SICP
Title Page
Copyright Page
1.
Introduction
1.1.
Dedication
1.2.
Foreword
1.3.
Preface to the LFE Edition
1.3.1.
The Hidden Origins of Lisp
1.3.1.1.
Giuseppe Peano
1.3.1.2.
Bertrand Russell
1.3.1.3.
Alonzo Church
1.3.1.4.
John McCarthy
1.3.2.
A Recap of Erlang's Genesis
1.3.3.
The Inspiration for LFE
1.3.4.
The Place of Lisp in the 21st Century
1.3.5.
Notes on Changes from the Original
1.3.6.
Obtaining the Book and Related Code
1.4.
Preface to the Second Edition
1.5.
Preface to the First Edition
1.6.
Acknowledgments
2.
Building Abstractions with Functions
2.1.
Programming in Lisp
2.2.
The Elements of Programming
2.2.1.
Expressions
2.2.2.
Naming and the Environment
2.2.3.
Evaluating Combinations
2.2.4.
Compound Functions
2.2.5.
The Substitution Model for Function Application
2.2.6.
Conditional Expressions and Predicates
2.2.7.
Exercises
2.2.8.
Example: Square Roots by Newton's Method
2.2.9.
Exercises
2.2.10.
Functions as Black-Box Abstractions
2.3.
Functions and the Processes They Generate
2.3.1.
Linear Recursion and Iteration
2.3.2.
Exercises
2.3.3.
Tree Recursion
2.3.4.
Exercises
2.3.5.
Orders of Growth
2.3.6.
Exercises
2.3.7.
Exponentiation
2.3.8.
Exercises
2.3.9.
Greatest Common Divisors
2.3.10.
Exercises
2.3.11.
Example: Testing for Primality
2.3.12.
Exercises
2.4.
Formulating Abstractions with Higher-Order Functions
2.4.1.
Functions as Arguments
2.4.2.
Exercises
2.4.3.
Constructing Functions Using Lambda
2.4.4.
Exercises
2.4.5.
Functions as General Methods
2.4.6.
Exercises
2.4.7.
Functions as Returned Values
2.4.8.
Exercises
3.
Building Abstractions with Data
3.1.
Introduction to Data Abstraction
3.1.1.
Example: Arithmetic Operations for Rational Numbers
3.1.2.
Exercises
3.1.3.
Abstraction Barriers
3.1.4.
Exercises
3.1.5.
What Is Meant by Data?
3.1.6.
Exercises
3.1.7.
Extended Exercise: Interval Arithmetic
3.1.8.
Exercises
3.2.
Hierarchical Data and the Closure Property
3.2.1.
Representing Sequences
3.2.1.1.
List operations
3.2.1.2.
Exercises
3.2.1.3.
Mapping over lists
3.2.1.4.
Exercises
3.2.2.
Hierarchical Structures
3.2.2.1.
Exercises
3.2.2.2.
Mapping over trees
3.2.2.3.
Exercises
3.2.3.
Sequences as Conventional Interfaces
3.2.3.1.
Sequence operations
3.2.3.2.
Exercises
3.2.3.3.
Nested mappings
3.2.3.4.
Exercises
3.2.4.
Example: A Picture Language
3.2.4.1.
The picture language
3.2.4.2.
Exercises
3.2.4.3.
Higher order operations
3.2.4.4.
Frames
3.2.4.5.
Exercises
3.2.4.6.
Painters
3.2.4.7.
Exercises
3.2.4.8.
Transforming and combining painters
3.2.4.9.
Exercises
3.2.4.10.
Levels of language for robust design
3.2.4.11.
Exercises
3.3.
Symbolic Data
3.3.1.
Quotation
3.3.2.
Example: Symbolic Differentiation
3.3.3.
Example: Representing Sets
3.3.4.
Example: Huffman Encoding Trees
3.4.
Multiple Representations for Abstract Data
3.4.1.
Representations for Complex Numbers
3.4.2.
Tagged data
3.4.3.
Data-Directed Programming and Additivity
3.5.
Systems with Generic Operations
3.5.1.
Generic Arithmetic Operations
3.5.2.
Combining Data of Different Types
3.5.3.
Example: Symbolic Algebra
4.
Modularity, Objects, and State
4.1.
Assignment and Local State
4.1.1.
Local State Variables
4.1.2.
The Benefits of Introducing Assignment
4.1.3.
The Costs of Introducing Assignment
4.2.
The Environment Model of Evaluation
4.2.1.
The Rules for Evaluation
4.2.2.
Applying Simple Functions
4.2.3.
Frames as the Repository of Local State
4.2.4.
Internal Definitions
4.3.
Modeling with Mutable Data
4.3.1.
Mutable List Structure
4.3.2.
Representing Queues
4.3.3.
Representing Tables
4.3.4.
A Simulator for Digital Circuits
4.3.5.
Propagation of Constraints
4.4.
Concurrency: Time Is of the Essence
4.4.1.
The Nature of Time in Concurrent Systems
4.4.2.
Mechanisms for Controlling Concurrency
4.5.
Streams
4.5.1.
Streams Are Delayed Lists
4.5.2.
Infinite Streams
4.5.3.
Exploiting the Stream Paradigm
4.5.4.
Streams and Delayed Evaluation
4.5.5.
Modularity of Functional Programs and Modularity of Objects
5.
Metalinguistic Abstraction
5.1.
The Metacircular Evaluator
5.1.1.
The Core of the Evaluator
5.1.2.
Representing Expressions
5.1.3.
Evaluator Data Structures
5.1.4.
Running the Evaluator as a Program
5.1.5.
Data as Programs
5.1.6.
Internal Definitions
5.1.7.
Separating Syntactic Analysis from Execution
5.2.
Variations on a Scheme -- Lazy Evaluation
5.2.1.
Normal Order and Applicative Order
5.2.2.
An Interpreter with Lazy Evaluation
5.2.3.
Streams as Lazy Lists
5.3.
Variations on a Scheme -- Nondeterministic Computing
5.3.1.
Amb and Search
5.3.2.
Examples of Nondeterministic Programs
5.3.3.
Implementing the Amb Evaluator
5.4.
Logic Programming
5.4.1.
Deductive Information Retrieval
5.4.2.
How the Query System Works
5.4.3.
Is Logic Programming Mathematical Logic?
5.4.4.
Implementing the Query System
6.
Computing with Register Machines
6.1.
Designing Register Machines
6.1.1.
A Language for Describing Register Machines
6.1.2.
Abstraction in Machine Design
6.1.3.
Subroutines
6.1.4.
Using a Stack to Implement Recursion
6.1.5.
Instruction Summary
6.2.
A Register-Machine Simulator
6.2.1.
The Machine Model
6.2.2.
The Assembler
6.2.3.
Generating Execution Functions for Instructions
6.2.4.
Monitoring Machine Performance
6.3.
Storage Allocation and Garbage Collection
6.3.1.
Memory as Vectors
6.3.2.
Maintaining the Illusion of Infinite Memory
6.4.
The Explicit-Control Evaluator
6.4.1.
The Core of the Explicit-Control Evaluator
6.4.2.
Sequence Evaluation and Tail Recursion
6.4.3.
Conditionals, Assignments, and Definitions
6.4.4.
Running the Evaluator
6.5.
Compilation
6.5.1.
Structure of the Compiler
6.5.2.
Compiling Expressions
6.5.3.
Compiling Combinations
6.5.4.
Combining Instruction Sequences
6.5.5.
An Example of Compiled Code
6.5.6.
Lexical Addressing
6.5.7.
Interfacing Compiled Code to the Evaluator
7.
References
8.
List of Exercises
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