A Uniform Bijection Between Nonnesting and Noncrossing Partitions
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Fuzzy Logic : Introduction
Fuzzy Logic : Introduction Debasis Samanta IIT Kharagpur [email protected] 07.01.2015 Debasis Samanta (IIT Kharagpur) Soft Computing Applications 07.01.2015 1 / 69 What is Fuzzy logic? Fuzzy logic is a mathematical language to express something. This means it has grammar, syntax, semantic like a language for communication. There are some other mathematical languages also known • Relational algebra (operations on sets) • Boolean algebra (operations on Boolean variables) • Predicate logic (operations on well formed formulae (wff), also called predicate propositions) Fuzzy logic deals with Fuzzy set. Debasis Samanta (IIT Kharagpur) Soft Computing Applications 07.01.2015 2 / 69 A brief history of Fuzzy Logic First time introduced by Lotfi Abdelli Zadeh (1965), University of California, Berkley, USA (1965). He is fondly nick-named as LAZ Debasis Samanta (IIT Kharagpur) Soft Computing Applications 07.01.2015 3 / 69 A brief history of Fuzzy logic 1 Dictionary meaning of fuzzy is not clear, noisy etc. Example: Is the picture on this slide is fuzzy? 2 Antonym of fuzzy is crisp Example: Are the chips crisp? Debasis Samanta (IIT Kharagpur) Soft Computing Applications 07.01.2015 4 / 69 Example : Fuzzy logic vs. Crisp logic Yes or No Crisp answer True or False Milk Yes Water A liquid Crisp Coca No Spite Is the liquid colorless? Debasis Samanta (IIT Kharagpur) Soft Computing Applications 07.01.2015 5 / 69 Example : Fuzzy logic vs. Crisp logic May be May not be Fuzzy answer Absolutely Partially etc Debasis Samanta (IIT Kharagpur) Soft Computing Applications -
Mathematics Is Megethology
17 Mathematics is megethology Mereology is the theory of the relation of part to whole, and kindred notions. Megethology is the result of adding plural quantification, as advocated by George Boolos in [1] and [2], to the language of mere ology. It is so-called because it turns out to have enough expressive power to let us express interesting hypotheses about the size of Reality. It also has the power, as John P. Burgess and A. P. Hazen have shown in [3], to simulate quantification over relations. It is generally accepted that mathematics reduces to set theory. In my book Parts of Classes, [6], I argued that set theory in turn reduces, with the aid of mereology, to the theory of singleton functions. I also argued (somewhat reluctantly) for a 'structuralist' approach to the theory of singleton functions. We need not think we have somehow achieved a primitive grasp of some one special singleton function. Rather, we can take the theory of singleton functions, and hence set theory, and hence mathematics, to consist of generalisations about all singleton functions. We need only assume, to avoid vacuity, that there exists at least one singleton function. But we need not assume even that. For it now turns out that if the size of Reality is right, there must exist a singleton function. All we need as a foundation for mathematics, apart from the framework of megethology, are some hypotheses about the size of Reality. First published in this form in Philosophia Mathematila 3 (1993), 3--23. This paper consists in part of near-verbatim excerpts from David Lewis, Parts of Classes (Oxford, Blackwell, 1991). -
Definiteness and Determinacy
Linguistics and Philosophy manuscript No. (will be inserted by the editor) Definiteness and Determinacy Elizabeth Coppock · David Beaver the date of receipt and acceptance should be inserted later Abstract This paper distinguishes between definiteness and determinacy. Defi- niteness is seen as a morphological category which, in English, marks a (weak) uniqueness presupposition, while determinacy consists in denoting an individual. Definite descriptions are argued to be fundamentally predicative, presupposing uniqueness but not existence, and to acquire existential import through general type-shifting operations that apply not only to definites, but also indefinites and possessives. Through these shifts, argumental definite descriptions may become either determinate (and thus denote an individual) or indeterminate (functioning as an existential quantifier). The latter option is observed in examples like `Anna didn't give the only invited talk at the conference', which, on its indeterminate reading, implies that there is nothing in the extension of `only invited talk at the conference'. The paper also offers a resolution of the issue of whether posses- sives are inherently indefinite or definite, suggesting that, like indefinites, they do not mark definiteness lexically, but like definites, they typically yield determinate readings due to a general preference for the shifting operation that produces them. Keywords definiteness · descriptions · possessives · predicates · type-shifting We thank Dag Haug, Reinhard Muskens, Luca Crniˇc,Cleo Condoravdi, Lucas -
TOPOLOGY and ITS APPLICATIONS the Number of Complements in The
TOPOLOGY AND ITS APPLICATIONS ELSEVIER Topology and its Applications 55 (1994) 101-125 The number of complements in the lattice of topologies on a fixed set Stephen Watson Department of Mathematics, York Uniuersity, 4700 Keele Street, North York, Ont., Canada M3J IP3 (Received 3 May 1989) (Revised 14 November 1989 and 2 June 1992) Abstract In 1936, Birkhoff ordered the family of all topologies on a set by inclusion and obtained a lattice with 1 and 0. The study of this lattice ought to be a basic pursuit both in combinatorial set theory and in general topology. In this paper, we study the nature of complementation in this lattice. We say that topologies 7 and (T are complementary if and only if 7 A c = 0 and 7 V (T = 1. For simplicity, we call any topology other than the discrete and the indiscrete a proper topology. Hartmanis showed in 1958 that any proper topology on a finite set of size at least 3 has at least two complements. Gaifman showed in 1961 that any proper topology on a countable set has at least two complements. In 1965, Steiner showed that any topology has a complement. The question of the number of distinct complements a topology on a set must possess was first raised by Berri in 1964 who asked if every proper topology on an infinite set must have at least two complements. In 1969, Schnare showed that any proper topology on a set of infinite cardinality K has at least K distinct complements and at most 2” many distinct complements. -
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. -
Solutions Are Available
Math 436 Introduction to Topology Spring 2018 Examination 1 1. Define (a) the concept of the closure of a set, and (b) the concept of a basis for a given topology. Solution. The closure of a set A can be defined as the smallest closed set containing A, or as the intersection of all closed sets containing A, or as the union of A with the set of limit points of A. A basis for a given topology is a subset of such that every element of can be obtained as a union of elements of . In alternative language, a basis for a given topology is a collection of open sets such that every open set can be obtained by taking a union of some open sets belonging to the basis. 2. Give an example of a separable Hausdorff topological space .X; / with the property that X has no limit points. Solution. A point x is a limit point of X when every neighborhood of x intersects X ⧵ ^x`. This property evidently holds unless the singleton set ^x` is an open set. Saying that X has no limit points is equivalent to saying that every singleton set is an open set. In other words, the topology on X is the discrete topology. The discrete topology is automatically Hausdorff, for if x and y are distinct points, then the sets ^x` and ^y` are disjoint open sets that separate x from y. A minimal basis for the discrete topology consists of all singletons, so the discrete topology is separable if and only if the space X is countable. -
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 -
Ring (Mathematics) 1 Ring (Mathematics)
Ring (mathematics) 1 Ring (mathematics) In mathematics, a ring is an algebraic structure consisting of a set together with two binary operations usually called addition and multiplication, where the set is an abelian group under addition (called the additive group of the ring) and a monoid under multiplication such that multiplication distributes over addition.a[›] In other words the ring axioms require that addition is commutative, addition and multiplication are associative, multiplication distributes over addition, each element in the set has an additive inverse, and there exists an additive identity. One of the most common examples of a ring is the set of integers endowed with its natural operations of addition and multiplication. Certain variations of the definition of a ring are sometimes employed, and these are outlined later in the article. Polynomials, represented here by curves, form a ring under addition The branch of mathematics that studies rings is known and multiplication. as ring theory. Ring theorists study properties common to both familiar mathematical structures such as integers and polynomials, and to the many less well-known mathematical structures that also satisfy the axioms of ring theory. The ubiquity of rings makes them a central organizing principle of contemporary mathematics.[1] Ring theory may be used to understand fundamental physical laws, such as those underlying special relativity and symmetry phenomena in molecular chemistry. The concept of a ring first arose from attempts to prove Fermat's last theorem, starting with Richard Dedekind in the 1880s. After contributions from other fields, mainly number theory, the ring notion was generalized and firmly established during the 1920s by Emmy Noether and Wolfgang Krull.[2] Modern ring theory—a very active mathematical discipline—studies rings in their own right. -
Arxiv:Cs/0410053V1 [Cs.DB] 20 Oct 2004 Oe.Teesrcue R Xeso of Extension Are Structures These Model
An Extended Generalized Disjunctive Paraconsistent Data Model for Disjunctive Information Haibin Wang, Hao Tian and Rajshekhar Sunderraman Department of Computer Science Georgia State University Atlanta, GA 30302 email: {hwang17,htian1}@student.gsu.edu, [email protected] Abstract. This paper presents an extension of generalized disjunctive paraconsistent relational data model in which pure disjunctive positive and negative information as well as mixed disjunctive positive and nega- tive information can be represented explicitly and manipulated. We con- sider explicit mixed disjunctive information in the context of disjunctive databases as there is an interesting interplay between these two types of information. Extended generalized disjunctive paraconsistent relation is introduced as the main structure in this model. The relational algebra is appropriately generalized to work on extended generalized disjunctive paraconsistent relations and their correctness is established. 1 Introduction Relational data model was proposed by Ted Codd’s pioneering paper [1]. Re- lational data model is value-oriented and has rich set of simple, but powerful algebraic operators. Moreover, a strong theoretical foundation for the model is provided by the classical first-order logic [2]. This combination of a respectable theoretical platform, ease of implementation and the practicality of the model resulted in its immediate success, and the model has enjoyed being used by many database management systems. One limitation of the relational data model, however, is its lack of applicabil- arXiv:cs/0410053v1 [cs.DB] 20 Oct 2004 ity to nonclassical situations. These are situations involving incomplete or even inconsistent information. Several types of incomplete information have been extensively studied in the past such as null values [3,4,5], partial values [6], fuzzy and uncertain values [7,8], and disjunctive information [9,10,11]. -
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 -
Naïve Set Theory Basic Definitions Naïve Set Theory Is the Non-Axiomatic Treatment of Set Theory
Naïve Set Theory Basic Definitions Naïve set theory is the non-axiomatic treatment of set theory. In the axiomatic treatment, which we will only allude to at times, a set is an undefined term. For us however, a set will be thought of as a collection of some (possibly none) objects. These objects are called the members (or elements) of the set. We use the symbol "∈" to indicate membership in a set. Thus, if A is a set and x is one of its members, we write x ∈ A and say "x is an element of A" or "x is in A" or "x is a member of A". Note that "∈" is not the same as the Greek letter "ε" epsilon. Basic Definitions Sets can be described notationally in many ways, but always using the set brackets "{" and "}". If possible, one can just list the elements of the set: A = {1,3, oranges, lions, an old wad of gum} or give an indication of the elements: ℕ = {1,2,3, ... } ℤ = {..., -2,-1,0,1,2, ...} or (most frequently in mathematics) using set-builder notation: S = {x ∈ ℝ | 1 < x ≤ 7 } or {x ∈ ℝ : 1 < x ≤ 7 } which is read as "S is the set of real numbers x, such that x is greater than 1 and less than or equal to 7". In some areas of mathematics sets may be denoted by special notations. For instance, in analysis S would be written (1,7]. Basic Definitions Note that sets do not contain repeated elements. An element is either in or not in a set, never "in the set 5 times" for instance.