Domain Theory and Differential Calculus (Functions of One Variable)
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Categories and Types for Axiomatic Domain Theory Adam Eppendahl
Department of Computer Science Research Report No. RR-03-05 ISSN 1470-5559 December 2003 Categories and Types for Axiomatic Domain Theory Adam Eppendahl 1 Categories and Types for Axiomatic Domain Theory Adam Eppendahl Submitted for the degree of Doctor of Philosophy University of London 2003 2 3 Categories and Types for Axiomatic Domain Theory Adam Eppendahl Abstract Domain Theory provides a denotational semantics for programming languages and calculi con- taining fixed point combinators and other so-called paradoxical combinators. This dissertation presents results in the category theory and type theory of Axiomatic Domain Theory. Prompted by the adjunctions of Domain Theory, we extend Benton’s linear/nonlinear dual- sequent calculus to include recursive linear types and define a class of models by adding Freyd’s notion of algebraic compactness to the monoidal adjunctions that model Benton’s calculus. We observe that algebraic compactness is better behaved in the context of categories with structural actions than in the usual context of enriched categories. We establish a theory of structural algebraic compactness that allows us to describe our models without reference to en- richment. We develop a 2-categorical perspective on structural actions, including a presentation of monoidal categories that leads directly to Kelly’s reduced coherence conditions. We observe that Benton’s adjoint type constructors can be treated individually, semantically as well as syntactically, using free representations of distributors. We type various of fixed point combinators using recursive types and function types, which we consider the core types of such calculi, together with the adjoint types. We use the idioms of these typings, which include oblique function spaces, to give a translation of the core of Levy’s Call-By-Push-Value. -
Notes on Calculus II Integral Calculus Miguel A. Lerma
Notes on Calculus II Integral Calculus Miguel A. Lerma November 22, 2002 Contents Introduction 5 Chapter 1. Integrals 6 1.1. Areas and Distances. The Definite Integral 6 1.2. The Evaluation Theorem 11 1.3. The Fundamental Theorem of Calculus 14 1.4. The Substitution Rule 16 1.5. Integration by Parts 21 1.6. Trigonometric Integrals and Trigonometric Substitutions 26 1.7. Partial Fractions 32 1.8. Integration using Tables and CAS 39 1.9. Numerical Integration 41 1.10. Improper Integrals 46 Chapter 2. Applications of Integration 50 2.1. More about Areas 50 2.2. Volumes 52 2.3. Arc Length, Parametric Curves 57 2.4. Average Value of a Function (Mean Value Theorem) 61 2.5. Applications to Physics and Engineering 63 2.6. Probability 69 Chapter 3. Differential Equations 74 3.1. Differential Equations and Separable Equations 74 3.2. Directional Fields and Euler’s Method 78 3.3. Exponential Growth and Decay 80 Chapter 4. Infinite Sequences and Series 83 4.1. Sequences 83 4.2. Series 88 4.3. The Integral and Comparison Tests 92 4.4. Other Convergence Tests 96 4.5. Power Series 98 4.6. Representation of Functions as Power Series 100 4.7. Taylor and MacLaurin Series 103 3 CONTENTS 4 4.8. Applications of Taylor Polynomials 109 Appendix A. Hyperbolic Functions 113 A.1. Hyperbolic Functions 113 Appendix B. Various Formulas 118 B.1. Summation Formulas 118 Appendix C. Table of Integrals 119 Introduction These notes are intended to be a summary of the main ideas in course MATH 214-2: Integral Calculus. -
CCC 2017 Continuity, Computability, Constructivity - from Logic to Algorithms
CCC 2017 Continuity, Computability, Constructivity - From Logic to Algorithms Inria { LORIA, Nancy 26-30 June 2017 Abstracts On the commutativity of the powerspace monads .......... 3 Matthew de Brecht Hybrid Semantics for Higher-Order Store ............... 4 Bernhard Reus Point-free Descriptive Set Theory and Algorithmic Randomness5 Alex Simpson Sequentially locally convex QCB-spaces and Complexity Theory6 Matthias Schr¨oder Concurrent program extraction ..................... 7 Ulrich Berger and Hideki Tsuiki ERA: Applications, Analysis and Improvements .......... 9 Franz Brauße, Margarita Korovina and Norbert Th. Mller σ-locales and Booleanization in Formal Topology .......... 11 Francesco Ciraulo Rigorous Function Calculi ......................... 13 Pieter Collins Ramsey actions and Gelfand duality .................. 15 Willem Fouch´e Geometric Lorenz attractors are computable ............. 17 Daniel Gra¸ca,Cristobal Rojas and Ning Zhong A Variant of EQU in which Open and Closed Subspaces are Complementary without Excluded Middle ............ 19 Reinhold Heckmann Duality of upper and lower powerlocales on locally compact locales 22 Tatsuji Kawai 1 Average case complexity for Hamiltonian dynamical systems .. 23 Akitoshi Kawamura, Holger Thies and Martin Ziegler The Perfect Tree Theorem and Open Determinacy ......... 26 Takayuki Kihara and Arno Pauly Towards Certified Algorithms for Exact Real Arithmetic ..... 28 Sunyoung Kim, Sewon Park, Gyesik Lee and Martin Ziegler Decidability in Symbolic-Heap System with Arithmetic and Ar- rays -
A Brief Tour of Vector Calculus
A BRIEF TOUR OF VECTOR CALCULUS A. HAVENS Contents 0 Prelude ii 1 Directional Derivatives, the Gradient and the Del Operator 1 1.1 Conceptual Review: Directional Derivatives and the Gradient........... 1 1.2 The Gradient as a Vector Field............................ 5 1.3 The Gradient Flow and Critical Points ....................... 10 1.4 The Del Operator and the Gradient in Other Coordinates*............ 17 1.5 Problems........................................ 21 2 Vector Fields in Low Dimensions 26 2 3 2.1 General Vector Fields in Domains of R and R . 26 2.2 Flows and Integral Curves .............................. 31 2.3 Conservative Vector Fields and Potentials...................... 32 2.4 Vector Fields from Frames*.............................. 37 2.5 Divergence, Curl, Jacobians, and the Laplacian................... 41 2.6 Parametrized Surfaces and Coordinate Vector Fields*............... 48 2.7 Tangent Vectors, Normal Vectors, and Orientations*................ 52 2.8 Problems........................................ 58 3 Line Integrals 66 3.1 Defining Scalar Line Integrals............................. 66 3.2 Line Integrals in Vector Fields ............................ 75 3.3 Work in a Force Field................................. 78 3.4 The Fundamental Theorem of Line Integrals .................... 79 3.5 Motion in Conservative Force Fields Conserves Energy .............. 81 3.6 Path Independence and Corollaries of the Fundamental Theorem......... 82 3.7 Green's Theorem.................................... 84 3.8 Problems........................................ 89 4 Surface Integrals, Flux, and Fundamental Theorems 93 4.1 Surface Integrals of Scalar Fields........................... 93 4.2 Flux........................................... 96 4.3 The Gradient, Divergence, and Curl Operators Via Limits* . 103 4.4 The Stokes-Kelvin Theorem..............................108 4.5 The Divergence Theorem ...............................112 4.6 Problems........................................114 List of Figures 117 i 11/14/19 Multivariate Calculus: Vector Calculus Havens 0. -
Differentiation Rules (Differential Calculus)
Differentiation Rules (Differential Calculus) 1. Notation The derivative of a function f with respect to one independent variable (usually x or t) is a function that will be denoted by D f . Note that f (x) and (D f )(x) are the values of these functions at x. 2. Alternate Notations for (D f )(x) d d f (x) d f 0 (1) For functions f in one variable, x, alternate notations are: Dx f (x), dx f (x), dx , dx (x), f (x), f (x). The “(x)” part might be dropped although technically this changes the meaning: f is the name of a function, dy 0 whereas f (x) is the value of it at x. If y = f (x), then Dxy, dx , y , etc. can be used. If the variable t represents time then Dt f can be written f˙. The differential, “d f ”, and the change in f ,“D f ”, are related to the derivative but have special meanings and are never used to indicate ordinary differentiation. dy 0 Historical note: Newton used y,˙ while Leibniz used dx . About a century later Lagrange introduced y and Arbogast introduced the operator notation D. 3. Domains The domain of D f is always a subset of the domain of f . The conventional domain of f , if f (x) is given by an algebraic expression, is all values of x for which the expression is defined and results in a real number. If f has the conventional domain, then D f usually, but not always, has conventional domain. Exceptions are noted below. -
Topology, Domain Theory and Theoretical Computer Science
Draft URL httpwwwmathtulaneedumwmtopandcspsgz pages Top ology Domain Theory and Theoretical Computer Science Michael W Mislove Department of Mathematics Tulane University New Orleans LA Abstract In this pap er we survey the use of ordertheoretic top ology in theoretical computer science with an emphasis on applications of domain theory Our fo cus is on the uses of ordertheoretic top ology in programming language semantics and on problems of p otential interest to top ologists that stem from concerns that semantics generates Keywords Domain theory Scott top ology p ower domains untyp ed lamb da calculus Subject Classication BFBNQ Intro duction Top ology has proved to b e an essential to ol for certain asp ects of theoretical computer science Conversely the problems that arise in the computational setting have provided new and interesting stimuli for top ology These prob lems also have increased the interaction b etween top ology and related areas of mathematics such as order theory and top ological algebra In this pap er we outline some of these interactions b etween top ology and theoretical computer science fo cusing on those asp ects that have b een most useful to one particular area of theoretical computation denotational semantics This pap er b egan with the goal of highlighting how the interaction of order and top ology plays a fundamental role in programming semantics and related areas It also started with the viewp oint that there are many purely top o logical notions that are useful in theoretical computer science that -
Visual Differential Calculus
Proceedings of 2014 Zone 1 Conference of the American Society for Engineering Education (ASEE Zone 1) Visual Differential Calculus Andrew Grossfield, Ph.D., P.E., Life Member, ASEE, IEEE Abstract— This expository paper is intended to provide = (y2 – y1) / (x2 – x1) = = m = tan(α) Equation 1 engineering and technology students with a purely visual and intuitive approach to differential calculus. The plan is that where α is the angle of inclination of the line with the students who see intuitively the benefits of the strategies of horizontal. Since the direction of a straight line is constant at calculus will be encouraged to master the algebraic form changing techniques such as solving, factoring and completing every point, so too will be the angle of inclination, the slope, the square. Differential calculus will be treated as a continuation m, of the line and the difference quotient between any pair of of the study of branches11 of continuous and smooth curves points. In the case of a straight line vertical changes, Δy, are described by equations which was initiated in a pre-calculus or always the same multiple, m, of the corresponding horizontal advanced algebra course. Functions are defined as the single changes, Δx, whether or not the changes are small. valued expressions which describe the branches of the curves. However for curves which are not straight lines, the Derivatives are secondary functions derived from the just mentioned functions in order to obtain the slopes of the lines situation is not as simple. Select two pairs of points at random tangent to the curves. -
Extra Examples for “Scott Continuity in Generalized Probabilistic Theories”
Extra Examples for “Scott Continuity in Generalized Probabilistic Theories” Robert Furber May 27, 2020 1 Introduction The purpose of this note is to draw out some counterexamples using the con- struction described in [9]. In Section 3 we show that there is an order-unit space A such that there exist 2@0 pairwise non-isometric base-norm spaces E such that A =∼ E∗ as order-unit spaces. This contrasts with the case of C∗-algebras, where if there is a predual (and therefore the C∗-algebra is a W∗-algebra), it is unique. In Section 4 we show that there is a base-norm space E, whose dual order- unit space A = E∗ is therefore bounded directed-complete, such that there is a Scott-continuous unital map f : A ! A that is not the adjoint of any linear map g : E ! E. Again, this contrasts with the situation for W∗-algebras, where Scott-continuous maps A ! B correspond to adjoints of linear maps between preduals B∗ ! A∗. In Section 5 we show that there are base-norm and order- unit spaces E such that E =∼ E∗∗, but the evaluation mapping E ! E∗∗ is not an isomorphism, i.e. E is not reflexive, using an example of R. C. James from Banach space theory. In Section 6 we use an example due to J. W. Roberts to show that there are base-norm spaces E admitting Hausdorff vectorial topologies in which the base and unit ball are compact, but are not dual spaces, and similarly that there are order-unit spaces A admitting Hausdorff vectorial topologies in which the unit interval and unit ball are compact, but are not dual spaces. -
Scott Domain Representability of a Class of Generalized Ordered Spaces
Scott Domain Representability of a Class of Generalized Ordered Spaces Kevin W. Duke∗ and David Lutzer† Draft of July 25, 2007 Abstract Many important topological examples (the Sorgenfrey line, the Michael line) belong to the class of GO-spaces constructed on the usual set R of real numbers. In this paper we show that every GO- space constructed on the real line, and more generally, any GO-space constructed on a locally compact LOTS, is Scott-domain representable, i.e., is homeomorphic to the space of maximal elements of some Scott domain with the Scott topology. MR Classifications: primary = 54F05; secondary = 54D35,54D45,54D80, 06F30 Key words and phrases: Scott domain, Scott topology, dcpo, domain-representable space, generalized ordered space, GO-space. 1 Introduction A topological space X is a Baire space if every intersection of countably many dense, open sets is dense. The Baire space property does not behave well under topological operations, and in the 1960s several authors (Choquet, deGroot, and Oxtoby, for example) described topological properties now called Choquet completeness, subcompactness, and pseudo-completeness that are stronger than the Baire space property and are well-behaved under the product operation. Such properties were studied in [1] and have come to be thought of as being strong completeness properties. More recently, topologists have borrowed a property called domain representability from theoret- ical computer science and have come to be see it as a kind of completeness property related to the Baire property. A space X is domain representable if X is homeomorphic to the space of maximal elements of some domain, topologized with the relative Scott topology. -
Domain Theory: an Introduction
Domain Theory: An Introduction Robert Cartwright Rebecca Parsons Rice University This monograph is an unauthorized revision of “Lectures On A Mathematical Theory of Computation” by Dana Scott [3]. Scott’s monograph uses a formulation of domains called neighborhood systems in which finite elements are selected subsets of a master set of objects called “tokens”. Since tokens have little intuitive significance, Scott has discarded neighborhood systems in favor of an equivalent formulation of domains called information systems [4]. Unfortunately, he has not rewritten his monograph to reflect this change. We have rewritten Scott’s monograph in terms of finitary bases (see Cartwright [2]) instead of information systems. A finitary basis is an information system that is closed under least upper bounds on finite consistent subsets. This convention ensures that every finite answer is represented by a single basis object instead of a set of objects. 1 The Rudiments of Domain Theory Motivation Programs perform computations by repeatedly applying primitive operations to data values. The set of primitive operations and data values depends on the particular programming language. Nearly all languages support a rich collection of data values including atomic objects, such as booleans, integers, characters, and floating point numbers, and composite objects, such as arrays, records, sequences, tuples, and infinite streams. More advanced languages also support functions and procedures as data values. To define the meaning of programs in a given language, we must first define the building blocks—the primitive data values and operations—from which computations in the language are constructed. Domain theory is a comprehensive mathematical framework for defining the data values and primitive operations of a programming language. -
MATH 162: Calculus II Differentiation
MATH 162: Calculus II Framework for Mon., Jan. 29 Review of Differentiation and Integration Differentiation Definition of derivative f 0(x): f(x + h) − f(x) f(y) − f(x) lim or lim . h→0 h y→x y − x Differentiation rules: 1. Sum/Difference rule: If f, g are differentiable at x0, then 0 0 0 (f ± g) (x0) = f (x0) ± g (x0). 2. Product rule: If f, g are differentiable at x0, then 0 0 0 (fg) (x0) = f (x0)g(x0) + f(x0)g (x0). 3. Quotient rule: If f, g are differentiable at x0, and g(x0) 6= 0, then 0 0 0 f f (x0)g(x0) − f(x0)g (x0) (x0) = 2 . g [g(x0)] 4. Chain rule: If g is differentiable at x0, and f is differentiable at g(x0), then 0 0 0 (f ◦ g) (x0) = f (g(x0))g (x0). This rule may also be expressed as dy dy du = . dx x=x0 du u=u(x0) dx x=x0 Implicit differentiation is a consequence of the chain rule. For instance, if y is really dependent upon x (i.e., y = y(x)), and if u = y3, then d du du dy d (y3) = = = (y3)y0(x) = 3y2y0. dx dx dy dx dy Practice: Find d x d √ d , (x2 y), and [y cos(xy)]. dx y dx dx MATH 162—Framework for Mon., Jan. 29 Review of Differentiation and Integration Integration The definite integral • the area problem • Riemann sums • definition Fundamental Theorem of Calculus: R x I: Suppose f is continuous on [a, b]. -
Unit – 1 Differential Calculus
Differential Calculus UNIT 1 DIFFERENTIAL CALCULUS Structure 1.0 Introduction 1.1 Objectives 1.2 Limits and Continuity 1.3 Derivative of a Function 1.4 The Chain Rule 1.5 Differentiation of Parametric Forms 1.6 Answers to Check Your Progress 1.7 Summary 1.0 INTRODUCTION In this Unit, we shall define the concept of limit, continuity and differentiability. 1.1 OBJECTIVES After studying this unit, you should be able to : define limit of a function; define continuity of a function; and define derivative of a function. 1.2 LIMITS AND CONTINUITY We start by defining a function. Let A and B be two non empty sets. A function f from the set A to the set B is a rule that assigns to each element x of A a unique element y of B. We call y the image of x under f and denote it by f(x). The domain of f is the set A, and the co−domain of f is the set B. The range of f consists of all images of elements in A. We shall only work with functions whose domains and co–domains are subsets of real numbers. Given functions f and g, their sum f + g, difference f – g, product f. g and quotient f / g are defined by (f + g ) (x) = f(x) + g(x) (f – g ) (x) = f(x) – g(x) (f . g ) (x) = f(x) g(x) f f() x and (x ) g g() x 5 Calculus For the functions f + g, f– g, f. g, the domain is defined to be intersections of the domains of f and g, and for f / g the domain is the intersection excluding the points where g(x) = 0.