The Category Theoretic Solution of Recursive Program Schemes*
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Mathematics I Basic Mathematics
Mathematics I Basic Mathematics Prepared by Prof. Jairus. Khalagai African Virtual university Université Virtuelle Africaine Universidade Virtual Africana African Virtual University NOTICE This document is published under the conditions of the Creative Commons http://en.wikipedia.org/wiki/Creative_Commons Attribution http://creativecommons.org/licenses/by/2.5/ License (abbreviated “cc-by”), Version 2.5. African Virtual University Table of ConTenTs I. Mathematics 1, Basic Mathematics _____________________________ 3 II. Prerequisite Course or Knowledge _____________________________ 3 III. Time ____________________________________________________ 3 IV. Materials _________________________________________________ 3 V. Module Rationale __________________________________________ 4 VI. Content __________________________________________________ 5 6.1 Overview ____________________________________________ 5 6.2 Outline _____________________________________________ 6 VII. General Objective(s) ________________________________________ 8 VIII. Specific Learning Objectives __________________________________ 8 IX. Teaching and Learning Activities ______________________________ 10 X. Key Concepts (Glossary) ____________________________________ 16 XI. Compulsory Readings ______________________________________ 18 XII. Compulsory Resources _____________________________________ 19 XIII. Useful Links _____________________________________________ 20 XIV. Learning Activities _________________________________________ 23 XV. Synthesis Of The Module ___________________________________ -
Axioms and Algebraic Systems*
Axioms and algebraic systems* Leong Yu Kiang Department of Mathematics National University of Singapore In this talk, we introduce the important concept of a group, mention some equivalent sets of axioms for groups, and point out the relationship between the individual axioms. We also mention briefly the definitions of a ring and a field. Definition 1. A binary operation on a non-empty set S is a rule which associates to each ordered pair (a, b) of elements of S a unique element, denoted by a* b, in S. The binary relation itself is often denoted by *· It may also be considered as a mapping from S x S to S, i.e., * : S X S ~ S, where (a, b) ~a* b, a, bE S. Example 1. Ordinary addition and multiplication of real numbers are binary operations on the set IR of real numbers. We write a+ b, a· b respectively. Ordinary division -;- is a binary relation on the set IR* of non-zero real numbers. We write a -;- b. Definition 2. A binary relation * on S is associative if for every a, b, c in s, (a* b) * c =a* (b *c). Example 2. The binary operations + and · on IR (Example 1) are as sociative. The binary relation -;- on IR* (Example 1) is not associative smce 1 3 1 ~ 2) ~ 3 _J_ - 1 ~ (2 ~ 3). ( • • = -6 I 2 = • • * Talk given at the Workshop on Algebraic Structures organized by the Singapore Mathemat- ical Society for school teachers on 5 September 1988. 11 Definition 3. A semi-group is a non-€mpty set S together with an asso ciative binary operation *, and is denoted by (S, *). -
Slides by Akihisa
Complete non-orders and fixed points Akihisa Yamada1, Jérémy Dubut1,2 1 National Institute of Informatics, Tokyo, Japan 2 Japanese-French Laboratory for Informatics, Tokyo, Japan Supported by ERATO HASUO Metamathematics for Systems Design Project (No. JPMJER1603), JST Introduction • Interactive Theorem Proving is appreciated for reliability • But it's also engineering tool for mathematics (esp. Isabelle/jEdit) • refactoring proofs and claims • sledgehammer • quickcheck/nitpick(/nunchaku) • We develop an Isabelle library of order theory (as a case study) ⇒ we could generalize many known results, like: • completeness conditions: duality and relationships • Knaster-Tarski fixed-point theorem • Kleene's fixed-point theorem Order A binary relation ⊑ • reflexive ⟺ � ⊑ � • transitive ⟺ � ⊑ � and � ⊑ � implies � ⊑ � • antisymmetric ⟺ � ⊑ � and � ⊑ � implies � = � • partial order ⟺ reflexive + transitive + antisymmetric Order A binary relation ⊑ locale less_eq_syntax = fixes less_eq :: 'a ⇒ 'a ⇒ bool (infix "⊑" 50) • reflexive ⟺ � ⊑ � locale reflexive = ... assumes "x ⊑ x" • transitive ⟺ � ⊑ � and � ⊑ � implies � ⊑ � locale transitive = ... assumes "x ⊑ y ⟹ y ⊑ z ⟹ x ⊑ z" • antisymmetric ⟺ � ⊑ � and � ⊑ � implies � = � locale antisymmetric = ... assumes "x ⊑ y ⟹ y ⊑ x ⟹ x = y" • partial order ⟺ reflexive + transitive + antisymmetric locale partial_order = reflexive + transitive + antisymmetric Quasi-order A binary relation ⊑ locale less_eq_syntax = fixes less_eq :: 'a ⇒ 'a ⇒ bool (infix "⊑" 50) • reflexive ⟺ � ⊑ � locale reflexive = ... assumes -
A List of Arithmetical Structures Complete with Respect to the First
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Theoretical Computer Science 257 (2001) 115–151 www.elsevier.com/locate/tcs A list of arithmetical structures complete with respect to the ÿrst-order deÿnability Ivan Korec∗;X Slovak Academy of Sciences, Mathematical Institute, Stefanikovaà 49, 814 73 Bratislava, Slovak Republic Abstract A structure with base set N is complete with respect to the ÿrst-order deÿnability in the class of arithmetical structures if and only if the operations +; × are deÿnable in it. A list of such structures is presented. Although structures with Pascal’s triangles modulo n are preferred a little, an e,ort was made to collect as many simply formulated results as possible. c 2001 Elsevier Science B.V. All rights reserved. MSC: primary 03B10; 03C07; secondary 11B65; 11U07 Keywords: Elementary deÿnability; Pascal’s triangle modulo n; Arithmetical structures; Undecid- able theories 1. Introduction A list of (arithmetical) structures complete with respect of the ÿrst-order deÿnability power (shortly: def-complete structures) will be presented. (The term “def-strongest” was used in the previous versions.) Most of them have the base set N but also structures with some other universes are considered. (Formal deÿnitions are given below.) The class of arithmetical structures can be quasi-ordered by ÿrst-order deÿnability power. After the usual factorization we obtain a partially ordered set, and def-complete struc- tures will form its greatest element. Of course, there are stronger structures (with respect to the ÿrst-order deÿnability) outside of the class of arithmetical structures. -
Basic Concepts of Set Theory, Functions and Relations 1. Basic
Ling 310, adapted from UMass Ling 409, Partee lecture notes March 1, 2006 p. 1 Basic Concepts of Set Theory, Functions and Relations 1. Basic Concepts of Set Theory........................................................................................................................1 1.1. Sets and elements ...................................................................................................................................1 1.2. Specification of sets ...............................................................................................................................2 1.3. Identity and cardinality ..........................................................................................................................3 1.4. Subsets ...................................................................................................................................................4 1.5. Power sets .............................................................................................................................................4 1.6. Operations on sets: union, intersection...................................................................................................4 1.7 More operations on sets: difference, complement...................................................................................5 1.8. Set-theoretic equalities ...........................................................................................................................5 Chapter 2. Relations and Functions ..................................................................................................................6 -
Binary Operations
4-22-2007 Binary Operations Definition. A binary operation on a set X is a function f : X × X → X. In other words, a binary operation takes a pair of elements of X and produces an element of X. It’s customary to use infix notation for binary operations. Thus, rather than write f(a, b) for the binary operation acting on elements a, b ∈ X, you write afb. Since all those letters can get confusing, it’s also customary to use certain symbols — +, ·, ∗ — for binary operations. Thus, f(a, b) becomes (say) a + b or a · b or a ∗ b. Example. Addition is a binary operation on the set Z of integers: For every pair of integers m, n, there corresponds an integer m + n. Multiplication is also a binary operation on the set Z of integers: For every pair of integers m, n, there corresponds an integer m · n. However, division is not a binary operation on the set Z of integers. For example, if I take the pair 3 (3, 0), I can’t perform the operation . A binary operation on a set must be defined for all pairs of elements 0 from the set. Likewise, a ∗ b = (a random number bigger than a or b) does not define a binary operation on Z. In this case, I don’t have a function Z × Z → Z, since the output is ambiguously defined. (Is 3 ∗ 5 equal to 6? Or is it 117?) When a binary operation occurs in mathematics, it usually has properties that make it useful in con- structing abstract structures. -
Binary Operations
Binary operations 1 Binary operations The essence of algebra is to combine two things and get a third. We make this into a definition: Definition 1.1. Let X be a set. A binary operation on X is a function F : X × X ! X. However, we don't write the value of the function on a pair (a; b) as F (a; b), but rather use some intermediate symbol to denote this value, such as a + b or a · b, often simply abbreviated as ab, or a ◦ b. For the moment, we will often use a ∗ b to denote an arbitrary binary operation. Definition 1.2. A binary structure (X; ∗) is a pair consisting of a set X and a binary operation on X. Example 1.3. The examples are almost too numerous to mention. For example, using +, we have (N; +), (Z; +), (Q; +), (R; +), (C; +), as well as n vector space and matrix examples such as (R ; +) or (Mn;m(R); +). Using n subtraction, we have (Z; −), (Q; −), (R; −), (C; −), (R ; −), (Mn;m(R); −), but not (N; −). For multiplication, we have (N; ·), (Z; ·), (Q; ·), (R; ·), (C; ·). If we define ∗ ∗ ∗ Q = fa 2 Q : a 6= 0g, R = fa 2 R : a 6= 0g, C = fa 2 C : a 6= 0g, ∗ ∗ ∗ then (Q ; ·), (R ; ·), (C ; ·) are also binary structures. But, for example, ∗ (Q ; +) is not a binary structure. Likewise, (U(1); ·) and (µn; ·) are binary structures. In addition there are matrix examples: (Mn(R); ·), (GLn(R); ·), (SLn(R); ·), (On; ·), (SOn; ·). Next, there are function composition examples: for a set X,(XX ; ◦) and (SX ; ◦). -
Mathematical Tools MPRI 2–6: Abstract Interpretation, Application to Verification and Static Analysis
Mathematical Tools MPRI 2{6: Abstract Interpretation, application to verification and static analysis Antoine Min´e CNRS, Ecole´ normale sup´erieure course 1, 2012{2013 course 1, 2012{2013 Mathematical Tools Antoine Min´e p. 1 / 44 Order theory course 1, 2012{2013 Mathematical Tools Antoine Min´e p. 2 / 44 Partial orders Partial orders course 1, 2012{2013 Mathematical Tools Antoine Min´e p. 3 / 44 Partial orders Partial orders Given a set X , a relation v2 X × X is a partial order if it is: 1 reflexive: 8x 2 X ; x v x 2 antisymmetric: 8x; y 2 X ; x v y ^ y v x =) x = y 3 transitive: 8x; y; z 2 X ; x v y ^ y v z =) x v z. (X ; v) is a poset (partially ordered set). If we drop antisymmetry, we have a preorder instead. course 1, 2012{2013 Mathematical Tools Antoine Min´e p. 4 / 44 Partial orders Examples of posets (Z; ≤) is a poset (in fact, completely ordered) (P(X ); ⊆) is a poset (not completely ordered) (S; =) is a poset for any S course 1, 2012{2013 Mathematical Tools Antoine Min´e p. 5 / 44 Partial orders Examples of posets (cont.) Given by a Hasse diagram, e.g.: g e f g v g c d f v f ; g e v e; g b d v d; f ; g c v c; e; f ; g b v b; c; d; e; f ; g a a v a; b; c; d; e; f ; g course 1, 2012{2013 Mathematical Tools Antoine Min´e p. -
The Poset of Closure Systems on an Infinite Poset: Detachability and Semimodularity Christian Ronse
The poset of closure systems on an infinite poset: Detachability and semimodularity Christian Ronse To cite this version: Christian Ronse. The poset of closure systems on an infinite poset: Detachability and semimodularity. Portugaliae Mathematica, European Mathematical Society Publishing House, 2010, 67 (4), pp.437-452. 10.4171/PM/1872. hal-02882874 HAL Id: hal-02882874 https://hal.archives-ouvertes.fr/hal-02882874 Submitted on 31 Aug 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Portugal. Math. (N.S.) Portugaliae Mathematica Vol. xx, Fasc. , 200x, xxx–xxx c European Mathematical Society The poset of closure systems on an infinite poset: detachability and semimodularity Christian Ronse Abstract. Closure operators on a poset can be characterized by the corresponding clo- sure systems. It is known that in a directed complete partial order (DCPO), in particular in any finite poset, the collection of all closure systems is closed under arbitrary inter- section and has a “detachability” or “anti-matroid” property, which implies that the collection of all closure systems is a lower semimodular complete lattice (and dually, the closure operators form an upper semimodular complete lattice). After reviewing the history of the problem, we generalize these results to the case of an infinite poset where closure systems do not necessarily constitute a complete lattice; thus the notions of lower semimodularity and detachability are extended accordingly. -
Programming Language Semantics a Rewriting Approach
Programming Language Semantics A Rewriting Approach Grigore Ros, u University of Illinois at Urbana-Champaign Chapter 2 Background and Preliminaries 15 2.6 Complete Partial Orders and the Fixed-Point Theorem This section introduces a fixed-point theorem for complete partial orders. Our main use of this theorem is to give denotational semantics to iterative (in Section 3.4) and to recursive (in Section 4.8) language constructs. Complete partial orders with bottom elements are at the core of both domain theory and denotational semantics, often identified with the notion of “domain” itself. 2.6.1 Posets, Upper Bounds and Least Upper Bounds Here we recall basic notions of partial and total orders, such as upper bounds and least upper bounds, and discuss several examples and counter-examples. Definition 14. A partial order, or a poset (from partial order set) (D, ) consists of a set D and a binary v relation on D, written as an infix operation, which is v reflexive, that is, x x for any x D; • v ∈ transitive, that is, for any x, y, z D, x y and y z imply x z; and • ∈ v v v anti-symmetric, that is, if x y and y x for some x, y D then x = y. • v v ∈ A partial order (D, ) is called a total order when x y or y x for any x, y D. v v v ∈ Here are some examples of partial or total orders: ( (S ), ) is a partial order, where (S ) is the powerset of a set S , that is, the set of subsets of S . -
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. -
Do We Need Recursion?
Do We Need Recursion? V´ıtˇezslav Svejdarˇ Charles University in Prague Appeared in Igor Sedl´arand Martin Blicha eds., The Logica Yearbook 2019, pp. 193{204, College Publications, London, 2020. Abstract The operation of primitive recursion, and recursion in a more general sense, is undoubtedly a useful tool. However, we will explain that in two situa- tion where we work with it, namely in the definition of partial recursive functions and in logic when defining the basic syntactic notions, its use can be avoided. We will also explain why one would want to do so. 1 What is recursion, where do we meet it? Recursion in a narrow sense is an operation with multivariable functions whose arguments are natural numbers. Let z denote the k-tuple z1; : : ; zk (where the number k of variables does not have to be indicated), and let fb(x; z) denote the code hf(0; z); : : ; f(x − 1; z)i of the sequence f(0; z); : : ; f(x − 1; z) under some suitable coding of finite sequences. If (1), (2) or (3) holds for any choice of arguments: f(0) = a; f(x + 1) = h(f(x); x); (1) f(0; z) = g(z); f(x + 1; z) = h(f(x; z); x; z); (2) f(x; z) = h(fb(x; z); z); (3) then in the cases (1) and (2) we say that f is derived from a number a and a function h or from two functions g and h by primitive recursion, while in the case (3) we say that f is derived from h by course-of-values recursion.