Advanced Discrete Mathematics Mm-504 &
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On Scattered Convex Geometries
ON SCATTERED CONVEX GEOMETRIES KIRA ADARICHEVA AND MAURICE POUZET Abstract. A convex geometry is a closure space satisfying the anti-exchange axiom. For several types of algebraic convex geometries we describe when the collection of closed sets is order scat- tered, in terms of obstructions to the semilattice of compact elements. In particular, a semilattice Ω(η), that does not appear among minimal obstructions to order-scattered algebraic modular lattices, plays a prominent role in convex geometries case. The connection to topological scat- teredness is established in convex geometries of relatively convex sets. 1. Introduction We call a pair X; φ of a non-empty set X and a closure operator φ 2X 2X on X a convex geometry[6], if it is a zero-closed space (i.e. ) and φ satisfies the anti-exchange axiom: ( ) ∶ → x A y and x∅ =A∅imply that y A x for all x y in X and all closed A X: ∈ ∪ { } ∉ ∉ ∪ { } The study of convex geometries in finite≠ case was inspired by their⊆ frequent appearance in mod- eling various discrete structures, as well as by their juxtaposition to matroids, see [20, 21]. More recently, there was a number of publications, see, for example, [4, 43, 44, 45, 48, 7] brought up by studies in infinite convex geometries. A convex geometry is called algebraic, if the closure operator φ is finitary. Most of interesting infinite convex geometries are algebraic, such as convex geometries of relatively convex sets, sub- semilattices of a semilattice, suborders of a partial order or convex subsets of a partially ordered set. -
An Elementary Approach to Boolean Algebra
Eastern Illinois University The Keep Plan B Papers Student Theses & Publications 6-1-1961 An Elementary Approach to Boolean Algebra Ruth Queary Follow this and additional works at: https://thekeep.eiu.edu/plan_b Recommended Citation Queary, Ruth, "An Elementary Approach to Boolean Algebra" (1961). Plan B Papers. 142. https://thekeep.eiu.edu/plan_b/142 This Dissertation/Thesis is brought to you for free and open access by the Student Theses & Publications at The Keep. It has been accepted for inclusion in Plan B Papers by an authorized administrator of The Keep. For more information, please contact [email protected]. r AN ELEr.:ENTARY APPRCACH TC BCCLF.AN ALGEBRA RUTH QUEAHY L _J AN ELE1~1ENTARY APPRCACH TC BC CLEAN ALGEBRA Submitted to the I<:athematics Department of EASTERN ILLINCIS UNIVERSITY as partial fulfillment for the degree of !•:ASTER CF SCIENCE IN EJUCATION. Date :---"'f~~-----/_,_ffo--..i.-/ _ RUTH QUEARY JUNE 1961 PURPOSE AND PLAN The purpose of this paper is to provide an elementary approach to Boolean algebra. It is designed to give an idea of what is meant by a Boclean algebra and to supply the necessary background material. The only prerequisite for this unit is one year of high school algebra and an open mind so that new concepts will be considered reason able even though they nay conflict with preconceived ideas. A mathematical science when put in final form consists of a set of undefined terms and unproved propositions called postulates, in terrrs of which all other concepts are defined, and from which all other propositions are proved. -
Abstract Algebra: Monoids, Groups, Rings
Notes on Abstract Algebra John Perry University of Southern Mississippi [email protected] http://www.math.usm.edu/perry/ Copyright 2009 John Perry www.math.usm.edu/perry/ Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States You are free: to Share—to copy, distribute and transmit the work • to Remix—to adapt the work Under• the following conditions: Attribution—You must attribute the work in the manner specified by the author or licen- • sor (but not in any way that suggests that they endorse you or your use of the work). Noncommercial—You may not use this work for commercial purposes. • Share Alike—If you alter, transform, or build upon this work, you may distribute the • resulting work only under the same or similar license to this one. With the understanding that: Waiver—Any of the above conditions can be waived if you get permission from the copy- • right holder. Other Rights—In no way are any of the following rights affected by the license: • Your fair dealing or fair use rights; ◦ Apart from the remix rights granted under this license, the author’s moral rights; ◦ Rights other persons may have either in the work itself or in how the work is used, ◦ such as publicity or privacy rights. Notice—For any reuse or distribution, you must make clear to others the license terms of • this work. The best way to do this is with a link to this web page: http://creativecommons.org/licenses/by-nc-sa/3.0/us/legalcode Table of Contents Reference sheet for notation...........................................................iv A few acknowledgements..............................................................vi Preface ...............................................................................vii Overview ...........................................................................vii Three interesting problems ............................................................1 Part . -
Properties & Multiplying a Polynomial by a Monomial Date
Algebra I Block Name Unit #1: Linear Equations & Inequalities Period Lesson #2: Properties & Multiplying a Polynomial by a Monomial Date Properties In algebra, it is important to know certain properties. These properties are basically rules that allow us to manipulate and work with equations in order to solve for a given variable. We will be dealing with them all year, and you’ve probably actually been using them for awhile without even realizing it! The Commutative Property deals with the order of things. In other words, if I switch the order of two or more items, will my answer be the same? The Commutative Property of Addition states that when it comes to adding two different numbers, the order does . The Commutative Property of Multiplication states that when it comes to multiplying two different numbers, the order does . When it comes to subtracting and dividing numbers, the order matter, so there are no commutative properties for subtraction or division. The Associative Property deals with how things are grouped together. In other words, if I regroup two or more numbers, will my answer still be the same? Just like the commutative property, the associative property applies to addition and multiplication only. There are Identity Properties that apply all four basic operations. When it comes to identity properties, just ask yourself “What can I do to keep the answer the same?” The identity property of addition states that if I add to anything, it will stay the same. The same is true for subtraction. The identity property of multiplication states that if I multiply anything by _______, it will stay the same. -
Completely Representable Lattices
Completely representable lattices Robert Egrot and Robin Hirsch Abstract It is known that a lattice is representable as a ring of sets iff the lattice is distributive. CRL is the class of bounded distributive lattices (DLs) which have representations preserving arbitrary joins and meets. jCRL is the class of DLs which have representations preserving arbitrary joins, mCRL is the class of DLs which have representations preserving arbitrary meets, and biCRL is defined to be jCRL ∩ mCRL. We prove CRL ⊂ biCRL = mCRL ∩ jCRL ⊂ mCRL =6 jCRL ⊂ DL where the marked inclusions are proper. Let L be a DL. Then L ∈ mCRL iff L has a distinguishing set of complete, prime filters. Similarly, L ∈ jCRL iff L has a distinguishing set of completely prime filters, and L ∈ CRL iff L has a distinguishing set of complete, completely prime filters. Each of the classes above is shown to be pseudo-elementary hence closed under ultraproducts. The class CRL is not closed under elementary equivalence, hence it is not elementary. 1 Introduction An atomic representation h of a Boolean algebra B is a representation h: B → ℘(X) (some set X) where h(1) = {h(a): a is an atom of B}. It is known that a representation of a Boolean algebraS is a complete representation (in the sense of a complete embedding into a field of sets) if and only if it is an atomic repre- sentation and hence that the class of completely representable Boolean algebras is precisely the class of atomic Boolean algebras, and hence is elementary [6]. arXiv:1201.2331v3 [math.RA] 30 Aug 2016 This result is not obvious as the usual definition of a complete representation is thoroughly second order. -
On Birkhoff's Common Abstraction Problem
F. Paoli On Birkho®'s Common C. Tsinakis Abstraction Problem Abstract. In his milestone textbook Lattice Theory, Garrett Birkho® challenged his readers to develop a \common abstraction" that includes Boolean algebras and lattice- ordered groups as special cases. In this paper, after reviewing the past attempts to solve the problem, we provide our own answer by selecting as common generalization of BA and LG their join BA_LG in the lattice of subvarieties of FL (the variety of FL-algebras); we argue that such a solution is optimal under several respects and we give an explicit equational basis for BA_LG relative to FL. Finally, we prove a Holland-type representation theorem for a variety of FL-algebras containing BA _ LG. Keywords: Residuated lattice, FL-algebra, Substructural logics, Boolean algebra, Lattice- ordered group, Birkho®'s problem, History of 20th C. algebra. 1. Introduction In his milestone textbook Lattice Theory [2, Problem 108], Garrett Birkho® challenged his readers by suggesting the following project: Develop a common abstraction that includes Boolean algebras (rings) and lattice ordered groups as special cases. Over the subsequent decades, several mathematicians tried their hands at Birkho®'s intriguing problem. Its very formulation, in fact, intrinsically seems to call for reiterated attempts: unlike most problems contained in the book, for which it is manifest what would count as a correct solution, this one is stated in su±ciently vague terms as to leave it open to debate whether any proposed answer is really adequate. It appears to us that Rama Rao puts things right when he remarks [28, p. -
Partial Orders — Basics
Partial Orders — Basics Edward A. Lee UC Berkeley — EECS EECS 219D — Concurrent Models of Computation Last updated: January 23, 2014 Outline Sets Join (Least Upper Bound) Relations and Functions Meet (Greatest Lower Bound) Notation Example of Join and Meet Directed Sets, Bottom Partial Order What is Order? Complete Partial Order Strict Partial Order Complete Partial Order Chains and Total Orders Alternative Definition Quiz Example Partial Orders — Basics Sets Frequently used sets: • B = {0, 1}, the set of binary digits. • T = {false, true}, the set of truth values. • N = {0, 1, 2, ···}, the set of natural numbers. • Z = {· · · , −1, 0, 1, 2, ···}, the set of integers. • R, the set of real numbers. • R+, the set of non-negative real numbers. Edward A. Lee | UC Berkeley — EECS3/32 Partial Orders — Basics Relations and Functions • A binary relation from A to B is a subset of A × B. • A partial function f from A to B is a relation where (a, b) ∈ f and (a, b0) ∈ f =⇒ b = b0. Such a partial function is written f : A*B. • A total function or just function f from A to B is a partial function where for all a ∈ A, there is a b ∈ B such that (a, b) ∈ f. Edward A. Lee | UC Berkeley — EECS4/32 Partial Orders — Basics Notation • A binary relation: R ⊆ A × B. • Infix notation: (a, b) ∈ R is written aRb. • A symbol for a relation: • ≤⊂ N × N • (a, b) ∈≤ is written a ≤ b. • A function is written f : A → B, and the A is called its domain and the B its codomain. -
Chain Based Lattices
Pacific Journal of Mathematics CHAIN BASED LATTICES GEORGE EPSTEIN AND ALFRED HORN Vol. 55, No. 1 September 1974 PACIFIC JOURNAL OF MATHEMATICS Vol. 55, No. 1, 1974 CHAIN BASED LATTICES G. EPSTEIN AND A. HORN In recent years several weakenings of Post algebras have been studied. Among these have been P0"lattices by T. Traezyk, Stone lattice of order n by T. Katrinak and A. Mitschke, and P-algebras by the present authors. Each of these system is an abstraction from certain aspects of Post algebras, and no two of them are comparable. In the present paper, the theory of P0-lattices will be developed further and two new systems, called Pi-lattices and P2-lattices are introduced. These systems are referred to as chain based lattices. P2-lattices form the intersection of all three weakenings mentioned above. While P-algebras and weaker systems such as L-algebras, Heyting algebras, and P-algebras, do not require any distinguished chain of elements other than 0, 1, chain based lattices require such a chain. Definitions are given in § 1. A P0-lattice is a bounded distributive lattice A which is generated by its center and a finite subchain con- taining 0 and 1. Such a subchain is called a chain base for A. The order of a P0-lattice A is the smallest number of elements in a chain base of A. In § 2, properties of P0-lattices are given which are used in later sections. If a P0-lattice A is a Heyting algebra, then it is shown in § 3, that there exists a unique chain base 0 = e0 < ex < < en_x — 1 such that ei+ί —* et = et for all i > 0. -
Hopf Monoids of Ordered Simplicial Complexes
HOPF MONOIDS OF ORDERED SIMPLICIAL COMPLEXES FEDERICO CASTILLO, JEREMY L. MARTIN, AND JOSE´ A. SAMPER ABSTRACT. We study pure ordered simplicial complexes (i.e., simplicial complexes with a linear order on their ground sets) from the Hopf-theoretic point of view. We define a Hopf class to be a family of pure ordered simplicial complexes that give rise to a Hopf monoid under join and deletion/contraction. The prototypical Hopf class is the family of ordered matroids. The idea of a Hopf class allows us to give a systematic study of simpli- cial complexes related to matroids, including shifted complexes, broken-circuit complexes, and unbounded matroids (which arise from unbounded generalized permutohedra with 0/1 coordinates). We compute the antipodes in two cases: facet-initial complexes (a much larger class than shifted complexes) and unbounded ordered matroids. In the latter case, we embed the Hopf monoid of ordered matroids into the Hopf monoid of ordered generalized permuto- hedra, enabling us to compute the antipode using the topological method of Aguiar and Ardila. The calculation is complicated by the appearance of certain auxiliary simplicial complexes that we call Scrope complexes, whose Euler characteristics control certain coeffi- cients of the antipode. The resulting antipode formula is multiplicity-free and cancellation- free. CONTENTS 1. Introduction2 1.1. Background: Matroids and combinatorial Hopf theory2 1.2. Ordered simplicial complexes3 1.3. Polyhedra5 1.4. Antipodes6 2. Background and notation7 2.1. Simplicial complexes8 2.2. Matroids9 2.3. Set compositions, preposets, and the braid fan9 2.4. Generalized permutohedra 11 2.5. Species and Hopf monoids 14 2.6. -
Lattice Duality: the Origin of Probability and Entropy
, 1 Lattice Duality: The Origin of Probability and Entropy Kevin H. Knuth NASA Ames Research Center, Mail Stop 269-3, Moffett Field CA 94035-1000, USA Abstract Bayesian probability theory is an inference calculus, which originates from a gen- eralization of inclusion on the Boolean lattice of logical assertions to a degree of inclusion represented by a real number. Dual to this lattice is the distributive lat- tice of questions constructed from the ordered set of down-sets of assertions, which forms the foundation of the calculus of inquiry-a generalization of information theory. In this paper we introduce this novel perspective on these spaces in which machine learning is performed and discuss the relationship between these results and several proposed generalizations of information theory in the literature. Key words: probability, entropy, lattice, information theory, Bayesian inference, inquiry PACS: 1 Introduction It has been known for some time that probability theory can be derived as a generalization of Boolean implication to degrees of implication repre- sented by real numbers [11,12]. Straightforward consistency requirements dic- tate the form of the sum and product rules of probability, and Bayes’ thee rem [11,12,47,46,20,34],which forms the basis of the inferential calculus, also known as inductive inference. However, in machine learning applications it is often times more useful to rely on information theory [45] in the design of an algorithm. On the surface, the connection between information theory and probability theory seems clear-information depends on entropy and entropy Email address: kevin.h. [email protected] (Kevin H. -
Vector Spaces
Chapter 1 Vector Spaces Linear algebra is the study of linear maps on finite-dimensional vec- tor spaces. Eventually we will learn what all these terms mean. In this chapter we will define vector spaces and discuss their elementary prop- erties. In some areas of mathematics, including linear algebra, better the- orems and more insight emerge if complex numbers are investigated along with real numbers. Thus we begin by introducing the complex numbers and their basic✽ properties. 1 2 Chapter 1. Vector Spaces Complex Numbers You should already be familiar with the basic properties of the set R of real numbers. Complex numbers were invented so that we can take square roots of negative numbers. The key idea is to assume we have i − i The symbol was√ first a square root of 1, denoted , and manipulate it using the usual rules used to denote −1 by of arithmetic. Formally, a complex number is an ordered pair (a, b), the Swiss where a, b ∈ R, but we will write this as a + bi. The set of all complex mathematician numbers is denoted by C: Leonhard Euler in 1777. C ={a + bi : a, b ∈ R}. If a ∈ R, we identify a + 0i with the real number a. Thus we can think of R as a subset of C. Addition and multiplication on C are defined by (a + bi) + (c + di) = (a + c) + (b + d)i, (a + bi)(c + di) = (ac − bd) + (ad + bc)i; here a, b, c, d ∈ R. Using multiplication as defined above, you should verify that i2 =−1. -
Lattices: an Introduction
Lattices: An Introduction Felix Gotti [email protected] UC Berkeley Student Combinatorics Seminar Felix Gotti [email protected] Lattices: An Introduction Outline General Lattices Modular Lattices Distributive Lattices Felix Gotti [email protected] Lattices: An Introduction Joins and Meets Definition (Joins and Meets) Let P be a poset. I If S ⊆ P a join (or supremum) of S, denoted by _ s; s2S is an element of u 2 P that is an upper bound of S satisfying that if u0 is any other upper bound of S, then u ≤ u0. I The definition of a meet (or infimum) of S ⊆ P, denoted by ^ s; s2S is dual to the definition of join. Remark: Note that if a join (resp., meet) exists then it is unique. Felix Gotti [email protected] Lattices: An Introduction Definition of Lattice Definition I A join-semilattice (resp., meet-semilattice) is a poset such that any pair of elements have a join (resp., meet). I A lattice is a poset that is both a join-semilattice and a meet-semilattice. I If L is a lattice and S ⊂ L such that r _ s; r ^ s 2 S for all r; s 2 S, we say that S is a sublattice of L. Example of lattices: I Every totally ordered set is a lattice. ∗ I If L and M are lattices, so are L ; L ⊕ M, and L × M. While L + M is not a lattice, at least L or M is empty, (L + M) [ f0^; 1^g is always a lattice. I The lattices L and M are sublattices of L ⊕ M and (L + M) [ f0^; 1^g.