Notes on Monoids and Automata

Total Page:16

File Type:pdf, Size:1020Kb

Notes on Monoids and Automata Notes on Monoids and Automata Uday S. Reddy November 9, 1994 In this article, I define a semantics for Algol programs with Reynolds’s syntactic control of interference (?; ?) in terms of comonoids in coherent spaces (also called correletation spaces). 1 Background: Monoids 1.1 Definition A monoid (in SET) is a triple M = (M, 1, ·) where • M is a set, • 1 ∈ M is an element called the unit, and • · : M × M → M is a binary operation called multiplication such that the following identities hold: • x · 1= x = 1 · x, for all x ∈ M, and • (x · y) · z = x · (y · z), for all x,y,z ∈ M. Note that the unit is necessarily unique. We often omit the · operator and write x · y as simply xy. The structure is called a semigroup if we don’t insist on the unit being present. It is called a commutative monoid if, in addition, • xy = yx, for all x, y ∈ M. Suppose only some instances of the commutativity equation hold so that the structure is a “partially commutative” monoid. Then, we can define a relation ∼⊆ M × M such that x ∼ y ⇐⇒ xy = yx. Evidently, ∼ is reflexive and symmetric. A zero in a monoid is an element 0 ∈ M such that 0x =0= x0 for all x ∈ M. Note that 0 is necessarily unique. A submonoid of M is a subset M ′ ⊆ M containing 1 and closed under multiplication. If x ∈ M, we use the notation xn for the n-fold product x ··· x (with x0 = 1). x∗ denotes the n ∗ set { x : n ≥ 0 }. Similarly, if S ⊆ M, S denotes the set {x1 ··· xn : x1,...,xn ∈ S }. Note that the S∗ is the least submonoid of M including S. If S∗ = M, we say that S is a set of generators for M. 1.2 Examples The following examples will be of much interest to our discussion: (i) Let Q be a set. The set of partial functions from Q to Q (called transformations of Q) forms a monoid. The identity transformation 1Q is the unit and the composition f; g of transforma- tions is the multiplication. We find it convenient to use postfix notation for transformations. Then, the above operations are defined by q1Q = q (qf)g, if qf is defined q(f; g) = (undefined, otherwise 1 We denote this monoid by [Q → Q]. It has a zero element, viz., the undefined transformation. Any submonoid of [Q → Q] is called a transformation monoid. (ii) More generally, in any category C, the endomorphisms on an object A form a monoid, denoted EndC(A). So, the set of binary relations over Q and the set of total functions from Q to Q form monoids too. (iii) Let Σ be a set (of “symbols”). The set of strings over Σ, denoted Σ∗ is a monoid with concatenation as the multiplication and the empty string ǫ as the unit. It is called the free monoid generated by Σ. Similarly, the set of nonempty strings over Σ (Σ+) is the free semigroup generated by Σ, and the set of finite multisets over Σ is the free commutative monoid generated by Σ. Some more examples of passing interest are as follows: (iv) The set of natural numbers ω forms a monoid with multiplication as the binary operation and the integer 1 as the unit. This is, in fact, a commutative monoid. (v) The set of natural numbers, again, forms a commutative monoid with addition as the binary operation and 0 as the unit. This is isomorphic, in the sense to be made precise below, to the monoid {1}∗ (the free monoid generated by {1}). (vi) The set of n × n matrices forms a monoid under matrix multiplication as the multiplication and the identity matrix as the unit. This is, of course, a special case of the endomorphisms in a category example mentioned above. The following examples give important constructions on monoids: (vii) If M = (M, 1, ·) is a monoid, its dual monoid is (M, 1, ∗) where ∗ is defined by x ∗ y = y · x. Thus, the notion of monoid is a symmetric concept. (viii) Let M be a monoid. For each x ∈ M, there corresponds a right multiplication operator Rx : M → M defined by zRx = zx. The set of right multiplication operators forms a monoid, in fact, a transformation monoid over M. The unit is R1 and the multiplication is Rx · Ry = Rxy. In fact, the right multiplication monoid is isomorphic to the monoid itself. Similarly, the left multiplication operators Lx : M → M are defined by Lx(z) = xz. They have a unit L1 and multiplication Lx · Ly = Lyx. Thus, the left multiplication operators form a monoid that is isomorphic to the dual of M. (ix) Let M be a monoid. An equivalence relation ≡ ⊆ M × M is called a (monoid) congruence relation if x ≡ x′ ∧ y ≡ y′ =⇒ xy ≡ x′y′. A congruence class is a maximal set X ⊆ M such that all the elements of X are equivalent to each other. The congruence class containing an element x is [x] = { y : y ≡ x }. The set of such congruence classes forms a monoid. The unit element is [1] and the multiplication of equivalence classes is defined by: [x] · [y]=[xy]. The fact that ≡ is a congruence relation ensures that multiplication is well-defined (as may be verified). This monoid is called the quotient monoid of M under ≡, denoted M/≡. (x) A pair (Σ, ∼), where Σ is a set and ∼⊆ Σ×Σ a symmetric relation, is called an independence alphabet (and ∼ its independence relation). Consider strings over Σ and the least congruence ≡ such that a ∼ b =⇒ ab ≡ ba. A congruence class [a1 ··· an] consists of all strings which only differ in the relative order of consecutive independent symbols. Such congruence classes are called traces and the quotient monoid Σ∗/≡ is called the free partially commutative monoid generated by Σ. (xi) Let Σ be an alphabet and S ⊆ Σ∗ be a set of strings. S induces an equivalence on strings by x ≈ y ⇐⇒ ∀z,z′. (zxz′ ∈ S ⇐⇒ zyz′ ∈ S). This is, in fact, a congruence relation, called the syntactic congruence or the Myhill congruence of S. Suppose x ≈ x′ and y ≈ y′. Then, zxx′z′ ∈ S ⇐⇒ zyx′z′ ∈ S from the first assumption and zyx′z′ ∈ S ⇐⇒ zyy′z′ ∈ S from the second assumption. Hence, xx′ ≈ yy′. Intuitively, x and y are equivalent if they behave 2 the same way as segments of strings in S. For example, let Σ = {a, b} and S be the regular set ab∗. Under the Myhill congruence of S, b ≈ bn+1 and a2 ≈ an+2 ≈ ban+1. Thus, the congruence classes are [ǫ], [a], [b] and [a2]. [ǫ] is the unit. [a2] is a zero. For the others, multiplication is defined by [a] · [b]=[a] and [b] · [a]=[a2]. The monoid Σ∗/≈ is called the syntactic monoid of S. More generally, a subset of any monoid S ⊆ M induces a syntactic congruence and a syntactic monoid. (xii) If S ⊆ Σ∗ is a set of strings, one can also define a syntactic right congruence on Σ∗ by x ≈r y iff ∀z. (xz ∈ S ⇐⇒ yz ∈ S). This is a right congruence relation in the sense that x ≈r y =⇒ xv ≡ yv for all v ∈ Σ∗. For example, for S = ab∗, the right congruence classes are [ǫ], [a] (containing all abn) and [a2] (containing all bn). Let the set of right congruence classes be Q. Then, each string x ∈ Σ∗ has a corresponding right multiplication operator on Q, defined by [z]Rx = [zx]. The set of right multiplication operators Rx forms a monoid with unit R1 and multiplication Rx · Ry = Rxy. This is in fact a transformation monoid over Q. As will be seen below, such a transformation monoid is nothing but an automaton. 1.3 Definition A monoid homomorphism h : (M, 1, ·) → (M ′, 1′, ·′) is a function h : M → M ′ such that (i) h(1) = 1′, and (ii) h(x · y)= h(x) ·′ h(y) for all x, y ∈ M. A one-one homomorphism is called a monomorphism and an onto homomorphism is called an epimorphism.1 If there is an inverse homomorphism h−1 such that h ◦ h−1 = h−1 ◦ h = id then h is called an isomorphism, and M and M ′ are said to be isomorphic. Whenever h : M → M ′ is a homomorphism, the image of h, defined by Im(h) = { y : ∃x ∈ M. h(x)= u }, is a submonoid of M ′. Dually, the equivalence relation ≡ on M defined by x ≡ y ⇐⇒ h(x)= h(y) is a congruence relation (called the kernel of h). The homomorphism h′ : M/≡ → M ′ given by h′([x]) = h(x), is then a mono. Therefore, M/≡ and Im(h) are isomorphic. If f : Σ → M is a function, it extends to a unique homomorphism h : Σ∗ → M such that h restricted to Σ is f. We can define it explicitly by h(a1 ··· an)= f(a1) ··· f(an). If h is a surjection, M is isomorphic to the quotient monoid Σ∗/≡ where ≡ is the kernel of h. More generally, if M has a set of generators S, to specify a homomorphism h : M → M ′, we only need to specify it on S. 2 Background: Automata 2.4 Definition Let Σ be a set (the alphabet or the instruction set of the automaton). A Σ- automaton is a pair A = (Q, F ) where Q is a set (the state set) and F : Σ → [Q → Q] is a function called the interpretation (mapping instructions to transformations of Q). We also call A an automaton class of type Σ, and denote this fact by writing A = (Q, F )Σ.
Recommended publications
  • Classifying Regular Languages Via Cascade Products of Automata
    Classifying Regular Languages via Cascade Products of Automata Marcus Gelderie RWTH Aachen, Lehrstuhl f¨urInformatik 7, D-52056 Aachen [email protected] Abstract. Building on the celebrated Krohn-Rhodes Theorem we char- acterize classes of regular languages in terms of the cascade decomposi- tions of minimal DFA of languages in those classes. More precisely we provide characterizations for the classes of piecewise testable languages and commutative languages. To this end we use biased resets, which are resets in the classical sense, that can change their state at most once. Next, we introduce the concept of the scope of a cascade product of reset automata in order to capture a notion of locality inside a cascade prod- uct and show that there exist constant bounds on the scope for certain classes of languages. Finally we investigate the impact of biased resets in a product of resets on the dot-depth of languages recognized by this product. This investigation allows us to refine an upper bound on the dot-depth of a language, given by Cohen and Brzozowski. 1 Introduction A significant result in the structure theory of regular languages is the Krohn- Rhodes Theorem [7], which states that any finite automaton can be decomposed into simple \prime factors" (a detailed exposition is given in [4, 6, 9, 10]). We use the Krohn-Rhodes Theorem to characterize classes of regular lan- guages in terms of the decompositions of the corresponding minimal automata. In [8] this has been done for star-free languages by giving an alternative proof for the famous Sch¨utzenberger Theorem [11].
    [Show full text]
  • The Partial Function Computed by a TM M(W)
    CS601 The Partial Function Computed by a TM Lecture 2 8 y if M on input “.w ” eventually <> t M(w) halts with output “.y ” ≡ t :> otherwise % Σ Σ .; ; Usually, Σ = 0; 1 ; w; y Σ? 0 ≡ − f tg 0 f g 2 0 Definition 2.1 Let f :Σ? Σ? be a total or partial function. We 0 ! 0 say that f is a partial, recursive function iff TM M(f = M( )), 9 · i.e., w Σ?(f(w) = M(w)). 8 2 0 Remark 2.2 There is an easy to compute 1:1 and onto map be- tween 0; 1 ? and N [Exercise]. Thus we can think of the contents f g of a TM tape as a natural number and talk about f : N N ! being a recursive function. If the partial, recursive function f is total, i.e., f : N N then we ! say that f is a total, recursive function. A partial function that is not total is called strictly partial. 1 CS601 Some Recursive Functions Lecture 2 Proposition 2.3 The following functions are recursive. They are all total except for peven. copy(w) = ww σ(n) = n + 1 plus(n; m) = n + m mult(n; m) = n m × exp(n; m) = nm (we let exp(0; 0) = 1) 1 if n is even χ (n) = even 0 otherwise 1 if n is even p (n) = even otherwise % Proof: Exercise: please convince yourself that you can build TMs to compute all of these functions! 2 Recursive Sets = Decidable Sets = Computable Sets Definition 2.4 Let S Σ? or S N.
    [Show full text]
  • Automata, Semigroups and Duality
    Automata, semigroups and duality Mai Gehrke1 Serge Grigorieff2 Jean-Eric´ Pin2 1Radboud Universiteit 2LIAFA, CNRS and University Paris Diderot TANCL’07, August 2007, Oxford LIAFA, CNRS and University Paris Diderot Outline (1) Four ways of defining languages (2) The profinite world (3) Duality (4) Back to the future LIAFA, CNRS and University Paris Diderot Part I Four ways of defining languages LIAFA, CNRS and University Paris Diderot Words and languages Words over the alphabet A = {a, b, c}: a, babb, cac, the empty word 1, etc. The set of all words A∗ is the free monoid on A. A language is a set of words. Recognizable (or regular) languages can be defined in various ways: ⊲ by (extended) regular expressions ⊲ by finite automata ⊲ in terms of logic ⊲ by finite monoids LIAFA, CNRS and University Paris Diderot Basic operations on languages • Boolean operations: union, intersection, complement. • Product: L1L2 = {u1u2 | u1 ∈ L1,u2 ∈ L2} Example: {ab, a}{a, ba} = {aa, aba, abba}. • Star: L∗ is the submonoid generated by L ∗ L = {u1u2 · · · un | n > 0 and u1,...,un ∈ L} {a, ba}∗ = {1, a, aa, ba, aaa, aba, . .}. LIAFA, CNRS and University Paris Diderot Various types of expressions • Regular expressions: union, product, star: (ab)∗ ∪ (ab)∗a • Extended regular expressions (union, intersection, complement, product and star): A∗ \ (bA∗ ∪ A∗aaA∗ ∪ A∗bbA∗) • Star-free expressions (union, intersection, complement, product but no star): ∅c \ (b∅c ∪∅caa∅c ∪∅cbb∅c) LIAFA, CNRS and University Paris Diderot Finite automata a 1 2 The set of states is {1, 2, 3}. b The initial state is 1. b a 3 The final states are 1 and 2.
    [Show full text]
  • Iris: Monoids and Invariants As an Orthogonal Basis for Concurrent Reasoning
    Iris: Monoids and Invariants as an Orthogonal Basis for Concurrent Reasoning Ralf Jung David Swasey Filip Sieczkowski Kasper Svendsen MPI-SWS & MPI-SWS Aarhus University Aarhus University Saarland University [email protected][email protected] [email protected] [email protected] rtifact * Comple * Aaron Turon Lars Birkedal Derek Dreyer A t te n * te A is W s E * e n l l C o L D C o P * * Mozilla Research Aarhus University MPI-SWS c u e m s O E u e e P n R t v e o d t * y * s E a [email protected] [email protected] [email protected] a l d u e a t Abstract TaDA [8], and others. In this paper, we present a logic called Iris that We present Iris, a concurrent separation logic with a simple premise: explains some of the complexities of these prior separation logics in monoids and invariants are all you need. Partial commutative terms of a simpler unifying foundation, while also supporting some monoids enable us to express—and invariants enable us to enforce— new and powerful reasoning principles for concurrency. user-defined protocols on shared state, which are at the conceptual Before we get to Iris, however, let us begin with a brief overview core of most recent program logics for concurrency. Furthermore, of some key problems that arise in reasoning compositionally about through a novel extension of the concept of a view shift, Iris supports shared state, and how prior approaches have dealt with them.
    [Show full text]
  • CAI 2017 Book of Abstracts.Pdf
    Table of Contents Track 1: Automata Theory and Logic ........................... 1 Invited speaker: Heiko Vogler . 1 Languages and formations generated by D4 and D8: Jean-Éric Pin, Xaro Soler-Escrivà . 2 Syntactic structures of regular languages: O. Klíma, L. Polák . 26 Improving witnesses for state complexity of catenation combined with boolean operations: P. Caron, J.-G. Luque, B. Patrou . 44 Track 2: Cryptography and Coding Theory ..................... 63 Invited speaker: Claude Carlet . 63 A topological approach to network coding: Cristina Martínez and Alberto Besana . 64 Pairing-friendly elliptic curves resistant to TNFS attacks: G. Fotiadis, E. Konstantinou . 65 Collaborative multi-authority key-policy attribute-based encryption for shorter keys and parameters: R. Longo, C. Marcolla, M. Sala 67 Conditional blind signatures: A. Zacharakis, P. Grontas, A. Pagourtzis . 68 Hash function design for cloud storage data auditing: Nikolaos Doukas, Oleksandr P. Markovskyi, Nikolaos G. Bardis . 69 Method for accelerated zero-knowledge identification of remote users based on standard block ciphers: Nikolaos G. Bardis, Oleksandr P. Markovskyi, Nikolaos Doukas . 81 Determining whether a given block cipher is a permutation of another given block cipher— a problem in intellectual property (Extended Abstract): G. V. Bard . 91 Track 3: Computer Algebra ..................................... 95 Invited speaker: Michael Wibmer . 95 Interpolation of syzygies for implicit matrix representations: Ioannis Z. Emiris, Konstantinos Gavriil, and Christos Konaxis . 97 Reduction in free modules: C. Fürst, G. Landsmann . 115 Instructing small cellular free resolutions for monomial ideals: J. Àlvarez Montaner, O. Fernández-Ramos, P. Gimenez . 117 Low autocorrelation binary sequences (LABS): lias S. Kotsireas . 123 A signature based border basis algorithm: J. Horáček, M.
    [Show full text]
  • Sage 9.4 Reference Manual: Monoids Release 9.4
    Sage 9.4 Reference Manual: Monoids Release 9.4 The Sage Development Team Aug 24, 2021 CONTENTS 1 Monoids 3 2 Free Monoids 5 3 Elements of Free Monoids 9 4 Free abelian monoids 11 5 Abelian Monoid Elements 15 6 Indexed Monoids 17 7 Free String Monoids 23 8 String Monoid Elements 29 9 Utility functions on strings 33 10 Hecke Monoids 35 11 Automatic Semigroups 37 12 Module of trace monoids (free partially commutative monoids). 47 13 Indices and Tables 55 Python Module Index 57 Index 59 i ii Sage 9.4 Reference Manual: Monoids, Release 9.4 Sage supports free monoids and free abelian monoids in any finite number of indeterminates, as well as free partially commutative monoids (trace monoids). CONTENTS 1 Sage 9.4 Reference Manual: Monoids, Release 9.4 2 CONTENTS CHAPTER ONE MONOIDS class sage.monoids.monoid.Monoid_class(names) Bases: sage.structure.parent.Parent EXAMPLES: sage: from sage.monoids.monoid import Monoid_class sage: Monoid_class(('a','b')) <sage.monoids.monoid.Monoid_class_with_category object at ...> gens() Returns the generators for self. EXAMPLES: sage: F.<a,b,c,d,e>= FreeMonoid(5) sage: F.gens() (a, b, c, d, e) monoid_generators() Returns the generators for self. EXAMPLES: sage: F.<a,b,c,d,e>= FreeMonoid(5) sage: F.monoid_generators() Family (a, b, c, d, e) sage.monoids.monoid.is_Monoid(x) Returns True if x is of type Monoid_class. EXAMPLES: sage: from sage.monoids.monoid import is_Monoid sage: is_Monoid(0) False sage: is_Monoid(ZZ) # The technical math meaning of monoid has ....: # no bearing whatsoever on the result: it's ....: # a typecheck which is not satisfied by ZZ ....: # since it does not inherit from Monoid_class.
    [Show full text]
  • Enumerations of the Kolmogorov Function
    Enumerations of the Kolmogorov Function Richard Beigela Harry Buhrmanb Peter Fejerc Lance Fortnowd Piotr Grabowskie Luc Longpr´ef Andrej Muchnikg Frank Stephanh Leen Torenvlieti Abstract A recursive enumerator for a function h is an algorithm f which enu- merates for an input x finitely many elements including h(x). f is a aEmail: [email protected]. Department of Computer and Information Sciences, Temple University, 1805 North Broad Street, Philadelphia PA 19122, USA. Research per- formed in part at NEC and the Institute for Advanced Study. Supported in part by a State of New Jersey grant and by the National Science Foundation under grants CCR-0049019 and CCR-9877150. bEmail: [email protected]. CWI, Kruislaan 413, 1098SJ Amsterdam, The Netherlands. Partially supported by the EU through the 5th framework program FET. cEmail: [email protected]. Department of Computer Science, University of Mas- sachusetts Boston, Boston, MA 02125, USA. dEmail: [email protected]. Department of Computer Science, University of Chicago, 1100 East 58th Street, Chicago, IL 60637, USA. Research performed in part at NEC Research Institute. eEmail: [email protected]. Institut f¨ur Informatik, Im Neuenheimer Feld 294, 69120 Heidelberg, Germany. fEmail: [email protected]. Computer Science Department, UTEP, El Paso, TX 79968, USA. gEmail: [email protected]. Institute of New Techologies, Nizhnyaya Radi- shevskaya, 10, Moscow, 109004, Russia. The work was partially supported by Russian Foundation for Basic Research (grants N 04-01-00427, N 02-01-22001) and Council on Grants for Scientific Schools. hEmail: [email protected]. School of Computing and Department of Mathe- matics, National University of Singapore, 3 Science Drive 2, Singapore 117543, Republic of Singapore.
    [Show full text]
  • Monoids (I = 1, 2) We Can Define 1  C 2000 M
    Geometria Superiore Reference Cards Push out (Sommes amalgam´ees). Given a diagram i : P ! Qi Monoids (i = 1; 2) we can define 1 c 2000 M. Cailotto, Permissions on last. v0.0 u −!2 Send comments and corrections to [email protected] Q1 ⊕P Q2 := coker P −! Q1 ⊕ Q2 u1 The category of Monoids. 1 and it is a standard fact that the natural arrows ji : Qi ! A monoid (M; ·; 1) (commutative with unit) is a set M with a Q1 ⊕P Q2 define a cocartesian square: u1 composition law · : M × M ! M and an element 1 2 M such P −−−! Q1 that: the composition is associative: (a · b) · c = a · (b · c) (for ? ? all a; b; c 2 M), commutative: a · b = b · a (for all a; b 2 M), u2 y y j1 and 1 is a neutral element for the composition: 1 · a = a (for Q2 −! Q1 ⊕P Q2 : all a 2 M). j2 ' ' More explicitly we have Q1 ⊕P Q2 = (Q1 ⊕ Q2)=R with R the A morphism (M; ·; 1M ) −!(N; ·; 1N ) of monoids is a map M ! N smallest equivalence relation stable under product (in Q1 ⊕P of sets commuting with · and 1: '(ab) = '(a)'(b) for all Q2) and making (u1(p); 1) ∼R (1; u2(p)) for all p 2 P . a; b 2 M and '(1 ) = 1 . Thus we have defined the cat- M N In general it is not easy to understand the relation involved in egory Mon of monoids. the description of Q1 ⊕P Q2, but in the case in which one of f1g is a monoid, initial and final object of the category Mon.
    [Show full text]
  • Division by Zero in Logic and Computing Jan Bergstra
    Division by Zero in Logic and Computing Jan Bergstra To cite this version: Jan Bergstra. Division by Zero in Logic and Computing. 2021. hal-03184956v2 HAL Id: hal-03184956 https://hal.archives-ouvertes.fr/hal-03184956v2 Preprint submitted on 19 Apr 2021 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. DIVISION BY ZERO IN LOGIC AND COMPUTING JAN A. BERGSTRA Abstract. The phenomenon of division by zero is considered from the per- spectives of logic and informatics respectively. Division rather than multi- plicative inverse is taken as the point of departure. A classification of views on division by zero is proposed: principled, physics based principled, quasi- principled, curiosity driven, pragmatic, and ad hoc. A survey is provided of different perspectives on the value of 1=0 with for each view an assessment view from the perspectives of logic and computing. No attempt is made to survey the long and diverse history of the subject. 1. Introduction In the context of rational numbers the constants 0 and 1 and the operations of addition ( + ) and subtraction ( − ) as well as multiplication ( · ) and division ( = ) play a key role. When starting with a binary primitive for subtraction unary opposite is an abbreviation as follows: −x = 0 − x, and given a two-place division function unary inverse is an abbreviation as follows: x−1 = 1=x.
    [Show full text]
  • Division by Zero: a Survey of Options
    Division by Zero: A Survey of Options Jan A. Bergstra Informatics Institute, University of Amsterdam Science Park, 904, 1098 XH, Amsterdam, The Netherlands [email protected], [email protected] Submitted: 12 May 2019 Revised: 24 June 2019 Abstract The idea that, as opposed to the conventional viewpoint, division by zero may produce a meaningful result, is long standing and has attracted inter- est from many sides. We provide a survey of some options for defining an outcome for the application of division in case the second argument equals zero. The survey is limited by a combination of simplifying assumptions which are grouped together in the idea of a premeadow, which generalises the notion of an associative transfield. 1 Introduction The number of options available for assigning a meaning to the expression 1=0 is remarkably large. In order to provide an informative survey of such options some conditions may be imposed, thereby reducing the number of options. I will understand an option for division by zero as an arithmetical datatype, i.e. an algebra, with the following signature: • a single sort with name V , • constants 0 (zero) and 1 (one) for sort V , • 2-place functions · (multiplication) and + (addition), • unary functions − (additive inverse, also called opposite) and −1 (mul- tiplicative inverse), • 2 place functions − (subtraction) and = (division). Decimal notations like 2; 17; −8 are used as abbreviations, e.g. 2 = 1 + 1, and −3 = −((1 + 1) + 1). With inverse the multiplicative inverse is meant, while the additive inverse is referred to as opposite. 1 This signature is referred to as the signature of meadows ΣMd in [6], with the understanding that both inverse and division (and both opposite and sub- traction) are present.
    [Show full text]
  • A Quantum Query Complexity Trichotomy for Regular Languages
    A Quantum Query Complexity Trichotomy for Regular Languages Scott Aaronson∗ Daniel Grier† Luke Schaeffer UT Austin MIT MIT [email protected] [email protected] [email protected] Abstract We present a trichotomy theorem for the quantum query complexity of regular languages. Every regular language has quantum query complexity Θ(1), Θ˜ (√n), or Θ(n). The extreme uniformity of regular languages prevents them from taking any other asymptotic complexity. This is in contrast to even the context-free languages, which we show can have query complex- ity Θ(nc) for all computable c [1/2,1]. Our result implies an equivalent trichotomy for the approximate degree of regular∈ languages, and a dichotomy—either Θ(1) or Θ(n)—for sensi- tivity, block sensitivity, certificate complexity, deterministic query complexity, and randomized query complexity. The heart of the classification theorem is an explicit quantum algorithm which decides membership in any star-free language in O˜ (√n) time. This well-studied family of the regu- lar languages admits many interesting characterizations, for instance, as those languages ex- pressible as sentences in first-order logic over the natural numbers with the less-than relation. Therefore, not only do the star-free languages capture functions such as OR, they can also ex- press functions such as “there exist a pair of 2’s such that everything between them is a 0.” Thus, we view the algorithm for star-free languages as a nontrivial generalization of Grover’s algorithm which extends the quantum quadratic speedup to a much wider range of string- processing algorithms than was previously known.
    [Show full text]
  • Quantum Conditional Strategies for Prisoners' Dilemmata Under The
    Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 30 May 2019 doi:10.20944/preprints201905.0366.v1 Peer-reviewed version available at Appl. Sci. 2019, 9, 2635; doi:10.3390/app9132635 Article Quantum conditional strategies for prisoners’ dilemmata under the EWL scheme Konstantinos Giannakis* , Georgia Theocharopoulou, Christos Papalitsas, Sofia Fanarioti, and Theodore Andronikos* Department of Informatics, Ionian University, Tsirigoti Square 7, Corfu, 49100, Greece; {kgiann, zeta.theo, c14papa, sofiafanar, andronikos}@ionio.gr * Correspondence: [email protected] (K.G.); [email protected] (Th.A.) 1 Abstract: Classic game theory is an important field with a long tradition of useful results. Recently, 2 the quantum versions of classical games, such as the Prisoner’s Dilemma (PD), have attracted a lot of 3 attention. Similarly, state machines and specifically finite automata have also been under constant 4 and thorough study for plenty of reasons. The quantum analogues of these abstract machines, like the 5 quantum finite automata, have been studied extensively. In this work, we examine some well-known 6 game conditional strategies that have been studied within the framework of the repeated PD game. 7 Then, we try to associate these strategies to proper quantum finite automata that receive them as 8 inputs and recognize them with probability 1, achieving some interesting results. We also study the 9 quantum version of PD under the Eisert-Wilkens-Lewenstein scheme, proposing a novel conditional 10 strategy for the repeated version of this game. 11 Keywords: quantum game theory, quantum automata, prisoner’s dilemma, conditional strategies, 12 quantum strategies 13 1. Introduction 14 Quantum game theory has gained a lot of research interest since the first pioneering works of the 15 late ’90s [1–5].
    [Show full text]