Categorical Notions of Fibration
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Experience Implementing a Performant Category-Theory Library in Coq
Experience Implementing a Performant Category-Theory Library in Coq Jason Gross, Adam Chlipala, and David I. Spivak Massachusetts Institute of Technology, Cambridge, MA, USA [email protected], [email protected], [email protected] Abstract. We describe our experience implementing a broad category- theory library in Coq. Category theory and computational performance are not usually mentioned in the same breath, but we have needed sub- stantial engineering effort to teach Coq to cope with large categorical constructions without slowing proof script processing unacceptably. In this paper, we share the lessons we have learned about how to repre- sent very abstract mathematical objects and arguments in Coq and how future proof assistants might be designed to better support such rea- soning. One particular encoding trick to which we draw attention al- lows category-theoretic arguments involving duality to be internalized in Coq's logic with definitional equality. Ours may be the largest Coq development to date that uses the relatively new Coq version developed by homotopy type theorists, and we reflect on which new features were especially helpful. Keywords: Coq · category theory · homotopy type theory · duality · performance 1 Introduction Category theory [36] is a popular all-encompassing mathematical formalism that casts familiar mathematical ideas from many domains in terms of a few unifying concepts. A category can be described as a directed graph plus algebraic laws stating equivalences between paths through the graph. Because of this spar- tan philosophical grounding, category theory is sometimes referred to in good humor as \formal abstract nonsense." Certainly the popular perception of cat- egory theory is quite far from pragmatic issues of implementation. -
A Category-Theoretic Approach to Representation and Analysis of Inconsistency in Graph-Based Viewpoints
A Category-Theoretic Approach to Representation and Analysis of Inconsistency in Graph-Based Viewpoints by Mehrdad Sabetzadeh A thesis submitted in conformity with the requirements for the degree of Master of Science Graduate Department of Computer Science University of Toronto Copyright c 2003 by Mehrdad Sabetzadeh Abstract A Category-Theoretic Approach to Representation and Analysis of Inconsistency in Graph-Based Viewpoints Mehrdad Sabetzadeh Master of Science Graduate Department of Computer Science University of Toronto 2003 Eliciting the requirements for a proposed system typically involves different stakeholders with different expertise, responsibilities, and perspectives. This may result in inconsis- tencies between the descriptions provided by stakeholders. Viewpoints-based approaches have been proposed as a way to manage incomplete and inconsistent models gathered from multiple sources. In this thesis, we propose a category-theoretic framework for the analysis of fuzzy viewpoints. Informally, a fuzzy viewpoint is a graph in which the elements of a lattice are used to specify the amount of knowledge available about the details of nodes and edges. By defining an appropriate notion of morphism between fuzzy viewpoints, we construct categories of fuzzy viewpoints and prove that these categories are (finitely) cocomplete. We then show how colimits can be employed to merge the viewpoints and detect the inconsistencies that arise independent of any particular choice of viewpoint semantics. Taking advantage of the same category-theoretic techniques used in defining fuzzy viewpoints, we will also introduce a more general graph-based formalism that may find applications in other contexts. ii To my mother and father with love and gratitude. Acknowledgements First of all, I wish to thank my supervisor Steve Easterbrook for his guidance, support, and patience. -
Category Theory
Michael Paluch Category Theory April 29, 2014 Preface These note are based in part on the the book [2] by Saunders Mac Lane and on the book [3] by Saunders Mac Lane and Ieke Moerdijk. v Contents 1 Foundations ....................................................... 1 1.1 Extensionality and comprehension . .1 1.2 Zermelo Frankel set theory . .3 1.3 Universes.....................................................5 1.4 Classes and Gödel-Bernays . .5 1.5 Categories....................................................6 1.6 Functors .....................................................7 1.7 Natural Transformations. .8 1.8 Basic terminology . 10 2 Constructions on Categories ....................................... 11 2.1 Contravariance and Opposites . 11 2.2 Products of Categories . 13 2.3 Functor Categories . 15 2.4 The category of all categories . 16 2.5 Comma categories . 17 3 Universals and Limits .............................................. 19 3.1 Universal Morphisms. 19 3.2 Products, Coproducts, Limits and Colimits . 20 3.3 YonedaLemma ............................................... 24 3.4 Free cocompletion . 28 4 Adjoints ........................................................... 31 4.1 Adjoint functors and universal morphisms . 31 4.2 Freyd’s adjoint functor theorem . 38 5 Topos Theory ...................................................... 43 5.1 Subobject classifier . 43 5.2 Sieves........................................................ 45 5.3 Exponentials . 47 vii viii Contents Index .................................................................. 53 Acronyms List of categories. Ab The category of small abelian groups and group homomorphisms. AlgA The category of commutative A-algebras. Cb The category Func(Cop,Sets). Cat The category of small categories and functors. CRings The category of commutative ring with an identity and ring homomor- phisms which preserve identities. Grp The category of small groups and group homomorphisms. Sets Category of small set and functions. Sets Category of small pointed set and pointed functions. -
Notes and Solutions to Exercises for Mac Lane's Categories for The
Stefan Dawydiak Version 0.3 July 2, 2020 Notes and Exercises from Categories for the Working Mathematician Contents 0 Preface 2 1 Categories, Functors, and Natural Transformations 2 1.1 Functors . .2 1.2 Natural Transformations . .4 1.3 Monics, Epis, and Zeros . .5 2 Constructions on Categories 6 2.1 Products of Categories . .6 2.2 Functor categories . .6 2.2.1 The Interchange Law . .8 2.3 The Category of All Categories . .8 2.4 Comma Categories . 11 2.5 Graphs and Free Categories . 12 2.6 Quotient Categories . 13 3 Universals and Limits 13 3.1 Universal Arrows . 13 3.2 The Yoneda Lemma . 14 3.2.1 Proof of the Yoneda Lemma . 14 3.3 Coproducts and Colimits . 16 3.4 Products and Limits . 18 3.4.1 The p-adic integers . 20 3.5 Categories with Finite Products . 21 3.6 Groups in Categories . 22 4 Adjoints 23 4.1 Adjunctions . 23 4.2 Examples of Adjoints . 24 4.3 Reflective Subcategories . 28 4.4 Equivalence of Categories . 30 4.5 Adjoints for Preorders . 32 4.5.1 Examples of Galois Connections . 32 4.6 Cartesian Closed Categories . 33 5 Limits 33 5.1 Creation of Limits . 33 5.2 Limits by Products and Equalizers . 34 5.3 Preservation of Limits . 35 5.4 Adjoints on Limits . 35 5.5 Freyd's adjoint functor theorem . 36 1 6 Chapter 6 38 7 Chapter 7 38 8 Abelian Categories 38 8.1 Additive Categories . 38 8.2 Abelian Categories . 38 8.3 Diagram Lemmas . 39 9 Special Limits 41 9.1 Interchange of Limits . -
A Subtle Introduction to Category Theory
A Subtle Introduction to Category Theory W. J. Zeng Department of Computer Science, Oxford University \Static concepts proved to be very effective intellectual tranquilizers." L. L. Whyte \Our study has revealed Mathematics as an array of forms, codify- ing ideas extracted from human activates and scientific problems and deployed in a network of formal rules, formal definitions, formal axiom systems, explicit theorems with their careful proof and the manifold in- terconnections of these forms...[This view] might be called formal func- tionalism." Saunders Mac Lane Let's dive right in then, shall we? What can we say about the array of forms MacLane speaks of? Claim (Tentative). Category theory is about the formal aspects of this array of forms. Commentary: It might be tempting to put the brakes on right away and first grab hold of what we mean by formal aspects. Instead, rather than trying to present \the formal" as the object of study and category theory as our instrument, we would nudge the reader to consider another perspective. Category theory itself defines formal aspects in much the same way that physics defines physical concepts or laws define legal (and correspondingly illegal) aspects: it embodies them. When we speak of what is formal/physical/legal we inescapably speak of category/physical/legal theory, and vice versa. Thus we pass to the somewhat grammatically awkward revision of our initial claim: Claim (Tentative). Category theory is the formal aspects of this array of forms. Commentary: Let's unpack this a little bit. While individual forms are themselves tautologically formal, arrays of forms and everything else networked in a system lose this tautological formality. -
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). -
Category Theory
CMPT 481/731 Functional Programming Fall 2008 Category Theory Category theory is a generalization of set theory. By having only a limited number of carefully chosen requirements in the definition of a category, we are able to produce a general algebraic structure with a large number of useful properties, yet permit the theory to apply to many different specific mathematical systems. Using concepts from category theory in the design of a programming language leads to some powerful and general programming constructs. While it is possible to use the resulting constructs without knowing anything about category theory, understanding the underlying theory helps. Category Theory F. Warren Burton 1 CMPT 481/731 Functional Programming Fall 2008 Definition of a Category A category is: 1. a collection of ; 2. a collection of ; 3. operations assigning to each arrow (a) an object called the domain of , and (b) an object called the codomain of often expressed by ; 4. an associative composition operator assigning to each pair of arrows, and , such that ,a composite arrow ; and 5. for each object , an identity arrow, satisfying the law that for any arrow , . Category Theory F. Warren Burton 2 CMPT 481/731 Functional Programming Fall 2008 In diagrams, we will usually express and (that is ) by The associative requirement for the composition operators means that when and are both defined, then . This allow us to think of arrows defined by paths throught diagrams. Category Theory F. Warren Burton 3 CMPT 481/731 Functional Programming Fall 2008 I will sometimes write for flip , since is less confusing than Category Theory F. -
Quasi-Categories Vs Simplicial Categories
Quasi-categories vs Simplicial categories Andr´eJoyal January 07 2007 Abstract We show that the coherent nerve functor from simplicial categories to simplicial sets is the right adjoint in a Quillen equivalence between the model category for simplicial categories and the model category for quasi-categories. Introduction A quasi-category is a simplicial set which satisfies a set of conditions introduced by Boardman and Vogt in their work on homotopy invariant algebraic structures [BV]. A quasi-category is often called a weak Kan complex in the literature. The category of simplicial sets S admits a Quillen model structure in which the cofibrations are the monomorphisms and the fibrant objects are the quasi- categories [J2]. We call it the model structure for quasi-categories. The resulting model category is Quillen equivalent to the model category for complete Segal spaces and also to the model category for Segal categories [JT2]. The goal of this paper is to show that it is also Quillen equivalent to the model category for simplicial categories via the coherent nerve functor of Cordier. We recall that a simplicial category is a category enriched over the category of simplicial sets S. To every simplicial category X we can associate a category X0 enriched over the homotopy category of simplicial sets Ho(S). A simplicial functor f : X → Y is called a Dwyer-Kan equivalence if the functor f 0 : X0 → Y 0 is an equivalence of Ho(S)-categories. It was proved by Bergner, that the category of (small) simplicial categories SCat admits a Quillen model structure in which the weak equivalences are the Dwyer-Kan equivalences [B1]. -
Parametrized Higher Category Theory
Parametrized higher category theory Jay Shah MIT May 1, 2017 Jay Shah (MIT) Parametrized higher category theory May 1, 2017 1 / 32 Answer: depends on the class of weak equivalences one inverts in the larger category of G-spaces. Inverting the class of maps that induce a weak equivalence of underlying spaces, X ; the homotopy type of the underlying space X , together with the homotopy coherent G-action. Can extract homotopy fixed points and hG orbits X , XhG from this. Equivariant homotopy theory Let G be a finite group and let X be a topological space with G-action (e.g. G = C2 and X = U(n) with the complex conjugation action). What is the \homotopy type" of X ? Jay Shah (MIT) Parametrized higher category theory May 1, 2017 2 / 32 Inverting the class of maps that induce a weak equivalence of underlying spaces, X ; the homotopy type of the underlying space X , together with the homotopy coherent G-action. Can extract homotopy fixed points and hG orbits X , XhG from this. Equivariant homotopy theory Let G be a finite group and let X be a topological space with G-action (e.g. G = C2 and X = U(n) with the complex conjugation action). What is the \homotopy type" of X ? Answer: depends on the class of weak equivalences one inverts in the larger category of G-spaces. Jay Shah (MIT) Parametrized higher category theory May 1, 2017 2 / 32 Equivariant homotopy theory Let G be a finite group and let X be a topological space with G-action (e.g. -
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. -
Yoneda's Lemma for Internal Higher Categories
YONEDA'S LEMMA FOR INTERNAL HIGHER CATEGORIES LOUIS MARTINI Abstract. We develop some basic concepts in the theory of higher categories internal to an arbitrary 1- topos. We define internal left and right fibrations and prove a version of the Grothendieck construction and of Yoneda's lemma for internal categories. Contents 1. Introduction 2 Motivation 2 Main results 3 Related work 4 Acknowledgment 4 2. Preliminaries 4 2.1. General conventions and notation4 2.2. Set theoretical foundations5 2.3. 1-topoi 5 2.4. Universe enlargement 5 2.5. Factorisation systems 8 3. Categories in an 1-topos 10 3.1. Simplicial objects in an 1-topos 10 3.2. Categories in an 1-topos 12 3.3. Functoriality and base change 16 3.4. The (1; 2)-categorical structure of Cat(B) 18 3.5. Cat(S)-valued sheaves on an 1-topos 19 3.6. Objects and morphisms 21 3.7. The universe for groupoids 23 3.8. Fully faithful and essentially surjective functors 26 arXiv:2103.17141v2 [math.CT] 2 May 2021 3.9. Subcategories 31 4. Groupoidal fibrations and Yoneda's lemma 36 4.1. Left fibrations 36 4.2. Slice categories 38 4.3. Initial functors 42 4.4. Covariant equivalences 49 4.5. The Grothendieck construction 54 4.6. Yoneda's lemma 61 References 71 Date: May 4, 2021. 1 2 LOUIS MARTINI 1. Introduction Motivation. In various areas of geometry, one of the principal strategies is to study geometric objects by means of algebraic invariants such as cohomology, K-theory and (stable or unstable) homotopy groups. -
Categorical Databases
Categorical databases David I. Spivak [email protected] Mathematics Department Massachusetts Institute of Technology Presented on 2014/02/28 at Oracle David I. Spivak (MIT) Categorical databases Presented on 2014/02/28 1 / 1 Introduction Purpose of the talk Purpose of the talk There is an fundamental connection between databases and categories. Category theory can simplify how we think about and use databases. We can clearly see all the working parts and how they fit together. Powerful theorems can be brought to bear on classical DB problems. David I. Spivak (MIT) Categorical databases Presented on 2014/02/28 2 / 1 Introduction The pros and cons of relational databases The pros and cons of relational databases Relational databases are reliable, scalable, and popular. They are provably reliable to the extent that they strictly adhere to the underlying mathematics. Make a distinction between the system you know and love, vs. the relational model, as a mathematical foundation for this system. David I. Spivak (MIT) Categorical databases Presented on 2014/02/28 3 / 1 Introduction The pros and cons of relational databases You're not really using the relational model. Current implementations have departed from the strict relational formalism: Tables may not be relational (duplicates, e.g from a query). Nulls (and labeled nulls) are commonly used. The theory of relations (150 years old) is not adequate to mathematically describe modern DBMS. The relational model does not offer guidance for schema mappings and data migration. Databases have been intuitively moving toward what's best described with a more modern mathematical foundation. David I.