On Logical Extension of Algebraic Division
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Division by Zero Calculus and Singular Integrals
Proceedings of International Conference on Mechanical, Electrical and Medical Intelligent System 2017 Division by Zero Calculus and Singular Integrals Tsutomu Matsuura1, a and Saburou Saitoh2,b 1Faculty of Science and Technology, Gunma University, 1-5-1 Tenjin-cho, Kiryu 376-8515, Japan 2Institute of Reproducing Kernels, Kawauchi-cho, 5-1648-16, Kiryu 376-0041, Japan [email protected], [email protected] Keywords: division by zero, point at infinity, singularity, Laurent expansion, Hadamard finite part Abstract. In this paper, we will introduce the formulas log 0= log ∞= 0 (not as limiting values) in the meaning of the one point compactification of Aleksandrov and their fundamental applications, and we will also examine the relationship between Laurent expansion and the division by zero. Based on those examinations we give the interpretation for the Hadamard finite part of singular integrals by means of the division by zero calculus. In particular, we will know that the division by zero is our elementary and fundamental mathematics. 1. Introduction By a natural extension of the fractions b (1) a for any complex numbers a and b , we found the simple and beautiful result, for any complex number b b = 0, (2) 0 incidentally in [1] by the Tikhonov regularization for the Hadamard product inversions for matrices and we discussed their properties and gave several physical interpretations on the general fractions in [2] for the case of real numbers. The result is very special case for general fractional functions in [3]. The division by zero has a long and mysterious story over the world (see, for example, H. -
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. -
Nios II Custom Instruction User Guide
Nios II Custom Instruction User Guide Subscribe UG-20286 | 2020.04.27 Send Feedback Latest document on the web: PDF | HTML Contents Contents 1. Nios II Custom Instruction Overview..............................................................................4 1.1. Custom Instruction Implementation......................................................................... 4 1.1.1. Custom Instruction Hardware Implementation............................................... 5 1.1.2. Custom Instruction Software Implementation................................................ 6 2. Custom Instruction Hardware Interface......................................................................... 7 2.1. Custom Instruction Types....................................................................................... 7 2.1.1. Combinational Custom Instructions.............................................................. 8 2.1.2. Multicycle Custom Instructions...................................................................10 2.1.3. Extended Custom Instructions................................................................... 11 2.1.4. Internal Register File Custom Instructions................................................... 13 2.1.5. External Interface Custom Instructions....................................................... 15 3. Custom Instruction Software Interface.........................................................................16 3.1. Custom Instruction Software Examples................................................................... 16 -
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. -
The MPFR Team [email protected] This Manual Documents How to Install and Use the Multiple Precision Floating-Point Reliable Library, Version 2.2.0
MPFR The Multiple Precision Floating-Point Reliable Library Edition 2.2.0 September 2005 The MPFR team [email protected] This manual documents how to install and use the Multiple Precision Floating-Point Reliable Library, version 2.2.0. Copyright 1991, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005 Free Software Foundation, Inc. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.1 or any later version published by the Free Software Foundation; with no Invariant Sections, with the Front-Cover Texts being “A GNU Manual”, and with the Back-Cover Texts being “You have freedom to copy and modify this GNU Manual, like GNU software”. A copy of the license is included in Appendix A [GNU Free Documentation License], page 30. i Table of Contents MPFR Copying Conditions ................................ 1 1 Introduction to MPFR ................................. 2 1.1 How to use this Manual ........................................................ 2 2 Installing MPFR ....................................... 3 2.1 How to install ................................................................. 3 2.2 Other make targets ............................................................ 3 2.3 Known Build Problems ........................................................ 3 2.4 Getting the Latest Version of MPFR ............................................ 4 3 Reporting Bugs ........................................ 5 4 MPFR Basics ......................................... -
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 . -
IEEE Std 754-1985 Revision of Reaffirmed1990 IEEE Standard for Binary Floating-Point Arithmetic
Recognized as an American National Standard (ANSI) IEEE Std 754-1985 An American National Standard IEEE Standard for Binary Floating-Point Arithmetic Sponsor Standards Committee of the IEEE Computer Society Approved March 21, 1985 Reaffirmed December 6, 1990 IEEE Standards Board Approved July 26, 1985 Reaffirmed May 21, 1991 American National Standards Institute © Copyright 1985 by The Institute of Electrical and Electronics Engineers, Inc 345 East 47th Street, New York, NY 10017, USA No part of this publication may be reproduced in any form, in an electronic retrieval system or otherwise, without the prior written permission of the publisher. IEEE Standards documents are developed within the Technical Committees of the IEEE Societies and the Standards Coordinating Committees of the IEEE Standards Board. Members of the committees serve voluntarily and without compensation. They are not necessarily members of the Institute. The standards developed within IEEE represent a consensus of the broad expertise on the subject within the Institute as well as those activities outside of IEEE which have expressed an interest in participating in the development of the standard. Use of an IEEE Standard is wholly voluntary. The existence of an IEEE Standard does not imply that there are no other ways to produce, test, measure, purchase, market, or provide other goods and services related to the scope of the IEEE Standard. Furthermore, the viewpoint expressed at the time a standard is approved and issued is subject to change brought about through developments in the state of the art and comments received from users of the standard. Every IEEE Standard is subjected to review at least once every five years for revision or reaffirmation. -
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. -
IEEE Standard 754 for Binary Floating-Point Arithmetic
Work in Progress: Lecture Notes on the Status of IEEE 754 October 1, 1997 3:36 am Lecture Notes on the Status of IEEE Standard 754 for Binary Floating-Point Arithmetic Prof. W. Kahan Elect. Eng. & Computer Science University of California Berkeley CA 94720-1776 Introduction: Twenty years ago anarchy threatened floating-point arithmetic. Over a dozen commercially significant arithmetics boasted diverse wordsizes, precisions, rounding procedures and over/underflow behaviors, and more were in the works. “Portable” software intended to reconcile that numerical diversity had become unbearably costly to develop. Thirteen years ago, when IEEE 754 became official, major microprocessor manufacturers had already adopted it despite the challenge it posed to implementors. With unprecedented altruism, hardware designers had risen to its challenge in the belief that they would ease and encourage a vast burgeoning of numerical software. They did succeed to a considerable extent. Anyway, rounding anomalies that preoccupied all of us in the 1970s afflict only CRAY X-MPs — J90s now. Now atrophy threatens features of IEEE 754 caught in a vicious circle: Those features lack support in programming languages and compilers, so those features are mishandled and/or practically unusable, so those features are little known and less in demand, and so those features lack support in programming languages and compilers. To help break that circle, those features are discussed in these notes under the following headings: Representable Numbers, Normal and Subnormal, Infinite -
Classical Logic and the Division by Zero Ilija Barukčić#1 #Internist Horandstrasse, DE-26441 Jever, Germany
International Journal of Mathematics Trends and Technology (IJMTT) – Volume 65 Issue 8 – August 2019 Classical logic and the division by zero Ilija Barukčić#1 #Internist Horandstrasse, DE-26441 Jever, Germany Abstract — The division by zero turned out to be a long lasting and not ending puzzle in mathematics and physics. An end of this long discussion appears not to be in sight. In particular zero divided by zero is treated as indeterminate thus that a result cannot be found out. It is the purpose of this publication to solve the problem of the division of zero by zero while relying on the general validity of classical logic. A systematic re-analysis of classical logic and the division of zero by zero has been undertaken. The theorems of this publication are grounded on classical logic and Boolean algebra. There is some evidence that the problem of zero divided by zero can be solved by today’s mathematical tools. According to classical logic, zero divided by zero is equal to one. Keywords — Indeterminate forms, Classical logic, Zero divided by zero, Infinity I. INTRODUCTION Aristotle‘s unparalleled influence on the development of scientific knowledge in western world is documented especially by his contributions to classical logic too. Besides of some serious limitations of Aristotle‘s logic, Aristotle‘s logic became dominant and is still an adequate basis of our understanding of science to some extent, since centuries. In point of fact, some authors are of the opinion that Aristotle himself has discovered everything there was to know about classical logic. After all, classical logic as such is at least closely related to the study of objective reality and deals with absolutely certain inferences and truths.