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Second Edition
by Harold Abelson and Gerald Jay Sussman, with Julie Sussman
foreword by Alan J. Perlis
(C) 1996 Massachusetts Institute of Technology
Unofficial Texinfo Format version 2.neilvandyke4 (January 10, 2007)
Unofficial Texinfo Format Dedication Foreword Preface to the Second Edition Preface to the First Edition Acknowledgements 1. Building Abstractions with Procedures 2. Building Abstractions with Data 3. Modularity, Objects, and State 4. Metalinguistic Abstraction 5. Computing with Register Machines References Index
-- The Detailed Node Listing ---
Programming in Lisp
1.1 The Elements of Programming 1.2 Procedures and the Processes They Generate 1.3 Formulating Abstractions with Higher-Order Procedures
The Elements of Programming
1.1.1 Expressions 1.1.2 Naming and the Environment 1.1.3 Evaluating Combinations 1.1.4 Compound Procedures 1.1.5 The Substitution Model for Procedure Application 1.1.6 Conditional Expressions and Predicates 1.1.7 Example: Square Roots by Newton's Method 1.1.8 Procedures as Black-Box Abstractions
Procedures and the Processes They Generate
1.2.1 Linear Recursion and Iteration 1.2.2 Tree Recursion 1.2.3 Orders of Growth 1.2.4 Exponentiation 1.2.5 Greatest Common Divisors 1.2.6 Example: Testing for Primality
Formulating Abstractions with Higher-Order Procedures
1.3.1 Procedures as Arguments 1.3.2 Constructing Procedures Using Lambda1.3.3 Procedures as General Methods 1.3.4 Procedures as Returned Values
Building Abstractions with Data
2.1 Introduction to Data Abstraction 2.2 Hierarchical Data and the Closure Property 2.3 Symbolic Data 2.4 Multiple Representations for Abstract Data 2.5 Systems with Generic Operations
Introduction to Data Abstraction
2.1.1 Example: Arithmetic Operations for Rational Numbers 2.1.2 Abstraction Barriers 2.1.3 What Is Meant by Data? 2.1.4 Extended Exercise: Interval Arithmetic
Hierarchical Data and the Closure Property
2.2.1 Representing Sequences 2.2.2 Hierarchical Structures 2.2.3 Sequences as Conventional Interfaces 2.2.4 Example: A Picture Language
Symbolic Data
2.3.1 Quotation 2.3.2 Example: Symbolic Differentiation 2.3.3 Example: Representing Sets 2.3.4 Example: Huffman Encoding Trees
Multiple Representations for Abstract Data
2.4.1 Representations for Complex Numbers 2.4.2 Tagged data 2.4.3 Data-Directed Programming and Additivity
Systems with Generic Operations
2.5.1 Generic Arithmetic Operations 2.5.2 Combining Data of Different Types 2.5.3 Example: Symbolic Algebra
Modularity, Objects, and State
3.1 Assignment and Local State 3.2 The Environment Model of Evaluation 3.3 Modeling with Mutable Data 3.4 Concurrency: Time Is of the Essence 3.5 Streams
Assignment and Local State
3.1.1 Local State Variables 3.1.2 The Benefits of Introducing Assignment 3.1.3 The Costs of Introducing Assignment
The Environment Model of Evaluation
3.2.1 The Rules for Evaluation 3.2.2 Applying Simple Procedures 3.2.3 Frames as the Repository of Local State 3.2.4 Internal Definitions
Modeling with Mutable Data
3.3.1 Mutable List Structure 3.3.2 Representing Queues 3.3.3 Representing Tables 3.3.4 A Simulator for Digital Circuits 3.3.5 Propagation of Constraints
Concurrency: Time Is of the Essence
3.4.1 The Nature of Time in Concurrent Systems 3.4.2 Mechanisms for Controlling Concurrency
Streams
3.5.1 Streams Are Delayed Lists 3.5.2 Infinite Streams 3.5.3 Exploiting the Stream Paradigm 3.5.4 Streams and Delayed Evaluation 3.5.5 Modularity of Functional Programs and Modularity of Objects
Metalinguistic Abstraction
4.1 The Metacircular Evaluator 4.2 Variations on a Scheme -- Lazy Evaluation 4.3 Variations on a Scheme -- Nondeterministic Computing 4.4 Logic Programming
The Metacircular Evaluator
4.1.1 The Core of the Evaluator 4.1.2 Representing Expressions 4.1.3 Evaluator Data Structures 4.1.4 Running the Evaluator as a Program 4.1.5 Data as Programs 4.1.6 Internal Definitions 4.1.7 Separating Syntactic Analysis from Execution
Variations on a Scheme -- Lazy Evaluation
4.2.1 Normal Order and Applicative Order 4.2.2 An Interpreter with Lazy Evaluation 4.2.3 Streams as Lazy Lists
Variations on a Scheme -- Nondeterministic Computing
4.3.1 Amb and Search 4.3.2 Examples of Nondeterministic Programs 4.3.3 Implementing the AmbEvaluator
Logic Programming
4.4.1 Deductive Information Retrieval 4.4.2 How the Query System Works 4.4.3 Is Logic Programming Mathematical Logic? 4.4.4 Implementing the Query System
Implementing the Query System
4.4.4.1 The Driver Loop and Instantiation 4.4.4.2 The Evaluator 4.4.4.3 Finding Assertions by Pattern Matching 4.4.4.4 Rules and Unification 4.4.4.5 Maintaining the Data Base 4.4.4.6 Stream Operations 4.4.4.7 Query Syntax Procedures 4.4.4.8 Frames and Bindings
Computing with Register Machines
5.1 Designing Register Machines 5.2 A Register-Machine Simulator 5.3 Storage Allocation and Garbage Collection 5.4 The Explicit-Control Evaluator 5.5 Compilation
Designing Register Machines
5.1.1 A Language for Describing Register Machines 5.1.2 Abstraction in Machine Design 5.1.3 Subroutines 5.1.4 Using a Stack to Implement Recursion 5.1.5 Instruction Summary
A Register-Machine Simulator
5.2.1 The Machine Model 5.2.2 The Assembler 5.2.3 Generating Execution Procedures for Instructions 5.2.4 Monitoring Machine Performance
Storage Allocation and Garbage Collection
5.3.1 Memory as Vectors 5.3.2 Maintaining the Illusion of Infinite Memory
Registers and operations
5.4.1 The Core of the Explicit-Control Evaluator 5.4.2 Sequence Evaluation and Tail Recursion 5.4.3 Conditionals, Assignments, and Definitions 5.4.4 Running the Evaluator
An overview of the compiler
5.5.1 Structure of the Compiler 5.5.2 Compiling Expressions 5.5.3 Compiling Combinations 5.5.4 Combining Instruction Sequences 5.5.5 An Example of Compiled Code 5.5.6 Lexical Addressing 5.5.7 Interfacing Compiled Code to the Evaluator