Defining (Co)Datatypes and Primitively (Co)Recursive Functions in Isabelle
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Unfold/Fold Transformations and Loop Optimization of Logic Programs
Unfold/Fold Transformations and Loop Optimization of Logic Programs Saumya K. Debray Department of Computer Science The University of Arizona Tucson, AZ 85721 Abstract: Programs typically spend much of their execution time in loops. This makes the generation of ef®cient code for loops essential for good performance. Loop optimization of logic programming languages is complicated by the fact that such languages lack the iterative constructs of traditional languages, and instead use recursion to express loops. In this paper, we examine the application of unfold/fold transformations to three kinds of loop optimization for logic programming languages: recur- sion removal, loop fusion and code motion out of loops. We describe simple unfold/fold transformation sequences for these optimizations that can be automated relatively easily. In the process, we show that the properties of uni®cation and logical variables can sometimes be used to generalize, from traditional languages, the conditions under which these optimizations may be carried out. Our experience suggests that such source-level transformations may be used as an effective tool for the optimization of logic pro- grams. 1. Introduction The focus of this paper is on the static optimization of logic programs. Speci®cally, we investigate loop optimization of logic programs. Since programs typically spend most of their time in loops, the generation of ef®cient code for loops is essential for good performance. In the context of logic programming languages, the situation is complicated by the fact that iterative constructs, such as for or while, are unavailable. Loops are usually expressed using recursive procedures, and loop optimizations have be considered within the general framework of inter- procedural optimization. -
A Right-To-Left Type System for Value Recursion
1 A right-to-left type system for value recursion 61 2 62 3 63 4 Alban Reynaud Gabriel Scherer Jeremy Yallop 64 5 ENS Lyon, France INRIA, France University of Cambridge, UK 65 6 66 1 Introduction Seeking to address these problems, we designed and imple- 7 67 mented a new check for recursive definition safety based ona 8 In OCaml recursive functions are defined using the let rec opera- 68 novel static analysis, formulated as a simple type system (which we 9 tor, as in the following definition of factorial: 69 have proved sound with respect to an existing operational seman- 10 let rec fac x = if x = 0 then 1 70 tics [Nordlander et al. 2008]), and implemented as part of OCaml’s 11 else x * (fac (x - 1)) 71 type-checking phase. Our check was merged into the OCaml distri- 12 Beside functions, let rec can define recursive values, such as 72 bution in August 2018. 13 an infinite list ones where every element is 1: 73 Moving the check from the middle end to the type checker re- 14 74 let rec ones = 1 :: ones stores the desirable property that compilation of well-typed programs 15 75 Note that this “infinite” list is actually cyclic, and consists of asingle does not go wrong. This property is convenient for tools that reuse 16 76 cons-cell referencing itself. OCaml’s type-checker without performing compilation, such as 17 77 However, not all recursive definitions can be computed. The MetaOCaml [Kiselyov 2014] (which type-checks quoted code) and 18 78 following definition is justly rejected by the compiler: Merlin [Bour et al. -
Chapter 5 Type Declarations
Ch.5: Type Declarations Plan Chapter 5 Type Declarations (Version of 27 September 2004) 1. Renaming existing types . 5.2 2. Enumeration types . 5.3 3. Constructed types . 5.5 4. Parameterised/polymorphic types . 5.10 5. Exceptions, revisited . 5.12 6. Application: expression evaluation . 5.13 °c P. Flener/IT Dept/Uppsala Univ. FP 5.1 Ch.5: Type Declarations 5.1. Renaming existing types 5.1. Renaming existing types Example: polynomials, revisited Representation of a polynomial by a list of integers: type poly = int list ² Introduction of an abbreviation for the type int list ² The two names denote the same type ² The object [4,2,8] is of type poly and of type int list - type poly = int list ; type poly = int list - type poly2 = int list ; type poly2 = int list - val p:poly = [1,2] ; val p = [1,2] : poly - val p2:poly2 = [1,2] ; val p2 = [1,2] : poly2 - p = p2 ; val it = true : bool °c P. Flener/IT Dept/Uppsala Univ. FP 5.2 Ch.5: Type Declarations 5.2. Enumeration types 5.2. Enumeration types Declaration of a new type having a finite number of values Example (weekend.sml) datatype months = Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec datatype days = Mon | Tue | Wed | Thu | Fri | Sat | Sun fun weekend Sat = true | weekend Sun = true | weekend d = false - datatype months = Jan | ... | Dec ; datatype months = Jan | ... | Dec - datatype days = Mon | ... | Sun ; datatype days = Mon | ... | Sun - fun weekend ... ; val weekend = fn : days -> bool °c P. Flener/IT Dept/Uppsala Univ. FP 5.3 Ch.5: Type Declarations 5.2. -
Nominal Wyvern: Employing Semantic Separation for Usability Yu Xiang Zhu CMU-CS-19-105 April 2019
Nominal Wyvern: Employing Semantic Separation for Usability Yu Xiang Zhu CMU-CS-19-105 April 2019 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Thesis Committee: Jonathan Aldrich, Chair Heather Miller Alex Potanin, Victoria University of Wellington, NZ Submitted in partial fulfillment of the requirements for the degree of Master of Science. Copyright c 2019 Yu Xiang Zhu Keywords: Nominality, Wyvern, Dependent Object Types, Subtype Decidability Abstract This thesis presents Nominal Wyvern, a nominal type system that empha- sizes semantic separation for better usability. Nominal Wyvern is based on the dependent object types (DOT) calculus, which provides greater expressiv- ity than traditional object-oriented languages by incorporating concepts from functional languages. Although DOT is generally perceived to be nominal due to its path-dependent types, it is still a mostly structural system and relies on the free construction of types. This can present usability issues in a subtyping- based system where the semantics of a type are as important as its syntactic structure. Nominal Wyvern overcomes this problem by semantically separat- ing structural type/subtype definitions from ad hoc type refinements and type bound declarations. In doing so, Nominal Wyvern is also able to overcome the subtype undecidability problem of DOT by adopting a semantics-based sep- aration between types responsible for recursive subtype definitions and types that represent concrete data. The result is a more intuitive type system that achieves nominality and decidability while maintaining the expressiveness of F-bounded polymorphism that is used in practice. iv Acknowledgments This research would not have been possible without the support from the following in- dividuals. -
Cablelabs® Specifications Cablelabs' DHCP Options Registry CL-SP-CANN-DHCP-Reg-I13-160317
CableLabs® Specifications CableLabs' DHCP Options Registry CL-SP-CANN-DHCP-Reg-I13-160317 ISSUED Notice This CableLabs specification is the result of a cooperative effort undertaken at the direction of Cable Television Laboratories, Inc. for the benefit of the cable industry and its customers. You may download, copy, distribute, and reference the documents herein only for the purpose of developing products or services in accordance with such documents, and educational use. Except as granted by CableLabs in a separate written license agreement, no license is granted to modify the documents herein (except via the Engineering Change process), or to use, copy, modify or distribute the documents for any other purpose. This document may contain references to other documents not owned or controlled by CableLabs. Use and understanding of this document may require access to such other documents. Designing, manufacturing, distributing, using, selling, or servicing products, or providing services, based on this document may require intellectual property licenses from third parties for technology referenced in this document. To the extent this document contains or refers to documents of third parties, you agree to abide by the terms of any licenses associated with such third-party documents, including open source licenses, if any. Cable Television Laboratories, Inc. 2006-2016 CL-SP-CANN-DHCP-Reg-I13-160317 CableLabs® Specifications DISCLAIMER This document is furnished on an "AS IS" basis and neither CableLabs nor its members provides any representation or warranty, express or implied, regarding the accuracy, completeness, noninfringement, or fitness for a particular purpose of this document, or any document referenced herein. Any use or reliance on the information or opinion in this document is at the risk of the user, and CableLabs and its members shall not be liable for any damage or injury incurred by any person arising out of the completeness, accuracy, or utility of any information or opinion contained in the document. -
Project Overview 1 Introduction 2 a Quick Introduction to SOOL
CMSC 22600 Compilers for Computer Languages Handout 1 Autumn 2016 October 6, 2016 Project Overview 1 Introduction The project for the course is to implement a small object-oriented programming language, called SOOL. The project will consist of four parts: 1. the parser and scanner, which coverts a textual representation of the program into a parse tree representation. 2. the type checker, which checks the parse tree for type correctness and produces a typed ab- stract syntax tree (AST) 3. the normalizer, which converts the typed AST representation into a static single-assignment (SSA) representation. 4. the code generator, which takes the SSA representation and generates LLVM assembly code. Each part of the project builds upon the previous parts, but we will provide reference solutions for previous parts. You will implement the project in the Standard ML programming language and submission of the project milestones will be managed using Phoenixforge. Important note: You are expected to submit code that compiles and that is well documented. Points for project code are assigned 30% for coding style (documentation, choice of variable names, and program structure), and 70% for correctness. Code that does not compile will not receive any points for correctness. 2 A quick introduction to SOOL SOOL is a statically-typed object-oriented language. It supports single inheritance with nominal subtyping and interfaces with structural subtyping. The SOOL version of the classic Hello World program is class main () { meth run () -> void { system.print ("hello world\n"); } } Here we are using the predefined system object to print the message. Every SOOL program has a main class that must have a run function. -
Functional Programming (ML)
Functional Programming CSE 215, Foundations of Computer Science Stony Brook University http://www.cs.stonybrook.edu/~cse215 Functional Programming Function evaluation is the basic concept for a programming paradigm that has been implemented in functional programming languages. The language ML (“Meta Language”) was originally introduced in the 1970’s as part of a theorem proving system, and was intended for describing and implementing proof strategies. Standard ML of New Jersey (SML) is an implementation of ML. The basic mode of computation in SML is the use of the definition and application of functions. 2 (c) Paul Fodor (CS Stony Brook) Install Standard ML Download from: http://www.smlnj.org Start Standard ML: Type ''sml'' from the shell (run command line in Windows) Exit Standard ML: Ctrl-Z under Windows Ctrl-D under Unix/Mac 3 (c) Paul Fodor (CS Stony Brook) Standard ML The basic cycle of SML activity has three parts: read input from the user, evaluate it, print the computed value (or an error message). 4 (c) Paul Fodor (CS Stony Brook) First SML example SML prompt: - Simple example: - 3; val it = 3 : int The first line contains the SML prompt, followed by an expression typed in by the user and ended by a semicolon. The second line is SML’s response, indicating the value of the input expression and its type. 5 (c) Paul Fodor (CS Stony Brook) Interacting with SML SML has a number of built-in operators and data types. it provides the standard arithmetic operators - 3+2; val it = 5 : int The Boolean values true and false are available, as are logical operators such as not (negation), andalso (conjunction), and orelse (disjunction). -
Abstract Recursion and Intrinsic Complexity
ABSTRACT RECURSION AND INTRINSIC COMPLEXITY Yiannis N. Moschovakis Department of Mathematics University of California, Los Angeles [email protected] October 2018 iv Abstract recursion and intrinsic complexity was first published by Cambridge University Press as Volume 48 in the Lecture Notes in Logic, c Association for Symbolic Logic, 2019. The Cambridge University Press catalog entry for the work can be found at https://www.cambridge.org/us/academic/subjects/mathematics /logic-categories-and-sets /abstract-recursion-and-intrinsic-complexity. The published version can be purchased through Cambridge University Press and other standard distribution channels. This copy is made available for personal use only and must not be sold or redistributed. This final prepublication draft of ARIC was compiled on November 30, 2018, 22:50 CONTENTS Introduction ................................................... .... 1 Chapter 1. Preliminaries .......................................... 7 1A.Standardnotations................................ ............. 7 Partial functions, 9. Monotone and continuous functionals, 10. Trees, 12. Problems, 14. 1B. Continuous, call-by-value recursion . ..................... 15 The where -notation for mutual recursion, 17. Recursion rules, 17. Problems, 19. 1C.Somebasicalgorithms............................. .................... 21 The merge-sort algorithm, 21. The Euclidean algorithm, 23. The binary (Stein) algorithm, 24. Horner’s rule, 25. Problems, 25. 1D.Partialstructures............................... ...................... -
Generating Mutually Recursive Definitions
Generating Mutually Recursive Definitions Jeremy Yallop Oleg Kiselyov University of Cambridge, UK Tohoku University, Japan [email protected] [email protected] Abstract let rec s = function A :: r ! s r j B :: r ! t r j [] ! true and t = function A :: r ! s r j B :: r ! u r j [] ! false Many functional programs — state machines (Krishnamurthi and u = function A :: r ! t r j B :: r ! u r j [] ! false 2006), top-down and bottom-up parsers (Hutton and Meijer 1996; Hinze and Paterson 2003), evaluators (Abelson et al. 1984), GUI where each function s, t, and u realizes a recognizer, taking a list of initialization graphs (Syme 2006), &c. — are conveniently ex- A and B symbols and returning a boolean. However, the program pressed as groups of mutually recursive bindings. One therefore that builds such a group from a description of an arbitrary state expects program generators, such as those written in MetaOCaml, machine cannot be expressed in MetaOCaml. to be able to build programs with mutual recursion. The limited support for generating mutual recursion is a con- Unfortunately, currently MetaOCaml can only build recursive sequence of expression-based quotation: brackets enclose expres- groups whose size is hard-coded in the generating program. The sions, and splices insert expressions into expressions — but a group general case requires something other than quotation, and seem- of bindings is not an expression. There is a second difficulty: gen- ingly weakens static guarantees on the resulting code. We describe erating recursive definitions with ‘backward’ and ‘forward’ refer- the challenges and propose a new language construct for assuredly ences seemingly requires unrestricted, Lisp-like gensym, which de- generating binding groups of arbitrary size – illustrating with a col- feats MetaOCaml’s static guarantees. -
An Introduction to Lean
An Introduction to Lean Jeremy Avigad Leonardo de Moura Gabriel Ebner and Sebastian Ullrich Version 1fc176a, updated at 2017-01-09 14:16:26 -0500 2 Contents Contents 3 1 Overview 5 1.1 Perspectives on Lean ............................... 5 1.2 Where To Go From Here ............................. 12 2 Defining Objects in Lean 13 2.1 Some Basic Types ................................ 14 2.2 Defining Functions ................................ 17 2.3 Defining New Types ............................... 20 2.4 Records and Structures ............................. 22 2.5 Nonconstructive Definitions ........................... 25 3 Programming in Lean 27 3.1 Evaluating Expressions .............................. 28 3.2 Recursive Definitions ............................... 30 3.3 Inhabited Types, Subtypes, and Option Types . 32 3.4 Monads ...................................... 34 3.5 Input and Output ................................ 35 3.6 An Example: Abstract Syntax ......................... 36 4 Theorem Proving in Lean 38 4.1 Assertions in Dependent Type Theory ..................... 38 4.2 Propositions as Types .............................. 39 4.3 Induction and Calculation ............................ 42 4.4 Axioms ...................................... 45 5 Using Automation in Lean 46 6 Metaprogramming in Lean 47 3 CONTENTS 4 Bibliography 48 1 Overview This introduction offers a tour of Lean and its features, with a number of examples foryou to play around with and explore. If you are reading this in our online tutorial system, you can run examples like the one below by clicking the button that says “try it yourself.” #check "hello world!" The response from Lean appears in the small window underneath the editor text, and also in popup windows that you can read when you hover over the indicators in the left margin. Alternatively, if you have installed Lean and have it running in a stand-alone editor, you can copy and paste examples and try them there. -
Formats and Protocols for Continuous Data CD-1.1
IDC Software documentation Formats and Protocols for Continuous Data CD-1.1 Document Version: 0.3 Document Edition: English Document Status: Final Document ID: IDC 3.4.3 Document Date: 18 December 2002 IDC Software documentation Formats and Protocols for Continuous Data CD-1.1 Version: 0.3 Notice This document was published December 2002 as Revision 0.3, using layout templates established by the IPT Group of CERN and IDC Corporate Style guidelines. This document is a re-casting of Revision 0.2published in December 2001 as part of the International Data Centre (IDC) Documentation. It was first published in July 2000 and was repub- lished electronically as Revision 0.1 in September 2001 and as Revision 0.2 in December 2001 to include minor changes (see the following Change Page for Revision 0.2). IDC documents that have been changed substantially are indicated by a whole revision number (for example, Revision 1). Trademarks IEEE is a registered trademark of the Institute of Electrical and Electronics Engineers, Inc. Motorola is a registered trademark of Motorola Inc. SAIC is a trademark of Science Applications International Corporation. Sun is a registered trademark of Sun Microsystems. UNIX is a registered trademark of UNIX System Labs, Inc. This document has been prepared using the Software Documentation Layout Templates that have been prepared by the IPT Group (Information, Process and Technology), IT Division, CERN (The European Laboratory for Particle Physics). For more information, go to http://framemaker.cern.ch/. page ii Final Abstract Version: 0.3 Abstract This document describes the formats and protocols of Continuous Data (CD-1.1), a standard to be used for transmitting continuous, time series data from stations of the International Monitoring System (IMS) to the International Data Centre (IDC) and for transmitting these data from the IDC to National Data Centers (NDCs). -
Inductively Defined Functions in Functional Programming Languages
CORE Metadata, citation and similar papers at core.ac.uk Provided by Elsevier - Publisher Connector JOURNAL OF COMPUTER AND SYSTEM SCIENCES 34, 4099421 (1987) Inductively Defined Functions in Functional Programming Languages R. M. BURSTALL Department of Computer Science, University of Edinburgh, King$ Buildings, Mayfield Road, Edinburgh EH9 3JZ, Scotland, U.K. Received December 1985; revised August 1986 This paper proposes a notation for defining functions or procedures in such a way that their termination is guaranteed by the scope rules. It uses an extension of case expressions. Suggested uses include programming languages and logical languages; an application is also given to the problem of proving inequations from initial algebra specifications. 0 1987 Academic Press, Inc. 1. INTRODUCTION When we define recursive functions on natural numbers we can ensure that they terminate without a special proof, by adhering to the schema for primitive recur- sion. The schema can be extended to other data types. This requires inspection of the function definition to ensure that it conforms to such a schema, involving second-order pattern matching. However, if we combine the recursion with a case expression which decomposes elements of the data type we can use the ordinary scoping rules for variables to ensure termination, without imposing any special schema. I formulate this proposal for “inductive” case expressions in ML [S], since it offers succinct data-type definitions and a convenient case expression. Any other functional language with these facilities could do as well. A number of people have advocated the use of initial algebras to define data types in specification languages, see, for example, Goguen, Thatcher and Wagner [3] and Burstall and Goguen [ 11.