Variable-Arity Polymorphism
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Frequently Asked Questions in Mathematics
Frequently Asked Questions in Mathematics The Sci.Math FAQ Team. Editor: Alex L´opez-Ortiz e-mail: [email protected] Contents 1 Introduction 4 1.1 Why a list of Frequently Asked Questions? . 4 1.2 Frequently Asked Questions in Mathematics? . 4 2 Fundamentals 5 2.1 Algebraic structures . 5 2.1.1 Monoids and Groups . 6 2.1.2 Rings . 7 2.1.3 Fields . 7 2.1.4 Ordering . 8 2.2 What are numbers? . 9 2.2.1 Introduction . 9 2.2.2 Construction of the Number System . 9 2.2.3 Construction of N ............................... 10 2.2.4 Construction of Z ................................ 10 2.2.5 Construction of Q ............................... 11 2.2.6 Construction of R ............................... 11 2.2.7 Construction of C ............................... 12 2.2.8 Rounding things up . 12 2.2.9 What’s next? . 12 3 Number Theory 14 3.1 Fermat’s Last Theorem . 14 3.1.1 History of Fermat’s Last Theorem . 14 3.1.2 What is the current status of FLT? . 14 3.1.3 Related Conjectures . 15 3.1.4 Did Fermat prove this theorem? . 16 3.2 Prime Numbers . 17 3.2.1 Largest known Mersenne prime . 17 3.2.2 Largest known prime . 17 3.2.3 Largest known twin primes . 18 3.2.4 Largest Fermat number with known factorization . 18 3.2.5 Algorithms to factor integer numbers . 18 3.2.6 Primality Testing . 19 3.2.7 List of record numbers . 20 3.2.8 What is the current status on Mersenne primes? . -
Chapter 7 Expressions and Assignment Statements
Chapter 7 Expressions and Assignment Statements Chapter 7 Topics Introduction Arithmetic Expressions Overloaded Operators Type Conversions Relational and Boolean Expressions Short-Circuit Evaluation Assignment Statements Mixed-Mode Assignment Chapter 7 Expressions and Assignment Statements Introduction Expressions are the fundamental means of specifying computations in a programming language. To understand expression evaluation, need to be familiar with the orders of operator and operand evaluation. Essence of imperative languages is dominant role of assignment statements. Arithmetic Expressions Their evaluation was one of the motivations for the development of the first programming languages. Most of the characteristics of arithmetic expressions in programming languages were inherited from conventions that had evolved in math. Arithmetic expressions consist of operators, operands, parentheses, and function calls. The operators can be unary, or binary. C-based languages include a ternary operator, which has three operands (conditional expression). The purpose of an arithmetic expression is to specify an arithmetic computation. An implementation of such a computation must cause two actions: o Fetching the operands from memory o Executing the arithmetic operations on those operands. Design issues for arithmetic expressions: 1. What are the operator precedence rules? 2. What are the operator associativity rules? 3. What is the order of operand evaluation? 4. Are there restrictions on operand evaluation side effects? 5. Does the language allow user-defined operator overloading? 6. What mode mixing is allowed in expressions? Operator Evaluation Order 1. Precedence The operator precedence rules for expression evaluation define the order in which “adjacent” operators of different precedence levels are evaluated (“adjacent” means they are separated by at most one operand). -
Operators and Expressions
UNIT – 3 OPERATORS AND EXPRESSIONS Lesson Structure 3.0 Objectives 3.1 Introduction 3.2 Arithmetic Operators 3.3 Relational Operators 3.4 Logical Operators 3.5 Assignment Operators 3.6 Increment and Decrement Operators 3.7 Conditional Operator 3.8 Bitwise Operators 3.9 Special Operators 3.10 Arithmetic Expressions 3.11 Evaluation of Expressions 3.12 Precedence of Arithmetic Operators 3.13 Type Conversions in Expressions 3.14 Operator Precedence and Associability 3.15 Mathematical Functions 3.16 Summary 3.17 Questions 3.18 Suggested Readings 3.0 Objectives After going through this unit you will be able to: Define and use different types of operators in Java programming Understand how to evaluate expressions? Understand the operator precedence and type conversion And write mathematical functions. 3.1 Introduction Java supports a rich set of operators. We have already used several of them, such as =, +, –, and *. An operator is a symbol that tells the computer to perform certain mathematical or logical manipulations. Operators are used in programs to manipulate data and variables. They usually form a part of mathematical or logical expressions. Java operators can be classified into a number of related categories as below: 1. Arithmetic operators 2. Relational operators 1 3. Logical operators 4. Assignment operators 5. Increment and decrement operators 6. Conditional operators 7. Bitwise operators 8. Special operators 3.2 Arithmetic Operators Arithmetic operators are used to construct mathematical expressions as in algebra. Java provides all the basic arithmetic operators. They are listed in Tabled 3.1. The operators +, –, *, and / all works the same way as they do in other languages. -
ARM Instruction Set
4 ARM Instruction Set This chapter describes the ARM instruction set. 4.1 Instruction Set Summary 4-2 4.2 The Condition Field 4-5 4.3 Branch and Exchange (BX) 4-6 4.4 Branch and Branch with Link (B, BL) 4-8 4.5 Data Processing 4-10 4.6 PSR Transfer (MRS, MSR) 4-17 4.7 Multiply and Multiply-Accumulate (MUL, MLA) 4-22 4.8 Multiply Long and Multiply-Accumulate Long (MULL,MLAL) 4-24 4.9 Single Data Transfer (LDR, STR) 4-26 4.10 Halfword and Signed Data Transfer 4-32 4.11 Block Data Transfer (LDM, STM) 4-37 4.12 Single Data Swap (SWP) 4-43 4.13 Software Interrupt (SWI) 4-45 4.14 Coprocessor Data Operations (CDP) 4-47 4.15 Coprocessor Data Transfers (LDC, STC) 4-49 4.16 Coprocessor Register Transfers (MRC, MCR) 4-53 4.17 Undefined Instruction 4-55 4.18 Instruction Set Examples 4-56 ARM7TDMI-S Data Sheet 4-1 ARM DDI 0084D Final - Open Access ARM Instruction Set 4.1 Instruction Set Summary 4.1.1 Format summary The ARM instruction set formats are shown below. 3 3 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 9876543210 1 0 9 8 7 6 5 4 3 2 1 0 9 8 7 6 5 4 3 2 1 0 Cond 0 0 I Opcode S Rn Rd Operand 2 Data Processing / PSR Transfer Cond 0 0 0 0 0 0 A S Rd Rn Rs 1 0 0 1 Rm Multiply Cond 0 0 0 0 1 U A S RdHi RdLo Rn 1 0 0 1 Rm Multiply Long Cond 0 0 0 1 0 B 0 0 Rn Rd 0 0 0 0 1 0 0 1 Rm Single Data Swap Cond 0 0 0 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 Rn Branch and Exchange Cond 0 0 0 P U 0 W L Rn Rd 0 0 0 0 1 S H 1 Rm Halfword Data Transfer: register offset Cond 0 0 0 P U 1 W L Rn Rd Offset 1 S H 1 Offset Halfword Data Transfer: immediate offset Cond 0 -
A Call-By-Need Lambda Calculus
A CallByNeed Lamb da Calculus Zena M Ariola Matthias Fel leisen Computer Information Science Department Department of Computer Science University of Oregon Rice University Eugene Oregon Houston Texas John Maraist and Martin Odersky Philip Wad ler Institut fur Programmstrukturen Department of Computing Science Universitat Karlsruhe UniversityofGlasgow Karlsruhe Germany Glasgow Scotland Abstract arguments value when it is rst evaluated More pre cisely lazy languages only reduce an argument if the The mismatchbetween the op erational semantics of the value of the corresp onding formal parameter is needed lamb da calculus and the actual b ehavior of implemen for the evaluation of the pro cedure b o dy Moreover af tations is a ma jor obstacle for compiler writers They ter reducing the argument the evaluator will remember cannot explain the b ehavior of their evaluator in terms the resulting value for future references to that formal of source level syntax and they cannot easily com parameter This technique of evaluating pro cedure pa pare distinct implementations of dierent lazy strate rameters is called cal lbyneed or lazy evaluation gies In this pap er we derive an equational characteri A simple observation justies callbyneed the re zation of callbyneed and prove it correct with resp ect sult of reducing an expression if any is indistinguish to the original lamb da calculus The theory is a strictly able from the expression itself in all p ossible contexts smaller theory than the lamb da calculus Immediate Some implementations attempt -
NU-Prolog Reference Manual
NU-Prolog Reference Manual Version 1.5.24 edited by James A. Thom & Justin Zobel Technical Report 86/10 Machine Intelligence Project Department of Computer Science University of Melbourne (Revised November 1990) Copyright 1986, 1987, 1988, Machine Intelligence Project, The University of Melbourne. All rights reserved. The Technical Report edition of this publication is given away free subject to the condition that it shall not, by the way of trade or otherwise, be lent, resold, hired out, or otherwise circulated, without the prior consent of the Machine Intelligence Project at the University of Melbourne, in any form or cover other than that in which it is published and without a similar condition including this condition being imposed on the subsequent user. The Machine Intelligence Project makes no representations or warranties with respect to the contents of this document and specifically disclaims any implied warranties of merchantability or fitness for any particular purpose. Furthermore, the Machine Intelligence Project reserves the right to revise this document and to make changes from time to time in its content without being obligated to notify any person or body of such revisions or changes. The usual codicil that no part of this publication may be reproduced, stored in a retrieval system, used for wrapping fish, or transmitted, in any form or by any means, electronic, mechanical, photocopying, paper dart, recording, or otherwise, without someone's express permission, is not imposed. Enquiries relating to the acquisition of NU-Prolog should be made to NU-Prolog Distribution Manager Machine Intelligence Project Department of Computer Science The University of Melbourne Parkville, Victoria 3052 Australia Telephone: (03) 344 5229. -
PYTHON BASIC OPERATORS Rialspo Int.Co M/Pytho N/Pytho N Basic O Perato Rs.Htm Copyrig Ht © Tutorialspoint.Com
PYTHON BASIC OPERATORS http://www.tuto rialspo int.co m/pytho n/pytho n_basic_o perato rs.htm Copyrig ht © tutorialspoint.com What is an operator? Simple answer can be g iven using expression 4 + 5 is equal to 9. Here, 4 and 5 are called operands and + is called operator. Python lang uag e supports the following types of operators. Arithmetic Operators Comparison (i.e., Relational) Operators Assig nment Operators Log ical Operators Bitwise Operators Membership Operators Identity Operators Let's have a look on all operators one by one. Python Arithmetic Operators: Assume variable a holds 10 and variable b holds 20, then: [ Show Example ] Operator Description Example + Addition - Adds values on either side of the a + b will g ive 30 operator - Subtraction - Subtracts rig ht hand operand a - b will g ive -10 from left hand operand * Multiplication - Multiplies values on either side a * b will g ive 200 of the operator / Division - Divides left hand operand by rig ht b / a will g ive 2 hand operand % Modulus - Divides left hand operand by rig ht b % a will g ive 0 hand operand and returns remainder ** Exponent - Performs exponential (power) a**b will g ive 10 to the power 20 calculation on operators // Floor Division - The division of operands 9//2 is equal to 4 and 9.0//2.0 is equal to where the result is the quotient in which the 4.0 dig its after the decimal point are removed. Python Comparison Operators: Assume variable a holds 10 and variable b holds 20, then: [ Show Example ] Operator Description Example == Checks if the value of two operands are equal (a == b) is not true. -
Variable-Arity Generic Interfaces
Variable-Arity Generic Interfaces T. Stephen Strickland, Richard Cobbe, and Matthias Felleisen College of Computer and Information Science Northeastern University Boston, MA 02115 [email protected] Abstract. Many programming languages provide variable-arity func- tions. Such functions consume a fixed number of required arguments plus an unspecified number of \rest arguments." The C++ standardiza- tion committee has recently lifted this flexibility from terms to types with the adoption of a proposal for variable-arity templates. In this paper we propose an extension of Java with variable-arity interfaces. We present some programming examples that can benefit from variable-arity generic interfaces in Java; a type-safe model of such a language; and a reduction to the core language. 1 Introduction In April of 2007 the C++ standardization committee adopted Gregor and J¨arvi's proposal [1] for variable-length type arguments in class templates, which pre- sented the idea and its implementation but did not include a formal model or soundness proof. As a result, the relevant constructs will appear in the upcom- ing C++09 draft. Demand for this feature is not limited to C++, however. David Hall submitted a request for variable-arity type parameters for classes to Sun in 2005 [2]. For an illustration of the idea, consider the remarks on first-class functions in Scala [3] from the language's homepage. There it says that \every function is a value. Scala provides a lightweight syntax for defining anonymous functions, it supports higher-order functions, it allows functions to be nested, and supports currying." To achieve this integration of objects and closures, Scala's standard library pre-defines ten interfaces (traits) for function types, which we show here in Java-like syntax: interface Function0<Result> { Result apply(); } interface Function1<Arg1,Result> { Result apply(Arg1 a1); } interface Function2<Arg1,Arg2,Result> { Result apply(Arg1 a1, Arg2 a2); } .. -
CS 0449: Introduction to Systems Software
CS 0449: Introduction to Systems Software Jonathan Misurda Computer Science Department University of Pittsburgh [email protected] http://www.cs.pitt.edu/∼jmisurda Version 3, revision 1 Last modified: July 27, 2017 at 1:33 P.M. Copyright © 2017 by Jonathan Misurda This text is meant to accompany the course CS 0449 at the University of Pittsburgh. Any other use, commercial or otherwise, is prohibited without permission of the author. All rights reserved. Java is a registered trademark of Oracle Corporation. This reference is dedicated to the students of CS 0449, Fall 2007 (2081). Their patience in dealing with a changing course and feedback on the first version of this text was greatly appreciated. Contents Contents i List of Figures v List of Code Listings vii Preface ix 1 Pointers 1 1.1 Basic Pointers . 2 1.1.1 Fundamental Operations . 2 1.2 Passing Pointers to Functions . 4 1.3 Pointers, Arrays, and Strings . 5 1.3.1 Pointer Arithmetic . 6 1.4 Terms and Definitions . 7 2 Variables: Scope & Lifetime 8 2.1 Scope and Lifetime in C . 9 2.1.1 Global Variables . 11 2.1.2 Automatic Variables . 12 2.1.3 Register variables . 13 2.1.4 Static Variables . 13 2.1.5 Volatile Variables . 16 2.2 Summary Table . 17 2.3 Terms and Definitions . 17 ii Contents 3 Compiling & Linking: From Code to Executable 19 3.1 The Stages of Compilation . 19 3.1.1 The Preprocessor . 20 3.1.2 The Compiler . 21 3.1.3 The Linker . 22 3.2 Executable File Formats . -
Small Hitting-Sets for Tiny Arithmetic Circuits Or: How to Turn Bad Designs Into Good
Electronic Colloquium on Computational Complexity, Report No. 35 (2017) Small hitting-sets for tiny arithmetic circuits or: How to turn bad designs into good Manindra Agrawal ∗ Michael Forbes† Sumanta Ghosh ‡ Nitin Saxena § Abstract Research in the last decade has shown that to prove lower bounds or to derandomize polynomial identity testing (PIT) for general arithmetic circuits it suffices to solve these questions for restricted circuits. In this work, we study the smallest possibly restricted class of circuits, in particular depth-4 circuits, which would yield such results for general circuits (that is, the complexity class VP). We show that if we can design poly(s)-time hitting-sets for Σ ∧a ΣΠO(log s) circuits of size s, where a = ω(1) is arbitrarily small and the number of variables, or arity n, is O(log s), then we can derandomize blackbox PIT for general circuits in quasipolynomial time. Further, this establishes that either E6⊆#P/poly or that VP6=VNP. We call the former model tiny diagonal depth-4. Note that these are merely polynomials with arity O(log s) and degree ω(log s). In fact, we show that one only needs a poly(s)-time hitting-set against individual-degree a′ = ω(1) polynomials that are computable by a size-s arity-(log s) ΣΠΣ circuit (note: Π fanin may be s). Alternatively, we claim that, to understand VP one only needs to find hitting-sets, for depth-3, that have a small parameterized complexity. Another tiny family of interest is when we restrict the arity n = ω(1) to be arbitrarily small. -
Making a Faster Curry with Extensional Types
Making a Faster Curry with Extensional Types Paul Downen Simon Peyton Jones Zachary Sullivan Microsoft Research Zena M. Ariola Cambridge, UK University of Oregon [email protected] Eugene, Oregon, USA [email protected] [email protected] [email protected] Abstract 1 Introduction Curried functions apparently take one argument at a time, Consider these two function definitions: which is slow. So optimizing compilers for higher-order lan- guages invariably have some mechanism for working around f1 = λx: let z = h x x in λy:e y z currying by passing several arguments at once, as many as f = λx:λy: let z = h x x in e y z the function can handle, which is known as its arity. But 2 such mechanisms are often ad-hoc, and do not work at all in higher-order functions. We show how extensional, call- It is highly desirable for an optimizing compiler to η ex- by-name functions have the correct behavior for directly pand f1 into f2. The function f1 takes only a single argu- expressing the arity of curried functions. And these exten- ment before returning a heap-allocated function closure; sional functions can stand side-by-side with functions native then that closure must subsequently be called by passing the to practical programming languages, which do not use call- second argument. In contrast, f2 can take both arguments by-name evaluation. Integrating call-by-name with other at once, without constructing an intermediate closure, and evaluation strategies in the same intermediate language ex- this can make a huge difference to run-time performance in presses the arity of a function in its type and gives a princi- practice [Marlow and Peyton Jones 2004]. -
Lambda-Calculus Types and Models
Jean-Louis Krivine LAMBDA-CALCULUS TYPES AND MODELS Translated from french by René Cori To my daughter Contents Introduction5 1 Substitution and beta-conversion7 Simple substitution ..............................8 Alpha-equivalence and substitution ..................... 12 Beta-conversion ................................ 18 Eta-conversion ................................. 24 2 Representation of recursive functions 29 Head normal forms .............................. 29 Representable functions ............................ 31 Fixed point combinators ........................... 34 The second fixed point theorem ....................... 37 3 Intersection type systems 41 System D ................................... 41 System D .................................... 50 Typings for normal terms ........................... 54 4 Normalization and standardization 61 Typings for normalizable terms ........................ 61 Strong normalization ............................. 68 ¯I-reduction ................................. 70 The ¸I-calculus ................................ 72 ¯´-reduction ................................. 74 The finite developments theorem ....................... 77 The standardization theorem ......................... 81 5 The Böhm theorem 87 3 4 CONTENTS 6 Combinatory logic 95 Combinatory algebras ............................. 95 Extensionality axioms ............................. 98 Curry’s equations ............................... 101 Translation of ¸-calculus ........................... 105 7 Models of lambda-calculus 111 Functional