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Avoiding the Undefined by Underspecification
Avoiding the Undefined by Underspecification David Gries* and Fred B. Schneider** Computer Science Department, Cornell University Ithaca, New York 14853 USA Abstract. We use the appeal of simplicity and an aversion to com- plexity in selecting a method for handling partial functions in logic. We conclude that avoiding the undefined by using underspecification is the preferred choice. 1 Introduction Everything should be made as simple as possible, but not any simpler. --Albert Einstein The first volume of Springer-Verlag's Lecture Notes in Computer Science ap- peared in 1973, almost 22 years ago. The field has learned a lot about computing since then, and in doing so it has leaned on and supported advances in other fields. In this paper, we discuss an aspect of one of these fields that has been explored by computer scientists, handling undefined terms in formal logic. Logicians are concerned mainly with studying logic --not using it. Some computer scientists have discovered, by necessity, that logic is actually a useful tool. Their attempts to use logic have led to new insights --new axiomatizations, new proof formats, new meta-logical notions like relative completeness, and new concerns. We, the authors, are computer scientists whose interests have forced us to become users of formal logic. We use it in our work on language definition and in the formal development of programs. This paper is born of that experience. The operation of a computer is nothing more than uninterpreted symbol ma- nipulation. Bits are moved and altered electronically, without regard for what they denote. It iswe who provide the interpretation, saying that one bit string represents money and another someone's name, or that one transformation rep- resents addition and another alphabetization. -
Informal Lecture Notes for Complex Analysis
Informal lecture notes for complex analysis Robert Neel I'll assume you're familiar with the review of complex numbers and their algebra as contained in Appendix G of Stewart's book, so we'll pick up where that leaves off. 1 Elementary complex functions In one-variable real calculus, we have a collection of basic functions, like poly- nomials, rational functions, the exponential and log functions, and the trig functions, which we understand well and which serve as the building blocks for more general functions. The same is true in one complex variable; in fact, the real functions we just listed can be extended to complex functions. 1.1 Polynomials and rational functions We start with polynomials and rational functions. We know how to multiply and add complex numbers, and thus we understand polynomial functions. To be specific, a degree n polynomial, for some non-negative integer n, is a function of the form n n−1 f(z) = cnz + cn−1z + ··· + c1z + c0; 3 where the ci are complex numbers with cn 6= 0. For example, f(z) = 2z + (1 − i)z + 2i is a degree three (complex) polynomial. Polynomials are clearly defined on all of C. A rational function is the quotient of two polynomials, and it is defined everywhere where the denominator is non-zero. z2+1 Example: The function f(z) = z2−1 is a rational function. The denomina- tor will be zero precisely when z2 = 1. We know that every non-zero complex number has n distinct nth roots, and thus there will be two points at which the denominator is zero. -
MATH 305 Complex Analysis, Spring 2016 Using Residues to Evaluate Improper Integrals Worksheet for Sections 78 and 79
MATH 305 Complex Analysis, Spring 2016 Using Residues to Evaluate Improper Integrals Worksheet for Sections 78 and 79 One of the interesting applications of Cauchy's Residue Theorem is to find exact values of real improper integrals. The idea is to integrate a complex rational function around a closed contour C that can be arbitrarily large. As the size of the contour becomes infinite, the piece in the complex plane (typically an arc of a circle) contributes 0 to the integral, while the part remaining covers the entire real axis (e.g., an improper integral from −∞ to 1). An Example Let us use residues to derive the formula p Z 1 x2 2 π 4 dx = : (1) 0 x + 1 4 Note the somewhat surprising appearance of π for the value of this integral. z2 First, let f(z) = and let C = L + C be the contour that consists of the line segment L z4 + 1 R R R on the real axis from −R to R, followed by the semi-circle CR of radius R traversed CCW (see figure below). Note that C is a positively oriented, simple, closed contour. We will assume that R > 1. Next, notice that f(z) has two singular points (simple poles) inside C. Call them z0 and z1, as shown in the figure. By Cauchy's Residue Theorem. we have I f(z) dz = 2πi Res f(z) + Res f(z) C z=z0 z=z1 On the other hand, we can parametrize the line segment LR by z = x; −R ≤ x ≤ R, so that I Z R x2 Z z2 f(z) dz = 4 dx + 4 dz; C −R x + 1 CR z + 1 since C = LR + CR. -
A Quick Algebra Review
A Quick Algebra Review 1. Simplifying Expressions 2. Solving Equations 3. Problem Solving 4. Inequalities 5. Absolute Values 6. Linear Equations 7. Systems of Equations 8. Laws of Exponents 9. Quadratics 10. Rationals 11. Radicals Simplifying Expressions An expression is a mathematical “phrase.” Expressions contain numbers and variables, but not an equal sign. An equation has an “equal” sign. For example: Expression: Equation: 5 + 3 5 + 3 = 8 x + 3 x + 3 = 8 (x + 4)(x – 2) (x + 4)(x – 2) = 10 x² + 5x + 6 x² + 5x + 6 = 0 x – 8 x – 8 > 3 When we simplify an expression, we work until there are as few terms as possible. This process makes the expression easier to use, (that’s why it’s called “simplify”). The first thing we want to do when simplifying an expression is to combine like terms. For example: There are many terms to look at! Let’s start with x². There Simplify: are no other terms with x² in them, so we move on. 10x x² + 10x – 6 – 5x + 4 and 5x are like terms, so we add their coefficients = x² + 5x – 6 + 4 together. 10 + (-5) = 5, so we write 5x. -6 and 4 are also = x² + 5x – 2 like terms, so we can combine them to get -2. Isn’t the simplified expression much nicer? Now you try: x² + 5x + 3x² + x³ - 5 + 3 [You should get x³ + 4x² + 5x – 2] Order of Operations PEMDAS – Please Excuse My Dear Aunt Sally, remember that from Algebra class? It tells the order in which we can complete operations when solving an equation. -
Arxiv:1207.1472V2 [Math.CV]
SOME SIMPLIFICATIONS IN THE PRESENTATIONS OF COMPLEX POWER SERIES AND UNORDERED SUMS OSWALDO RIO BRANCO DE OLIVEIRA Abstract. This text provides very easy and short proofs of some basic prop- erties of complex power series (addition, subtraction, multiplication, division, rearrangement, composition, differentiation, uniqueness, Taylor’s series, Prin- ciple of Identity, Principle of Isolated Zeros, and Binomial Series). This is done by simplifying the usual presentation of unordered sums of a (countable) family of complex numbers. All the proofs avoid formal power series, double series, iterated series, partial series, asymptotic arguments, complex integra- tion theory, and uniform continuity. The use of function continuity as well as epsilons and deltas is kept to a mininum. Mathematics Subject Classification: 30B10, 40B05, 40C15, 40-01, 97I30, 97I80 Key words and phrases: Power Series, Multiple Sequences, Series, Summability, Complex Analysis, Functions of a Complex Variable. Contents 1. Introduction 1 2. Preliminaries 2 3. Absolutely Convergent Series and Commutativity 3 4. Unordered Countable Sums and Commutativity 5 5. Unordered Countable Sums and Associativity. 9 6. Sum of a Double Sequence and The Cauchy Product 10 7. Power Series - Algebraic Properties 11 8. Power Series - Analytic Properties 14 References 17 arXiv:1207.1472v2 [math.CV] 27 Jul 2012 1. Introduction The objective of this work is to provide a simplification of the theory of un- ordered sums of a family of complex numbers (in particular, for a countable family of complex numbers) as well as very easy proofs of basic operations and properties concerning complex power series, such as addition, scalar multiplication, multipli- cation, division, rearrangement, composition, differentiation (see Apostol [2] and Vyborny [21]), Taylor’s formula, principle of isolated zeros, uniqueness, principle of identity, and binomial series. -
HOMOTECIA Nº 6-15 Junio 2017
HOMOTECIA Nº 6 – Año 15 Martes, 1º de Junio de 2017 1 Entre las expectativas futuras que se tienen sobre un docente en formación, está el considerar como indicativo de que logrará realizarse como tal, cuando evidencia confianza en lo que hace, cuando cree en sí mismo y no deja que su tiempo transcurra sin pro pósitos y sin significado. Estos son los principios que deberán pautar el ejercicio de su magisterio si aspira tener éxito en su labor, lo cual mostrará mediante su afán por dar lo bueno dentro de sí, por hacer lo mejor posible, por comprometerse con el porvenir de quienes confiadamente pondrán en sus manos la misión de enseñarles. Pero la responsabilidad implícita en este proceso lo debería llevar a considerar seriamente algunos GIACINTO MORERA (1856 – 1907 ) aspectos. Obtener una acreditación para enseñar no es un pergamino para exhib ir con petulancia ante familiares y Nació el 18 de julio de 1856 en Novara, y murió el 8 de febrero de 1907, en Turín; amistades. En otras palabras, viviendo en el mundo educativo, es ambas localidades en Italia. asumir que se produjo un cambio significativo en la manera de Matemático que hizo contribuciones a la dinámica. participar en este: pasó de ser guiado para ahora guiar. No es que no necesite que se le orie nte como profesional de la docencia, esto es algo que sucederá obligatoriamente a nivel organizacional, Giacinto Morera , hijo de un acaudalado hombre de pero el hecho es que adquirirá una responsabilidad mucho mayor negocios, se graduó en ingeniería y matemáticas en la porque así como sus preceptores universitarios tuvieron el compromiso de formarlo y const ruirlo cultural y Universidad de Turín, Italia, habiendo asistido a los académicamente, él tendrá el mismo compromiso de hacerlo con cursos por Enrico D'Ovidio, Angelo Genocchi y sus discípulos, sea cual sea el nivel docente donde se desempeñe. -
Undefinedness and Soft Typing in Formal Mathematics
Undefinedness and Soft Typing in Formal Mathematics PhD Research Proposal by Jonas Betzendahl January 7, 2020 Supervisors: Prof. Dr. Michael Kohlhase Dr. Florian Rabe Abstract During my PhD, I want to contribute towards more natural reasoning in formal systems for mathematics (i.e. closer resembling that of actual mathematicians), with special focus on two topics on the precipice between type systems and logics in formal systems: undefinedness and soft types. Undefined terms are common in mathematics as practised on paper or blackboard by math- ematicians, yet few of the many systems for formalising mathematics that exist today deal with it in a principled way or allow explicit reasoning with undefined terms because allowing for this often results in undecidability. Soft types are a way of allowing the user of a formal system to also incorporate information about mathematical objects into the type system that had to be proven or computed after their creation. This approach is equally a closer match for mathematics as performed by mathematicians and has had promising results in the past. The MMT system constitutes a natural fit for this endeavour due to its rapid prototyping ca- pabilities and existing infrastructure. However, both of the aspects above necessitate a stronger support for automated reasoning than currently available. Hence, a further goal of mine is to extend the MMT framework with additional capabilities for automated and interactive theorem proving, both for reasoning in and outside the domains of undefinedness and soft types. 2 Contents 1 Introduction & Motivation 4 2 State of the Art 5 2.1 Undefinedness in Mathematics . -
Complex Analysis Class 24: Wednesday April 2
Complex Analysis Math 214 Spring 2014 Fowler 307 MWF 3:00pm - 3:55pm c 2014 Ron Buckmire http://faculty.oxy.edu/ron/math/312/14/ Class 24: Wednesday April 2 TITLE Classifying Singularities using Laurent Series CURRENT READING Zill & Shanahan, §6.2-6.3 HOMEWORK Zill & Shanahan, §6.2 3, 15, 20, 24 33*. §6.3 7, 8, 9, 10. SUMMARY We shall be introduced to Laurent Series and learn how to use them to classify different various kinds of singularities (locations where complex functions are no longer analytic). Classifying Singularities There are basically three types of singularities (points where f(z) is not analytic) in the complex plane. Isolated Singularity An isolated singularity of a function f(z) is a point z0 such that f(z) is analytic on the punctured disc 0 < |z − z0| <rbut is undefined at z = z0. We usually call isolated singularities poles. An example is z = i for the function z/(z − i). Removable Singularity A removable singularity is a point z0 where the function f(z0) appears to be undefined but if we assign f(z0) the value w0 with the knowledge that lim f(z)=w0 then we can say that we z→z0 have “removed” the singularity. An example would be the point z = 0 for f(z) = sin(z)/z. Branch Singularity A branch singularity is a point z0 through which all possible branch cuts of a multi-valued function can be drawn to produce a single-valued function. An example of such a point would be the point z = 0 for Log (z). -
Control Systems
ECE 380: Control Systems Course Notes: Winter 2014 Prof. Shreyas Sundaram Department of Electrical and Computer Engineering University of Waterloo ii c Shreyas Sundaram Acknowledgments Parts of these course notes are loosely based on lecture notes by Professors Daniel Liberzon, Sean Meyn, and Mark Spong (University of Illinois), on notes by Professors Daniel Davison and Daniel Miller (University of Waterloo), and on parts of the textbook Feedback Control of Dynamic Systems (5th edition) by Franklin, Powell and Emami-Naeini. I claim credit for all typos and mistakes in the notes. The LATEX template for The Not So Short Introduction to LATEX 2" by T. Oetiker et al. was used to typeset portions of these notes. Shreyas Sundaram University of Waterloo c Shreyas Sundaram iv c Shreyas Sundaram Contents 1 Introduction 1 1.1 Dynamical Systems . .1 1.2 What is Control Theory? . .2 1.3 Outline of the Course . .4 2 Review of Complex Numbers 5 3 Review of Laplace Transforms 9 3.1 The Laplace Transform . .9 3.2 The Inverse Laplace Transform . 13 3.2.1 Partial Fraction Expansion . 13 3.3 The Final Value Theorem . 15 4 Linear Time-Invariant Systems 17 4.1 Linearity, Time-Invariance and Causality . 17 4.2 Transfer Functions . 18 4.2.1 Obtaining the transfer function of a differential equation model . 20 4.3 Frequency Response . 21 5 Bode Plots 25 5.1 Rules for Drawing Bode Plots . 26 5.1.1 Bode Plot for Ko ....................... 27 5.1.2 Bode Plot for sq ....................... 28 s −1 s 5.1.3 Bode Plot for ( p + 1) and ( z + 1) . -
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. -
Real Proofs of Complex Theorems (And Vice Versa)
REAL PROOFS OF COMPLEX THEOREMS (AND VICE VERSA) LAWRENCE ZALCMAN Introduction. It has become fashionable recently to argue that real and complex variables should be taught together as a unified curriculum in analysis. Now this is hardly a novel idea, as a quick perusal of Whittaker and Watson's Course of Modern Analysis or either Littlewood's or Titchmarsh's Theory of Functions (not to mention any number of cours d'analyse of the nineteenth or twentieth century) will indicate. And, while some persuasive arguments can be advanced in favor of this approach, it is by no means obvious that the advantages outweigh the disadvantages or, for that matter, that a unified treatment offers any substantial benefit to the student. What is obvious is that the two subjects do interact, and interact substantially, often in a surprising fashion. These points of tangency present an instructor the opportunity to pose (and answer) natural and important questions on basic material by applying real analysis to complex function theory, and vice versa. This article is devoted to several such applications. My own experience in teaching suggests that the subject matter discussed below is particularly well-suited for presentation in a year-long first graduate course in complex analysis. While most of this material is (perhaps by definition) well known to the experts, it is not, unfortunately, a part of the common culture of professional mathematicians. In fact, several of the examples arose in response to questions from friends and colleagues. The mathematics involved is too pretty to be the private preserve of specialists. -
Complex Logarithm
Complex Analysis Math 214 Spring 2014 Fowler 307 MWF 3:00pm - 3:55pm c 2014 Ron Buckmire http://faculty.oxy.edu/ron/math/312/14/ Class 14: Monday February 24 TITLE The Complex Logarithm CURRENT READING Zill & Shanahan, Section 4.1 HOMEWORK Zill & Shanahan, §4.1.2 # 23,31,34,42 44*; SUMMARY We shall return to the murky world of branch cuts as we expand our repertoire of complex functions when we encounter the complex logarithm function. The Complex Logarithm log z Let us define w = log z as the inverse of z = ew. NOTE Your textbook (Zill & Shanahan) uses ln instead of log and Ln instead of Log . We know that exp[ln |z|+i(θ+2nπ)] = z, where n ∈ Z, from our knowledge of the exponential function. So we can define log z =ln|z| + i arg z =ln|z| + i Arg z +2nπi =lnr + iθ where r = |z| as usual, and θ is the argument of z If we only use the principal value of the argument, then we define the principal value of log z as Log z, where Log z =ln|z| + i Arg z = Log |z| + i Arg z Exercise Compute Log (−2) and log(−2), Log (2i), and log(2i), Log (−4) and log(−4) Logarithmic Identities z = elog z but log ez = z +2kπi (Is this a surprise?) log(z1z2) = log z1 + log z2 z1 log = log z1 − log z2 z2 However these do not neccessarily apply to the principal branch of the logarithm, written as Log z. (i.e. is Log (2) + Log (−2) = Log (−4)? 1 Complex Analysis Worksheet 14 Math 312 Spring 2014 Log z: the Principal Branch of log z Log z is a single-valued function and is analytic in the domain D∗ consisting of all points of the complex plane except for those lying on the nonpositive real axis, where d 1 Log z = dz z Sketch the set D∗ and convince yourself that it is an open connected set.