APPENDIX Description of the L Yap AS Language* A. D. Zakrevskii
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Redacted for Privacy Professor Donald Guthrie, Jr
AN ABSTRACT OF THE THESIS OF John Anthony Battilega for the DOCTOR OF PHILOSOPHY (Name) (Degree) in Mathematics presented on May 4, 1973 (Major) (Date) Title: COMPUTATIONAL IMPROVEMENTS TO BENDERS DECOMPOSITION FOR GENERALIZED FIXED CHARGE PROBLEMS Abstract approved Redacted for privacy Professor Donald Guthrie, Jr. A computationally efficient algorithm has been developed for determining exact or approximate solutions for large scale gener- alized fixed charge problems. This algorithm is based on a relaxa- tion of the Benders decomposition procedure, combined with a linear mixed integer programming (MIP) algorithm specifically designed to solve the problem associated with Benders decomposition and a com- putationally improved generalized upper bounding (GUB) algorithm which solves a convex separable programming problem by generalized linear programming. A dynamic partitioning technique is defined and used to improve computational efficiency.All component algor- ithms have been theoretically and computationally integrated with the relaxed Benders algorithm for maximum efficiency for the gener- alized fixed charge problem. The research was directed toward the approximate solution of a particular class of large scale generalized fixed charge problems, and extensive computational results for problemsof this type are given.As the size of the problem diminishes, therelaxations can be enforced, resulting in a classical Bendersdecomposition, but with special purpose sub-algorithmsand improved convergence pro- perties. Many of the results obtained apply to the sub-algorithmsinde- pendently of the context in which theywere developed. The proce- dure for solving the associated MIP is applicableto any linear 0/1 problem of Benders form, and the techniquesdeveloped for the linear program are applicable to any large scale generalized GUB implemen- tation. -
An Array-Oriented Language with Static Rank Polymorphism
An array-oriented language with static rank polymorphism Justin Slepak, Olin Shivers, and Panagiotis Manolios Northeastern University fjrslepak,shivers,[email protected] Abstract. The array-computational model pioneered by Iverson's lan- guages APL and J offers a simple and expressive solution to the \von Neumann bottleneck." It includes a form of rank, or dimensional, poly- morphism, which renders much of a program's control structure im- plicit by lifting base operators to higher-dimensional array structures. We present the first formal semantics for this model, along with the first static type system that captures the full power of the core language. The formal dynamic semantics of our core language, Remora, illuminates several of the murkier corners of the model. This allows us to resolve some of the model's ad hoc elements in more general, regular ways. Among these, we can generalise the model from SIMD to MIMD computations, by extending the semantics to permit functions to be lifted to higher- dimensional arrays in the same way as their arguments. Our static semantics, a dependent type system of carefully restricted power, is capable of describing array computations whose dimensions cannot be determined statically. The type-checking problem is decidable and the type system is accompanied by the usual soundness theorems. Our type system's principal contribution is that it serves to extract the implicit control structure that provides so much of the language's expres- sive power, making this structure explicitly apparent at compile time. 1 The Promise of Rank Polymorphism Behind every interesting programming language is an interesting model of com- putation. -
Compendium of Technical White Papers
COMPENDIUM OF TECHNICAL WHITE PAPERS Compendium of Technical White Papers from Kx Technical Whitepaper Contents Machine Learning 1. Machine Learning in kdb+: kNN classification and pattern recognition with q ................................ 2 2. An Introduction to Neural Networks with kdb+ .......................................................................... 16 Development Insight 3. Compression in kdb+ ................................................................................................................. 36 4. Kdb+ and Websockets ............................................................................................................... 52 5. C API for kdb+ ............................................................................................................................ 76 6. Efficient Use of Adverbs ........................................................................................................... 112 Optimization Techniques 7. Multi-threading in kdb+: Performance Optimizations and Use Cases ......................................... 134 8. Kdb+ tick Profiling for Throughput Optimization ....................................................................... 152 9. Columnar Database and Query Optimization ............................................................................ 166 Solutions 10. Multi-Partitioned kdb+ Databases: An Equity Options Case Study ............................................. 192 11. Surveillance Technologies to Effectively Monitor Algo and High Frequency Trading .................. -
Dynamic Functions in Dyalog
Direct Functions in Dyalog APL John Scholes – Dyalog Ltd. [email protected] A Direct Function (dfn) is a new function definition style, which bridges the gap between named function expressions such as and APL’s traditional ‘header’ style definition. Simple Expressions The simplest form of dfn is: {expr} where expr is an APL expression containing s and s representing the left and right argument of the function respectively. For example: A dfn can be used in any function context ... ... and of course, assigned a name: Dfns are ambivalent. Their right (and if present, left) arguments are evaluated irrespective of whether these are subsequently referenced within the function body. Guards A guard is a boolean-single valued expression followed by . A simple expression can be preceded by a guard, and any number of guarded expressions can occur separated by s. Guards are evaluated in turn (left to right) until one of them yields a 1. Its corresponding expr is then evaluated as the result of the dfn. A guard is equivalent to an If-Then-Else or Switch-Case construct. A final simple expr can be thought of as a default case: The s can be replaced with newlines. For readability, extra null phrases can be included. The parity example above becomes: Named dfns can be reviewed using the system editor: or , and note how you can comment them in the normal way using . The following example interprets a dice throw: Local Definition The final piece of dfn syntax is the local definition. An expression whose principal function is a simple or vector assignment, introduces a name that is local to the dfn. -
Application and Interpretation
Programming Languages: Application and Interpretation Shriram Krishnamurthi Brown University Copyright c 2003, Shriram Krishnamurthi This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 United States License. If you create a derivative work, please include the version information below in your attribution. This book is available free-of-cost from the author’s Web site. This version was generated on 2007-04-26. ii Preface The book is the textbook for the programming languages course at Brown University, which is taken pri- marily by third and fourth year undergraduates and beginning graduate (both MS and PhD) students. It seems very accessible to smart second year students too, and indeed those are some of my most successful students. The book has been used at over a dozen other universities as a primary or secondary text. The book’s material is worth one undergraduate course worth of credit. This book is the fruit of a vision for teaching programming languages by integrating the “two cultures” that have evolved in its pedagogy. One culture is based on interpreters, while the other emphasizes a survey of languages. Each approach has significant advantages but also huge drawbacks. The interpreter method writes programs to learn concepts, and has its heart the fundamental belief that by teaching the computer to execute a concept we more thoroughly learn it ourselves. While this reasoning is internally consistent, it fails to recognize that understanding definitions does not imply we understand consequences of those definitions. For instance, the difference between strict and lazy evaluation, or between static and dynamic scope, is only a few lines of interpreter code, but the consequences of these choices is enormous. -
Handout 16: J Dictionary
J Dictionary Roger K.W. Hui Kenneth E. Iverson Copyright © 1991-2002 Jsoftware Inc. All Rights Reserved. Last updated: 2002-09-10 www.jsoftware.com . Table of Contents 1 Introduction 2 Mnemonics 3 Ambivalence 4 Verbs and Adverbs 5 Punctuation 6 Forks 7 Programs 8 Bond Conjunction 9 Atop Conjunction 10 Vocabulary 11 Housekeeping 12 Power and Inverse 13 Reading and Writing 14 Format 15 Partitions 16 Defined Adverbs 17 Word Formation 18 Names and Displays 19 Explicit Definition 20 Tacit Equivalents 21 Rank 22 Gerund and Agenda 23 Recursion 24 Iteration 25 Trains 26 Permutations 27 Linear Functions 28 Obverse and Under 29 Identity Functions and Neutrals 30 Secondaries 31 Sample Topics 32 Spelling 33 Alphabet and Numbers 34 Grammar 35 Function Tables 36 Bordering a Table 37 Tables (Letter Frequency) 38 Tables 39 Classification 40 Disjoint Classification (Graphs) 41 Classification I 42 Classification II 43 Sorting 44 Compositions I 45 Compositions II 46 Junctions 47 Partitions I 48 Partitions II 49 Geometry 50 Symbolic Functions 51 Directed Graphs 52 Closure 53 Distance 54 Polynomials 55 Polynomials (Continued) 56 Polynomials in Terms of Roots 57 Polynomial Roots I 58 Polynomial Roots II 59 Polynomials: Stopes 60 Dictionary 61 I. Alphabet and Words 62 II. Grammar 63 A. Nouns 64 B. Verbs 65 C. Adverbs and Conjunctions 66 D. Comparatives 67 E. Parsing and Execution 68 F. Trains 69 G. Extended and Rational Arithmeti 70 H. Frets and Scripts 71 I. Locatives 72 J. Errors and Suspensions 73 III. Definitions 74 Vocabulary 75 = Self-Classify - Equal 76 =. Is (Local) 77 < Box - Less Than 78 <. -
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Chapter 1 Basic Principles of Programming Languages
Chapter 1 Basic Principles of Programming Languages Although there exist many programming languages, the differences among them are insignificant compared to the differences among natural languages. In this chapter, we discuss the common aspects shared among different programming languages. These aspects include: programming paradigms that define how computation is expressed; the main features of programming languages and their impact on the performance of programs written in the languages; a brief review of the history and development of programming languages; the lexical, syntactic, and semantic structures of programming languages, data and data types, program processing and preprocessing, and the life cycles of program development. At the end of the chapter, you should have learned: what programming paradigms are; an overview of different programming languages and the background knowledge of these languages; the structures of programming languages and how programming languages are defined at the syntactic level; data types, strong versus weak checking; the relationship between language features and their performances; the processing and preprocessing of programming languages, compilation versus interpretation, and different execution models of macros, procedures, and inline procedures; the steps used for program development: requirement, specification, design, implementation, testing, and the correctness proof of programs. The chapter is organized as follows. Section 1.1 introduces the programming paradigms, performance, features, and the development of programming languages. Section 1.2 outlines the structures and design issues of programming languages. Section 1.3 discusses the typing systems, including types of variables, type equivalence, type conversion, and type checking during the compilation. Section 1.4 presents the preprocessing and processing of programming languages, including macro processing, interpretation, and compilation. -
A Modern Reversible Programming Language April 10, 2015
Arrow: A Modern Reversible Programming Language Author: Advisor: Eli Rose Bob Geitz Abstract Reversible programming languages are those whose programs can be run backwards as well as forwards. This condition impacts even the most basic constructs, such as =, if and while. I discuss Janus, the first im- perative reversible programming language, and its limitations. I then introduce Arrow, a reversible language with modern features, including functions. Example programs are provided. April 10, 2015 Introduction: Many processes in the world have the property of reversibility. To start washing your hands, you turn the knob to the right, and the water starts to flow; the process can be undone, and the water turned off, by turning the knob to the left. To turn your computer on, you press the power button; this too can be undone, by again pressing the power button. In each situation, we had a process (turning the knob, pressing the power button) and a rule that told us how to \undo" that process (turning the knob the other way, and pressing the power button again). Call the second two the inverses of the first two. By a reversible process, I mean a process that has an inverse. Consider two billiard balls, with certain positions and velocities such that they are about to collide. The collision is produced by moving the balls accord- ing to the laws of physics for a few seconds. Take that as our process. It turns out that we can find an inverse for this process { a set of rules to follow which will undo the collision and restore the balls to their original states1. -
APL-The Language Debugging Capabilities, Entirely in APL Terms (No Core Symbol Denotes an APL Function Named' 'Compress," Dumps Or Other Machine-Related Details)
DANIEL BROCKLEBANK APL - THE LANGUAGE Computer programming languages, once the specialized tools of a few technically trained peo p.le, are now fundamental to the education and activities of millions of people in many profes SIons, trades, and arts. The most widely known programming languages (Basic, Fortran, Pascal, etc.) have a strong commonality of concepts and symbols; as a collection, they determine our soci ety's general understanding of what programming languages are like. There are, however, several ~anguages of g~eat interest and quality that are strikingly different. One such language, which shares ItS acronym WIth the Applied Physics Laboratory, is APL (A Programming Language). A SHORT HISTORY OF APL it struggled through the 1970s. Its international con Over 20 years ago, Kenneth E. Iverson published tingent of enthusiasts was continuously hampered by a text with the rather unprepossessing title, A inefficient machine use, poor availability of suitable terminal hardware, and, as always, strong resistance Programming Language. I Dr. Iverson was of the opinion that neither conventional mathematical nota to a highly unconventional language. tions nor the emerging Fortran-like programming lan At the Applied Physics Laboratory, the APL lan guages were conducive to the fluent expression, guage and its practical use have been ongoing concerns publication, and discussion of algorithms-the many of the F. T. McClure Computing Center, whose staff alternative ideas and techniques for carrying out com has long been convinced of its value. Addressing the putation. His text presented a solution to this nota inefficiency problems, the Computing Center devel tion dilemma: a new formal language for writing clear, oped special APL systems software for the IBM concise computer programs. -
Ginger Documentation Release 1.0
Ginger Documentation Release 1.0 sfkl / gjh Nov 03, 2017 Contents 1 Contents 3 2 Help Topics 27 3 Common Syntax 53 4 Design Rationales 55 5 The Ginger Toolchain 83 6 Low-Level Implementation 99 7 Release Notes 101 8 Indices and tables 115 Bibliography 117 i ii Ginger Documentation, Release 1.0 This documentation is still very much work in progress The aim of the Ginger Project is to create a modern programming language and its ecosystem of libraries, documen- tation and supporting tools. The Ginger language draws heavily on the multi-language Poplog environment. Contents 1 Ginger Documentation, Release 1.0 2 Contents CHAPTER 1 Contents 1.1 Overview of Ginger Author Stephen Leach Email [email protected] 1.1.1 Background Ginger is our next evolution of the Spice project. Ginger itself is a intended to be a rigorous but friendly programming language and supporting toolset. It includes a syntax-neutral programming language, a virtual machine implemented in C++ that is designed to support the family of Spice language efficiently, and a collection of supporting tools. Spice has many features that are challenging to support efficiently in existing virtual machines: pervasive multiple values, multiple-dispatch, multiple-inheritance, auto-loading and auto-conversion, dynamic virtual machines, implicit forcing and last but not least fully dynamic typing. The virtual machine is a re-engineering of a prototype interpreter that I wrote on holiday while I was experimenting with GCC’s support for FORTH-like threaded interpreters. But the toolset is designed so that writing alternative VM implementations is quite straightforward - and we hope to exploit that to enable embedding Ginger into lots of other systems. -
A Guide to PL/M Programming
INTEL CORP. 3065 Bowers Avenue, Santa Clara, California 95051 • (408) 246-7501 mcs=a A Guide to PL/M programming PL/M is a new high level programming language designed specifically for Intel's 8 bit microcomputers. The new language gives the microcomputer systems program mer the same advantages of high level language programming currently available in the mini and large computer fields. Designed to meet the special needs of systems programming, the new language will drastically cut microcomputer programming time and costs without sacrifice of program efficiency. in addition, training, docu mentation, program maintenance and the inclusion of library subroutines will all be made correspondingly easier. PL/M is well suited for all microcomputer program ming applications, retaining the control and efficiency of assembly language, while greatly reducing programming effort The PL/M compiler is written in ANSI stand ard Fortran I V and thus will execute on most machines without alteration. SEPTEMBER 1973 REV.1 © Intel Corporation 1973 TABLE OF CONTENTS Section Page I. INTRODUCTION TO PL/M 1 II. A TUTORIAL APPROACH TO PL/M ............................ 2 1. The Organization of a PL/M Program ........................ 2 2. Basic Constituents of a PL/M Program ....................... 4 2.1. PL/M Character Set ................................ 4 2.2. Identifiers and Reserved Words ....................... 5 2.3. Comments . 7 3. PL/M Statement Organization . 7 4. PL/M Data Elements . 9 4.1. Variable Declarations ............................... 9 4.2. Byte and Double Byte Constants. .. 10 5. Well-Formed Expressions and Assignments . 11 6. A Simple Example. 15 7. DO-Groups. 16 7.1. The DO-WHILE Group ...........