjunction pro jection and complementation While the rst two We show that the prop ositional MuCalculus is eq are rather easy the pro of of Rabins Complemen uivalent in expressivepower to nite automata on in tation Lemma is extraordinarily complex and di nite trees Since complementation is trivial in the Mu cult Because of the imp ortance of the Complemen Calculus our equivalence provides a radically sim tation Lemma a numb er of authors have endeavored plied alternative pro of of Rabins complementation and continue to endeavor to simplify the argument lemma for tree automata which is the heart of one HR GH MS Mu Perhaps the b est of the deep est decidability results We also showhow known of these is the imp ortant work of Gurevich MuCalculus can b e used to establish determinacy of and Harrington GH which attacks the problem innite games used in earlier pro ofs of complementa from the standp oint of determinacy of innite games tion lemma and certain games used in the theory of While the presentation is brief the argument is still online algorithms extremely dicult and is probably b est appreciated Intro duction y the page supplementofMonk when accompanied b Mon We prop ose the prop ositional Mucalculus as a uniform framework for understanding and simplify In this pap er we present a new enormously sim ing the imp ortant and technically challenging -
Solutions to Problem Set 4: Connectedness
Solutions to Problem Set 4: Connectedness Problem 1 (8). Let X be a set, and T0 and T1 topologies on X. If T0 ⊂ T1, we say that T1 is finer than T0 (and that T0 is coarser than T1). a. Let Y be a set with topologies T0 and T1, and suppose idY :(Y; T1) ! (Y; T0) is continuous. What is the relationship between T0 and T1? Justify your claim. b. Let Y be a set with topologies T0 and T1 and suppose that T0 ⊂ T1. What does connectedness in one topology imply about connectedness in the other? c. Let Y be a set with topologies T0 and T1 and suppose that T0 ⊂ T1. What does one topology being Hausdorff imply about the other? d. Let Y be a set with topologies T0 and T1 and suppose that T0 ⊂ T1. What does convergence of a sequence in one topology imply about convergence in the other? Solution 1. a. By definition, idY is continuous if and only if preimages of open subsets of (Y; T0) are open subsets of (Y; T1). But, the preimage of U ⊂ Y is U itself. Hence, the map is continuous if and only if open subsets of (Y; T0) are open in (Y; T1), i.e. T0 ⊂ T1, i.e. T1 is finer than T0. ` b. If Y is disconnected in T0, it can be written as Y = U V , where U; V 2 T0 are non-empty. Then U and V are also open in T1; hence, Y is disconnected in T1 too. Thus, connectedness in T1 imply connectedness in T0. -
Natural Topology
Natural Topology Frank Waaldijk ú —with great support from Wim Couwenberg ‡ ú www.fwaaldijk.nl/mathematics.html ‡ http://members.chello.nl/ w.couwenberg ∼ Preface to the second edition In the second edition, we have rectified some omissions and minor errors from the first edition. Notably the composition of natural morphisms has now been properly detailed, as well as the definition of (in)finite-product spaces. The bibliography has been updated (but remains quite incomplete). We changed the names ‘path morphism’ and ‘path space’ to ‘trail morphism’ and ‘trail space’, because the term ‘path space’ already has a well-used meaning in general topology. Also, we have strengthened the part of applied mathematics (the APPLIED perspective). We give more detailed representations of complete metric spaces, and show that natural morphisms are efficient and ubiquitous. We link the theory of star-finite metric developments to efficient computing with morphisms. We hope that this second edition thus provides a unified frame- work for a smooth transition from theoretical (constructive) topology to ap- plied mathematics. For better readability we have changed the typography. The Computer Mod- ern fonts have been replaced by the Arev Sans fonts. This was no small operation (since most of the symbol-with-sub/superscript configurations had to be redesigned) but worthwhile, we believe. It would be nice if more fonts become available for LATEX, the choice at this moment is still very limited. (the author, 14 October 2012) Copyright © Frank Arjan Waaldijk, 2011, 2012 Published by the Brouwer Society, Nijmegen, the Netherlands All rights reserved First edition, July 2011 Second edition, October 2012 Cover drawing ocho infinito xxiii by the author (‘ocho infinito’ in co-design with Wim Couwenberg) Summary We develop a simple framework called ‘natural topology’, which can serve as a theoretical and applicable basis for dealing with real-world phenom- ena. -
Categories of Certain Minimal Topological Spaces
CATEGORIES OF CERTAIN MINIMAL TOPOLOGICAL SPACES MANUEL P. BERRI1 (received 9 September 1963) The main purpose of this paper is to discuss the categories of the minimal topological spaces investigated in [1], [2], [7], and [8]. After these results are given, an application will be made to answer the following question: If 2 is the lattice of topologies on a set X and % is a Hausdorff (or regular, or completely regular, or normal, or locally compact) topology does there always exist a minimal Hausdorff (or minimal regular, or minimal com- pletely regular, or minimal normal, or minimal locally compact) topology weaker than %1 The terminology used in this paper, in general, will be that found in Bourbaki: [3], [4], and [5]. Specifically, Tx and Fre"chet spaces are the same; T2 and Hausdorff spaces are the same; regular spaces, completely regular spaces, normal spaces, locally compact spaces, and compact spaces all satisfy the Hausdorff separation axiom. An open (closed) filter-base is one composed exclusively of open (closed) sets. A regular filter-base is an open filter-base which is equivalent to a closed filter-base. Finally in the lattice of topologies on a given set X, a topology is said to be minimal with respect to some property P (or is said to be a minimal P-topology) if there exists no other weaker (= smaller, coarser) topology on X with property P. The following theorems which characterize minimal Hausdorff topo- logies and minimal regular topologies are proved in [1], [2], and [5]. THEOREM 1. A Hausdorff space is minimal Hausdorff if, and only if, (i) every open filter-base has an adherent point; (ii) if such an adherent point is unique, then the filter-base converges to it. -
A First Course in Topology: Explaining Continuity (For Math 112, Spring 2005)
A FIRST COURSE IN TOPOLOGY: EXPLAINING CONTINUITY (FOR MATH 112, SPRING 2005) PAUL BANKSTON CONTENTS (1) Epsilons and Deltas ................................... .................2 (2) Distance Functions in Euclidean Space . .....7 (3) Metrics and Metric Spaces . ..........10 (4) Some Topology of Metric Spaces . ..........15 (5) Topologies and Topological Spaces . ...........21 (6) Comparison of Topologies on a Set . ............27 (7) Bases and Subbases for Topologies . .........31 (8) Homeomorphisms and Topological Properties . ........39 (9) The Basic Lower-Level Separation Axioms . .......46 (10) Convergence ........................................ ..................52 (11) Connectedness . ..............60 (12) Path Connectedness . ............68 (13) Compactness ........................................ .................71 (14) Product Spaces . ..............78 (15) Quotient Spaces . ..............82 (16) The Basic Upper-Level Separation Axioms . .......86 (17) Some Countability Conditions . .............92 1 2 PAUL BANKSTON (18) Further Reading . ...............97 TOPOLOGY 3 1. Epsilons and Deltas In this course we take the overarching view that the mathematical study called topology grew out of an attempt to make precise the notion of continuous function in mathematics. This is one of the most difficult concepts to get across to beginning calculus students, not least because it took centuries for mathematicians themselves to get it right. The intuitive idea is natural enough, and has been around for at least four hundred years. The mathematically precise formulation dates back only to the ninteenth century, however. This is the one involving those pesky epsilons and deltas, the one that leaves most newcomers completely baffled. Why, many ask, do we even bother with this confusing definition, when there is the original intuitive one that makes perfectly good sense? In this introductory section I hope to give a believable answer to this quite natural question.