Categories and Computability
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Finite Sum – Product Logic
Theory and Applications of Categories, Vol. 8, No. 5, pp. 63–99. FINITE SUM – PRODUCT LOGIC J.R.B. COCKETT1 AND R.A.G. SEELY2 ABSTRACT. In this paper we describe a deductive system for categories with finite products and coproducts, prove decidability of equality of morphisms via cut elimina- tion, and prove a “Whitman theorem” for the free such categories over arbitrary base categories. This result provides a nice illustration of some basic techniques in categorical proof theory, and also seems to have slipped past unproved in previous work in this field. Furthermore, it suggests a type-theoretic approach to 2–player input–output games. Introduction In the late 1960’s Lambekintroduced the notion of a “deductive system”, by which he meant the presentation of a sequent calculus for a logic as a category, whose objects were formulas of the logic, and whose arrows were (equivalence classes of) sequent deriva- tions. He noticed that “doctrines” of categories corresponded under this construction to certain logics. The classic example of this was cartesian closed categories, which could then be regarded as the “proof theory” for the ∧ – ⇒ fragment of intuitionistic propo- sitional logic. (An excellent account of this may be found in the classic monograph [Lambek–Scott 1986].) Since his original work, many categorical doctrines have been given similar analyses, but it seems one simple case has been overlooked, viz. the doctrine of categories with finite products and coproducts (without any closed structure and with- out any extra assumptions concerning distributivity of the one over the other). We began looking at this case with the thought that it would provide a nice simple introduction to some techniques in categorical proof theory, particularly the idea of rewriting systems modulo equations, which we have found useful in investigating categorical structures with two tensor products (“linearly distributive categories” [Blute et al. -
MAT 4162 - Homework Assignment 2
MAT 4162 - Homework Assignment 2 Instructions: Pick at least 5 problems of varying length and difficulty. You do not have to provide full details in all of the problems, but be sure to indicate which details you declare trivial. Due date: Monday June 22nd, 4pm. The category of relations Let Rel be the category whose objects are sets and whose morphisms X ! Y are relations R ⊆ X × Y . Two relations R ⊆ X × Y and S ⊆ Y × Z may be composed via S ◦ R = f(x; z)j9y 2 Y:R(x; y) ^ S(y; z): Exercise 1. Show that this composition is associative, and that Rel is indeed a category. The powerset functor(s) Consider the powerset functor P : Set ! Set. On objects, it sends a set X to its powerset P(X). A function f : X ! Y is sent to P(f): P(X) !P(Y ); U 7! P(f)(U) = f[U] =def ff(x)jx 2 Ug: Exercise 2. Show that P is indeed a functor. For every set X we have a singleton map ηX : X !P(X), defined by 2 S x 7! fxg. Also, we have a union map µX : P (X) !P(X), defined by α 7! α. Exercise 3. Show that η and µ constitute natural transformations 1Set !P and P2 !P, respectively. Objects of the form P(X) are more than mere sets. In class you have seen that they are in fact complete boolean algebras. For now, we regard P(X) as a complete sup-lattice. A partial ordering (P; ≤) is a complete sup-lattice if it is equipped with a supremum map W : P(P ) ! P which sends a subset U ⊆ P to W P , the least upper bound of U in P . -
Relations in Categories
Relations in Categories Stefan Milius A thesis submitted to the Faculty of Graduate Studies in partial fulfilment of the requirements for the degree of Master of Arts Graduate Program in Mathematics and Statistics York University Toronto, Ontario June 15, 2000 Abstract This thesis investigates relations over a category C relative to an (E; M)-factori- zation system of C. In order to establish the 2-category Rel(C) of relations over C in the first part we discuss sufficient conditions for the associativity of horizontal composition of relations, and we investigate special classes of morphisms in Rel(C). Attention is particularly devoted to the notion of mapping as defined by Lawvere. We give a significantly simplified proof for the main result of Pavlovi´c,namely that C Map(Rel(C)) if and only if E RegEpi(C). This part also contains a proof' that the category Map(Rel(C))⊆ is finitely complete, and we present the results obtained by Kelly, some of them generalized, i. e., without the restrictive assumption that M Mono(C). The next part deals with factorization⊆ systems in Rel(C). The fact that each set-relation has a canonical image factorization is generalized and shown to yield an (E¯; M¯ )-factorization system in Rel(C) in case M Mono(C). The setting without this condition is studied, as well. We propose a⊆ weaker notion of factorization system for a 2-category, where the commutativity in the universal property of an (E; M)-factorization system is replaced by coherent 2-cells. In the last part certain limits and colimits in Rel(C) are investigated. -
Chapter 2 of Concrete Abstractions: an Introduction to Computer
Out of print; full text available for free at http://www.gustavus.edu/+max/concrete-abstractions.html CHAPTER TWO Recursion and Induction 2.1 Recursion We have used Scheme to write procedures that describe how certain computational processes can be carried out. All the procedures we've discussed so far generate processes of a ®xed size. For example, the process generated by the procedure square always does exactly one multiplication no matter how big or how small the number we're squaring is. Similarly, the procedure pinwheel generates a process that will do exactly the same number of stack and turn operations when we use it on a basic block as it will when we use it on a huge quilt that's 128 basic blocks long and 128 basic blocks wide. Furthermore, the size of the procedure (that is, the size of the procedure's text) is a good indicator of the size of the processes it generates: Small procedures generate small processes and large procedures generate large processes. On the other hand, there are procedures of a ®xed size that generate computa- tional processes of varying sizes, depending on the values of their parameters, using a technique called recursion. To illustrate this, the following is a small, ®xed-size procedure for making paper chains that can still make chains of arbitrary lengthÐ it has a parameter n for the desired length. You'll need a bunch of long, thin strips of paper and some way of joining the ends of a strip to make a loop. You can use tape, a stapler, or if you use slitted strips of cardstock that look like this , you can just slip the slits together. -
Knowledge Representation in Bicategories of Relations
Knowledge Representation in Bicategories of Relations Evan Patterson Department of Statistics, Stanford University Abstract We introduce the relational ontology log, or relational olog, a knowledge representation system based on the category of sets and relations. It is inspired by Spivak and Kent’s olog, a recent categorical framework for knowledge representation. Relational ologs interpolate between ologs and description logic, the dominant formalism for knowledge representation today. In this paper, we investigate relational ologs both for their own sake and to gain insight into the relationship between the algebraic and logical approaches to knowledge representation. On a practical level, we show by example that relational ologs have a friendly and intuitive—yet fully precise—graphical syntax, derived from the string diagrams of monoidal categories. We explain several other useful features of relational ologs not possessed by most description logics, such as a type system and a rich, flexible notion of instance data. In a more theoretical vein, we draw on categorical logic to show how relational ologs can be translated to and from logical theories in a fragment of first-order logic. Although we make extensive use of categorical language, this paper is designed to be self-contained and has considerable expository content. The only prerequisites are knowledge of first-order logic and the rudiments of category theory. 1. Introduction arXiv:1706.00526v2 [cs.AI] 1 Nov 2017 The representation of human knowledge in computable form is among the oldest and most fundamental problems of artificial intelligence. Several recent trends are stimulating continued research in the field of knowledge representation (KR). -
Arxiv:1811.04966V3 [Math.NT]
Descartes’ rule of signs, Newton polygons, and polynomials over hyperfields Matthew Baker and Oliver Lorscheid Abstract. In this note, we develop a theory of multiplicities of roots for polynomials over hyperfields and use this to provide a unified and conceptual proof of both Descartes’ rule of signs and Newton’s “polygon rule”. Introduction Given a real polynomial p ∈ R[T ], Descartes’ rule of signs provides an upper bound for the number of positive (resp. negative) real roots of p in terms of the signs of the coeffi- cients of p. Specifically, the number of positive real roots of p (counting multiplicities) is bounded above by the number of sign changes in the coefficients of p(T ), and the number of negative roots is bounded above by the number of sign changes in the coefficients of p(−T ). Another classical “rule”, which is less well known to mathematicians in general but is used quite often in number theory, is Newton’s polygon rule. This rule concerns polynomi- als over fields equipped with a valuation, which is a function v : K → R ∪{∞} satisfying • v(a) = ∞ if and only if a = 0 • v(ab) = v(a) + v(b) • v(a + b) > min{v(a),v(b)}, with equality if v(a) 6= v(b) for all a,b ∈ K. An example is the p-adic valuation vp on Q, where p is a prime number, given by the formula vp(s/t) = ordp(s) − ordp(t), where ordp(n) is the maximum power of p dividing a nonzero integer n. Another example is the T -adic valuation v on k(T), for any field k, given by v ( f /g) = arXiv:1811.04966v3 [math.NT] 26 May 2020 T T ordT ( f )−ordT (g), where ordT ( f ) is the maximum power of T dividing a nonzero polyno- mial f ∈ k[T]. -
Diagrammatics in Categorification and Compositionality
Diagrammatics in Categorification and Compositionality by Dmitry Vagner Department of Mathematics Duke University Date: Approved: Ezra Miller, Supervisor Lenhard Ng Sayan Mukherjee Paul Bendich Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Mathematics in the Graduate School of Duke University 2019 ABSTRACT Diagrammatics in Categorification and Compositionality by Dmitry Vagner Department of Mathematics Duke University Date: Approved: Ezra Miller, Supervisor Lenhard Ng Sayan Mukherjee Paul Bendich An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Mathematics in the Graduate School of Duke University 2019 Copyright c 2019 by Dmitry Vagner All rights reserved Abstract In the present work, I explore the theme of diagrammatics and their capacity to shed insight on two trends|categorification and compositionality|in and around contemporary category theory. The work begins with an introduction of these meta- phenomena in the context of elementary sets and maps. Towards generalizing their study to more complicated domains, we provide a self-contained treatment|from a pedagogically novel perspective that introduces almost all concepts via diagrammatic language|of the categorical machinery with which we may express the broader no- tions found in the sequel. The work then branches into two seemingly unrelated disciplines: dynamical systems and knot theory. In particular, the former research defines what it means to compose dynamical systems in a manner analogous to how one composes simple maps. The latter work concerns the categorification of the slN link invariant. In particular, we use a virtual filtration to give a more diagrammatic reconstruction of Khovanov-Rozansky homology via a smooth TQFT. -
Relations: Categories, Monoidal Categories, and Props
Logical Methods in Computer Science Vol. 14(3:14)2018, pp. 1–25 Submitted Oct. 12, 2017 https://lmcs.episciences.org/ Published Sep. 03, 2018 UNIVERSAL CONSTRUCTIONS FOR (CO)RELATIONS: CATEGORIES, MONOIDAL CATEGORIES, AND PROPS BRENDAN FONG AND FABIO ZANASI Massachusetts Institute of Technology, United States of America e-mail address: [email protected] University College London, United Kingdom e-mail address: [email protected] Abstract. Calculi of string diagrams are increasingly used to present the syntax and algebraic structure of various families of circuits, including signal flow graphs, electrical circuits and quantum processes. In many such approaches, the semantic interpretation for diagrams is given in terms of relations or corelations (generalised equivalence relations) of some kind. In this paper we show how semantic categories of both relations and corelations can be characterised as colimits of simpler categories. This modular perspective is important as it simplifies the task of giving a complete axiomatisation for semantic equivalence of string diagrams. Moreover, our general result unifies various theorems that are independently found in literature and are relevant for program semantics, quantum computation and control theory. 1. Introduction Network-style diagrammatic languages appear in diverse fields as a tool to reason about computational models of various kinds, including signal processing circuits, quantum pro- cesses, Bayesian networks and Petri nets, amongst many others. In the last few years, there have been more and more contributions towards a uniform, formal theory of these languages which borrows from the well-established methods of programming language semantics. A significant insight stemming from many such approaches is that a compositional analysis of network diagrams, enabling their reduction to elementary components, is more effective when system behaviour is thought of as a relation instead of a function. -
Discussion Problems 2
CS103 Handout 11 Fall 2014 October 6, 2014 Discussion Problems 2 Problem One: Product Parities Suppose that you have n integers x₁, x₂, …, xₙ. Prove that the product x₁ · x₂ · … · xₙ is odd if and only if each number xᵢ is odd. You might want to use the following facts: the product of zero numbers is called the empty product and is, by definition, equal to 1, and any two true state- ments imply one another. Problem Two: Prime Numbers A natural number p ≥ 2 is called prime if it has no positive divisors except 1 and itself. A natural number is called composite if it is the product of two natural numbers m and n, where both m and n are greater than one. Every natural number greater than or equal to two is either prime or composite. Prove, by strong induction, that every natural number greater than or equal to two can be written as a product of prime numbers. Problem Three: Factorials! Multiplied together! The number n! is defined as 1 × 2 × … × n. We choose to define 0! = 1. Prove by induction that for any m, n ∈ ℕ, we have m!n! ≤ (m + n)!. Can you explain intuitively why this is true? Problem Four: Picking Coins Consider the following game for two players. Begin with a pile of n coins for some n ≥ 0. The first player then takes between one and ten coins out of the pile, then the second player takes between one and ten coins out of the pile. This process repeats until some player has no coins to take; at this point, that player loses the game. -
Rewriting Structured Cospans: a Syntax for Open Systems
UNIVERSITY OF CALIFORNIA RIVERSIDE Rewriting Structured Cospans: A Syntax For Open Systems A Dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Mathematics by Daniel Cicala June 2019 Dissertation Committee: Dr. John C. Baez, Chairperson Dr. Wee Liang Gan Dr. Jacob Greenstein Copyright by Daniel Cicala 2019 The Dissertation of Daniel Cicala is approved: Committee Chairperson University of California, Riverside Acknowledgments First and foremost, I would like to thank my advisor John Baez. In these past few years, I have learned more than I could have imagined about mathematics and the job of doing mathematics. I also want to thank the past and current Baez Crew for the many wonderful discussions. I am indebted to Math Department at the University of California, Riverside, which has afforded me numerous opportunities to travel to conferences near and far. Almost certainly, I would never have had a chance to pursue my doctorate had it not been for my parents who were there for me through every twist and turn on this, perhaps, too scenic route that I traveled. Most importantly, this project would have been impossible without the full-hearted support of my love, Elizabeth. I would also like to acknowledge the previously published material in this disser- tation. The interchange law in Section 3.1 was published in [15]. The material in Sections 3.2 and 3.3 appear in [16]. Also, the ZX-calculus example in Section 4.3 appears in [18]. iv Elizabeth. It’s finally over, baby! v ABSTRACT OF THE DISSERTATION Rewriting Structured Cospans: A Syntax For Open Systems by Daniel Cicala Doctor of Philosophy, Graduate Program in Mathematics University of California, Riverside, June 2019 Dr. -
Logic and Categories As Tools for Building Theories
Logic and Categories As Tools For Building Theories Samson Abramsky Oxford University Computing Laboratory 1 Introduction My aim in this short article is to provide an impression of some of the ideas emerging at the interface of logic and computer science, in a form which I hope will be accessible to philosophers. Why is this even a good idea? Because there has been a huge interaction of logic and computer science over the past half-century which has not only played an important r^olein shaping Computer Science, but has also greatly broadened the scope and enriched the content of logic itself.1 This huge effect of Computer Science on Logic over the past five decades has several aspects: new ways of using logic, new attitudes to logic, new questions and methods. These lead to new perspectives on the question: What logic is | and should be! Our main concern is with method and attitude rather than matter; nevertheless, we shall base the general points we wish to make on a case study: Category theory. Many other examples could have been used to illustrate our theme, but this will serve to illustrate some of the points we wish to make. 2 Category Theory Category theory is a vast subject. It has enormous potential for any serious version of `formal philosophy' | and yet this has hardly been realized. We shall begin with introduction to some basic elements of category theory, focussing on the fascinating conceptual issues which arise even at the most elementary level of the subject, and then discuss some its consequences and philosophical ramifications. -
Indexed Collections Let I Be a Set (Finite of Infinite). If for Each Element
Indexed Collections Let I be a set (finite of infinite). If for each element i of I there is an associated object xi (exactly one for each i) then we say the collection xi,i∈ I is indexed by I. Each element i ∈ I is called and index and I is called the index set for this collection. The definition is quite general. Usually the set I is the set of natural numbers or positive integers, and the objects xi are numbers or sets. It is important to remember that there is no requirement for the objects associated with different elements of I to be distinct. That is, it is possible to have xi = xj when i = j. It is also important to remember that there is exactly one object associated with each index. Technically, the above corresponds to having a set X of objects and a function i : I → X. Instead of writing i(x) for element of x associated with i, instead we write xi (just different notation). Examples of indexed collections. 1. With each natural number n, associate a real number an. Then the indexed collection a0,a1,a2,... is what we usually think of as a sequence of real numbers. 2. For each positive integer i, let Ai be a set. Then we have the indexed collection of sets A1,A2,.... n Sigma notation. Let a0,a1,a2,... be a sequence of numbers. The notation i=k ai stands for the sum ak + ak+1 + ak+2 + ···an. n If k>n, then i=k ai contains no terms.