Comparison Semigroups and Function Algebra
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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. -
The Matrix Calculus You Need for Deep Learning
The Matrix Calculus You Need For Deep Learning Terence Parr and Jeremy Howard July 3, 2018 (We teach in University of San Francisco's MS in Data Science program and have other nefarious projects underway. You might know Terence as the creator of the ANTLR parser generator. For more material, see Jeremy's fast.ai courses and University of San Francisco's Data Institute in- person version of the deep learning course.) HTML version (The PDF and HTML were generated from markup using bookish) Abstract This paper is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks. We assume no math knowledge beyond what you learned in calculus 1, and provide links to help you refresh the necessary math where needed. Note that you do not need to understand this material before you start learning to train and use deep learning in practice; rather, this material is for those who are already familiar with the basics of neural networks, and wish to deepen their understanding of the underlying math. Don't worry if you get stuck at some point along the way|just go back and reread the previous section, and try writing down and working through some examples. And if you're still stuck, we're happy to answer your questions in the Theory category at forums.fast.ai. Note: There is a reference section at the end of the paper summarizing all the key matrix calculus rules and terminology discussed here. arXiv:1802.01528v3 [cs.LG] 2 Jul 2018 1 Contents 1 Introduction 3 2 Review: Scalar derivative rules4 3 Introduction to vector calculus and partial derivatives5 4 Matrix calculus 6 4.1 Generalization of the Jacobian . -
IVC Factsheet Functions Comp Inverse
Imperial Valley College Math Lab Functions: Composition and Inverse Functions FUNCTION COMPOSITION In order to perform a composition of functions, it is essential to be familiar with function notation. If you see something of the form “푓(푥) = [expression in terms of x]”, this means that whatever you see in the parentheses following f should be substituted for x in the expression. This can include numbers, variables, other expressions, and even other functions. EXAMPLE: 푓(푥) = 4푥2 − 13푥 푓(2) = 4 ∙ 22 − 13(2) 푓(−9) = 4(−9)2 − 13(−9) 푓(푎) = 4푎2 − 13푎 푓(푐3) = 4(푐3)2 − 13푐3 푓(ℎ + 5) = 4(ℎ + 5)2 − 13(ℎ + 5) Etc. A composition of functions occurs when one function is “plugged into” another function. The notation (푓 ○푔)(푥) is pronounced “푓 of 푔 of 푥”, and it literally means 푓(푔(푥)). In other words, you “plug” the 푔(푥) function into the 푓(푥) function. Similarly, (푔 ○푓)(푥) is pronounced “푔 of 푓 of 푥”, and it literally means 푔(푓(푥)). In other words, you “plug” the 푓(푥) function into the 푔(푥) function. WARNING: Be careful not to confuse (푓 ○푔)(푥) with (푓 ∙ 푔)(푥), which means 푓(푥) ∙ 푔(푥) . EXAMPLES: 푓(푥) = 4푥2 − 13푥 푔(푥) = 2푥 + 1 a. (푓 ○푔)(푥) = 푓(푔(푥)) = 4[푔(푥)]2 − 13 ∙ 푔(푥) = 4(2푥 + 1)2 − 13(2푥 + 1) = [푠푚푝푙푓푦] … = 16푥2 − 10푥 − 9 b. (푔 ○푓)(푥) = 푔(푓(푥)) = 2 ∙ 푓(푥) + 1 = 2(4푥2 − 13푥) + 1 = 8푥2 − 26푥 + 1 A function can even be “composed” with itself: c. -
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] fi[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. -
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. -
The Logic of Recursive Equations Author(S): A
The Logic of Recursive Equations Author(s): A. J. C. Hurkens, Monica McArthur, Yiannis N. Moschovakis, Lawrence S. Moss, Glen T. Whitney Source: The Journal of Symbolic Logic, Vol. 63, No. 2 (Jun., 1998), pp. 451-478 Published by: Association for Symbolic Logic Stable URL: http://www.jstor.org/stable/2586843 . Accessed: 19/09/2011 22:53 Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at . http://www.jstor.org/page/info/about/policies/terms.jsp JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected]. Association for Symbolic Logic is collaborating with JSTOR to digitize, preserve and extend access to The Journal of Symbolic Logic. http://www.jstor.org THE JOURNAL OF SYMBOLIC LOGIC Volume 63, Number 2, June 1998 THE LOGIC OF RECURSIVE EQUATIONS A. J. C. HURKENS, MONICA McARTHUR, YIANNIS N. MOSCHOVAKIS, LAWRENCE S. MOSS, AND GLEN T. WHITNEY Abstract. We study logical systems for reasoning about equations involving recursive definitions. In particular, we are interested in "propositional" fragments of the functional language of recursion FLR [18, 17], i.e., without the value passing or abstraction allowed in FLR. The 'pure," propositional fragment FLRo turns out to coincide with the iteration theories of [1]. Our main focus here concerns the sharp contrast between the simple class of valid identities and the very complex consequence relation over several natural classes of models. -
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. -
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. -
On Extensions of Partial Functions A.B
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Expo. Math. 25 (2007) 345–353 www.elsevier.de/exmath On extensions of partial functions A.B. Kharazishvilia,∗, A.P. Kirtadzeb aA. Razmadze Mathematical Institute, M. Alexidze Street, 1, Tbilisi 0193, Georgia bI. Vekua Institute of Applied Mathematics of Tbilisi State University, University Street, 2, Tbilisi 0143, Georgia Received 2 October 2006; received in revised form 15 January 2007 Abstract The problem of extending partial functions is considered from the general viewpoint. Some as- pects of this problem are illustrated by examples, which are concerned with typical real-valued partial functions (e.g. semicontinuous, monotone, additive, measurable, possessing the Baire property). ᭧ 2007 Elsevier GmbH. All rights reserved. MSC 2000: primary: 26A1526A21; 26A30; secondary: 28B20 Keywords: Partial function; Extension of a partial function; Semicontinuous function; Monotone function; Measurable function; Sierpi´nski–Zygmund function; Absolutely nonmeasurable function; Extension of a measure In this note we would like to consider several facts from mathematical analysis concerning extensions of real-valued partial functions. Some of those facts are rather easy and are accessible to average-level students. But some of them are much deeper, important and have applications in various branches of mathematics. The best known example of this type is the famous Tietze–Urysohn theorem, which states that every real-valued continuous function defined on a closed subset of a normal topological space can be extended to a real- valued continuous function defined on the whole space (see, for instance, [8], Chapter 1, Section 14). -
Monoids: Theme and Variations (Functional Pearl)
University of Pennsylvania ScholarlyCommons Departmental Papers (CIS) Department of Computer & Information Science 7-2012 Monoids: Theme and Variations (Functional Pearl) Brent A. Yorgey University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/cis_papers Part of the Programming Languages and Compilers Commons, Software Engineering Commons, and the Theory and Algorithms Commons Recommended Citation Brent A. Yorgey, "Monoids: Theme and Variations (Functional Pearl)", . July 2012. This paper is posted at ScholarlyCommons. https://repository.upenn.edu/cis_papers/762 For more information, please contact [email protected]. Monoids: Theme and Variations (Functional Pearl) Abstract The monoid is a humble algebraic structure, at first glance ve en downright boring. However, there’s much more to monoids than meets the eye. Using examples taken from the diagrams vector graphics framework as a case study, I demonstrate the power and beauty of monoids for library design. The paper begins with an extremely simple model of diagrams and proceeds through a series of incremental variations, all related somehow to the central theme of monoids. Along the way, I illustrate the power of compositional semantics; why you should also pay attention to the monoid’s even humbler cousin, the semigroup; monoid homomorphisms; and monoid actions. Keywords monoid, homomorphism, monoid action, EDSL Disciplines Programming Languages and Compilers | Software Engineering | Theory and Algorithms This conference paper is available at ScholarlyCommons: https://repository.upenn.edu/cis_papers/762 Monoids: Theme and Variations (Functional Pearl) Brent A. Yorgey University of Pennsylvania [email protected] Abstract The monoid is a humble algebraic structure, at first glance even downright boring. -
Contents 3 Homomorphisms, Ideals, and Quotients
Ring Theory (part 3): Homomorphisms, Ideals, and Quotients (by Evan Dummit, 2018, v. 1.01) Contents 3 Homomorphisms, Ideals, and Quotients 1 3.1 Ring Isomorphisms and Homomorphisms . 1 3.1.1 Ring Isomorphisms . 1 3.1.2 Ring Homomorphisms . 4 3.2 Ideals and Quotient Rings . 7 3.2.1 Ideals . 8 3.2.2 Quotient Rings . 9 3.2.3 Homomorphisms and Quotient Rings . 11 3.3 Properties of Ideals . 13 3.3.1 The Isomorphism Theorems . 13 3.3.2 Generation of Ideals . 14 3.3.3 Maximal and Prime Ideals . 17 3.3.4 The Chinese Remainder Theorem . 20 3.4 Rings of Fractions . 21 3 Homomorphisms, Ideals, and Quotients In this chapter, we will examine some more intricate properties of general rings. We begin with a discussion of isomorphisms, which provide a way of identifying two rings whose structures are identical, and then examine the broader class of ring homomorphisms, which are the structure-preserving functions from one ring to another. Next, we study ideals and quotient rings, which provide the most general version of modular arithmetic in a ring, and which are fundamentally connected with ring homomorphisms. We close with a detailed study of the structure of ideals and quotients in commutative rings with 1. 3.1 Ring Isomorphisms and Homomorphisms • We begin our study with a discussion of structure-preserving maps between rings. 3.1.1 Ring Isomorphisms • We have encountered several examples of rings with very similar structures. • For example, consider the two rings R = Z=6Z and S = (Z=2Z) × (Z=3Z). -
Associativity and Confluence
Journal of Pure and Applied Mathematics: Advances and Applications Volume 3, Number 2, 2010, Pages 265-285 PARTIAL MONOIDS: ASSOCIATIVITY AND CONFLUENCE LAURENT POINSOT, GÉRARD H. E. DUCHAMP and CHRISTOPHE TOLLU Institut Galilée Laboratoire d’Informatique de Paris-Nord Université Paris-Nord UMR CNRS 7030 F-93430 Villetaneuse France e-mail: [email protected] Abstract A partial monoid P is a set with a partial multiplication × (and total identity 1P ), which satisfies some associativity axiom. The partial monoid P may be embedded in the free monoid P ∗ and the product × is simulated by a string rewriting system on P ∗ that consists in evaluating the concatenation of two letters as a product in P, when it is defined, and a letter 1P as the empty word . In this paper, we study the profound relations between confluence for such a system and associativity of the multiplication. Moreover, we develop a reduction strategy to ensure confluence, and which allows us to define a multiplication on normal forms associative up to a given congruence of P ∗. Finally, we show that this operation is associative, if and only if, the rewriting system under consideration is confluent. 2010 Mathematics Subject Classification: 16S15, 20M05, 68Q42. Keywords and phrases: partial monoid, string rewriting system, normal form, associativity and confluence. Received May 28, 2010 2010 Scientific Advances Publishers 266 LAURENT POINSOT et al. 1. Introduction A partial monoid is a set equipped with a partially-defined multiplication, say ×, which is associative in the sense that (x × y) × z = x × ()y × z means that the left-hand side is defined, if and only if the right-hand side is defined, and in this situation, they are equal.