（已有18条评价）基本信息
- 原书名：Algorithms in C, Parts 1-4: Fundamentals, Data Structures, Sorting, Searching (3rd Edition)
- 原出版社： Addison-Wesley
编辑推荐
对于在数学分析方面不算熟练且需要留意理论算法的普通程序员来说，本书是一本可读性很强的优秀读本。他们应该会从中获益良多。
——Steve Summit，《C Programming FAQs》的作者
Sedgewick有一种真正的天赋，可以用易于理解的方式来解释概念。书中采用了一些易懂的实战程序，其篇幅仅有一页左右，这更是锦上添花。而书中大量采用的图、程序、表格也会极大帮助读者的学习和理解，这使本书更显得与众不同。
——William A. Ward，南亚拉巴马大学
内容简介
书籍
计算机书籍
本书是Sedgewick彻底修订和重写的C算法系列的第一本。全书分为四部分，共16章。第一部分“基础知识” (第1-2章) 介绍基本算法分析原理。第二部分“数据结构” (第3-5章) 讲解算法分析中必须掌握的数据结构知识，主要包括基本数据结构、抽象数据结构、递归和树。第三部分“排序” (第6-11章) 按章节顺序分别讨论基本排序方法 (如选择排序、插入排序、冒泡排序、希尔排序等) 、快速排序方法、归并和归并排序方法、优先队列与堆排序方法、基数排序方法以及特殊目的排序方法，并比较了各种排序方法的性能特征。第四部分“搜索” (第12-16章) 在进一步讲解符号表、树等抽象数据类型的基础上，重点讨论哈希方法、基数搜索以及外部搜索方法。.
书中提供了用C语言描述的完整算法源程序，并且配有丰富的插图和练习。作者用简洁的实现将理论和实践成功地结合了起来，这些实现均可在真实应用上测试，使得本书自问世以来备受程序员的欢迎。...
计算机书籍
本书是Sedgewick彻底修订和重写的C算法系列的第一本。全书分为四部分，共16章。第一部分“基础知识” (第1-2章) 介绍基本算法分析原理。第二部分“数据结构” (第3-5章) 讲解算法分析中必须掌握的数据结构知识，主要包括基本数据结构、抽象数据结构、递归和树。第三部分“排序” (第6-11章) 按章节顺序分别讨论基本排序方法 (如选择排序、插入排序、冒泡排序、希尔排序等) 、快速排序方法、归并和归并排序方法、优先队列与堆排序方法、基数排序方法以及特殊目的排序方法，并比较了各种排序方法的性能特征。第四部分“搜索” (第12-16章) 在进一步讲解符号表、树等抽象数据类型的基础上，重点讨论哈希方法、基数搜索以及外部搜索方法。.
书中提供了用C语言描述的完整算法源程序，并且配有丰富的插图和练习。作者用简洁的实现将理论和实践成功地结合了起来，这些实现均可在真实应用上测试，使得本书自问世以来备受程序员的欢迎。...
作译者
Robert Sedgewick 拥有斯坦福大学博士学位(导师为Donald E.Knuth)，普林斯顿大学计算机科学系教授，Adobe Systems公司董事，曾是Xerox PARC的研究人员，还曾就职子美国国防部防御分析研究所以及INRIA。除本书外，他还与Philippe Flajolet合著了《算法分析导论》一书。...
目录
Chapter1.Introduction.
1.1Algorithms
1.2ASampleProblem--Connectivity
1.3Union-FindAlgorithms
1.4Perspective
1.5SummaryofTopics
Chapter2.PrinciplesofAlgorithmAnalysis
2.1ImplementationandEmpiricalAnalysis
2.2AnalysisofAlgorithms
2.3GrowthofFunctions
2.4Big-Ohnotation
2.5BasicRecurrences
2.6ExamplesofAlgorithmAnalysis
2.7Guarantees,Predictions,andLimitations
Chapter3.ElementaryDataStructures
3.1BuildingBlocks
3.2Arrays
3.3LinkedLists
3.4ElementaryListProcessing
3.5MemoryAllocationforLists
1.1Algorithms
1.2ASampleProblem--Connectivity
1.3Union-FindAlgorithms
1.4Perspective
1.5SummaryofTopics
Chapter2.PrinciplesofAlgorithmAnalysis
2.1ImplementationandEmpiricalAnalysis
2.2AnalysisofAlgorithms
2.3GrowthofFunctions
2.4Big-Ohnotation
2.5BasicRecurrences
2.6ExamplesofAlgorithmAnalysis
2.7Guarantees,Predictions,andLimitations
Chapter3.ElementaryDataStructures
3.1BuildingBlocks
3.2Arrays
3.3LinkedLists
3.4ElementaryListProcessing
3.5MemoryAllocationforLists
前言
THIS BOOK IS intended to survey the most important computer algorithms in use today, and to teach fundamental techniques to the growing number of people in need of knowing them. It can be used as a textbook for a second, third, or fourth course in computer science, after students have acquired basic programming skills and fa miliarity with computer systems, but before they have taken specialized courses in advanced areas of computer science or computer applications. The book also may be useful for self-study or as a reference for people engaged in the development of computer systems or applications programs, since it contains implementations of useful algorithms and detailed information on these algorithms' performance characteristics. The broad perspective taken makes the book an appropriate introduction to the field. .
I have completely rewritten the text for this new edition, and I have added more than a thousand new exercises, more than a hundred new figures, and dozens of new programs. I have also added detailed commentary on all the figures and programs. This new material provides both coverage of new topics and fuller explanations of many of the classic algorithms. A new emphasis on abstract data types throughout the book makes the programs more broadly useful and relevant in modern object-oriented programming environments. People who have read old editions of the book will find a wealth of new information throughout; all readers will find a wealth of pedagogical material that provides effective access to essential concepts.
Due to the large amount of new material, we have split the new edition into two volumes (each about the size of the old edition) of which this is the first. This volume covers fundamental concepts, data structures, sorting algorithms, and searching algorithms; the second volume covers advanced algorithms and applications, building on the basic abstractions and methods developed here. Nearly all the material on fundamentals and data structures in this edition is new.
This book is not just for programmers and computer-science students. Nearly everyone who uses a computer wants it to run faster or to solve larger problems. The algorithms in this book represent a body of knowledge developed over the last 50 years that has become indispensible in the efficient use of the computer, for a broad variety of applications. From N-body simulation problems in physics to genetic-sequencing problems in molecular biology, the basic methods described here have become essential in scientific research; and from database systems to Internet search engines, they have become essential parts 0f modern software systems. As the scope of computer applications becomes more widespread, so grows the impact of many of the basic methods covered here. The goal of this book is to serve as a resource for students and professionals interested in knowing and making intelligent use of these fundamental algorithms as basic tools for whatever computer application they might undertake.
Scope
The book contains 16 chapters grouped into four major parts: fundamentals, data structures, sorting, and searching. The descriptions here are intended to give readers an understanding of the basic properties of as broad a range of fundamental algorithms as possible. Ingenious methods ranging from binomial queues to patricia tries are described, all related to basic paradigms at the heart of computer science. The second volume consists of four additional parts that cover strings, geometry, graphs, and advanced topics. My primary goal in developing these books has been to bring together the fundamental methods from these diverse areas, to provide access to the best methods known for solving problems by computer.
You will most appreciate the material in this book if you have had one or two previous courses in computer science or have had equivalent programming experience: one course in programming in a high-level language such as C, Java, or C++, and perhaps another course that teaches fundamental concepts of programming systems. This book is thus intended for anyone conversant with a modern programming language and with the basic features of modern computer systems.References that might help to fill in gaps in your background are suggested in the text.
Most of the mathematical material supporting the analytic results is self-contained (or is labeled as beyond the scope of this book), so little specific preparation in mathematics is required for the bulk of the book, although mathematical maturity is definitely helpful.
Use in the Curriculum
There is a great deal of flexibility in how the material here can be taught, depending on the taste of the instructor and the preparation of the students. The algorithms described here have found widespread use for years, and represent an essential body of knowledge for both the practicing programmer and the computer-science student. There is sufficient coverage of basic material for the book to be used for a course on data structures, and there is sufficient detai! and coverage of advanced material for the book to be used for a course on algorithms.Some instructors may wish to emphasize implementations and practical concerns; others may wish to emphasize analysis and theoretical concepts.
A complete set of slide masters for use in lectures, sample programming assignments, interactive exercises for students, and other course materials may be found via the book's home page.
An elementary course on data structures and algorithms might emphasize the basic data structures in Part '2 and their use in the implementations in Parts 3 and 4. A course on design and analysis of algorithms might emphasize the fundamental material in Part 1 and Chapter 5, then study the ways in which the algorithms in Parts 3 and 4 achieve good asymptotic performance. A course on software engineering might omit the mathematical and advanced algorithmic material, and emphasize how to integrate the implementations given here into large programs or systems. A course on algorithms might take a survey approach and introduce concepts from all these areas.
Earlier editions of this book have been used in recent years at scores of colleges and universities around the world as a text for the second or third course in computer science and as supplemental reading for other courses. At Princeton, our experience has been that the breadth of coverage of material in this book provides our majors with an introduction to computer science that can be expanded upon in later courses on analysis of algorithms, systems programming and
theoretical computer science, while providing the growing group of students from other disciplines with a large set of techniques that these people can immediately put to good use.
The exercises--most of which are new to this edition--fall into several types. Some are intended to test understanding of material in the text, and simply ask readers to work through an example or to apply concepts described in the text. Others involve implementing and putting together the algorithms, or running empirical studies to compare variants of the algorithms and to learn their properties. Still others are a repository for important information at a level of detail that is not appropriate for the text. Reading and thinking about the exercises will pay dividends for every reader. ..
Algorithms of Practical Use
Anyone wanting to use a computer more effectively can use this book for reference or for self-study. People with programming experience can find information on specific topics throughout the book. To a large extent, you can read the individual chapters in the book independently of the others, although, in some cases, algorithms in one chapter make use of methods from a previous chapter.
The orientation of the book is to study algorithms likely to be of practical use. The book provides information about the tools of the trade to the point that readers can confidently implement, debug, and put to work algorithms to solve a problem or to provide functionality in an application. Full implementations of the methods discussed are included, as are descriptions of the operations of these programs on a consistent set of examples. Because we work with real code, rather than write pseudo-code, the programs can be put to practical use quickly. Program listings are available from the book's home page.
Indeed, one practical application of the algorithms has been to produce the hundreds of figures throughout the book. Many algorithms are brought to light on an intuitive level through the visual dimension provided by these figures.
Characteristics of the algorithms and of the situations in which they might he,useful are discussed in detail. Although not emphasized, connections to the analysis of algorithms and theoretical computer science are developed 4n context. When appropriate, erapirical and analytic results are presented to illustrate why certain algorithms are preferred. When interesting, the relationship of the practical algorithms being discussed to purely theoretical results is described. Specific information on performance characteristics of algorithms and implementations is synthesized, encapsulated, and discussed throughout the book.
I have completely rewritten the text for this new edition, and I have added more than a thousand new exercises, more than a hundred new figures, and dozens of new programs. I have also added detailed commentary on all the figures and programs. This new material provides both coverage of new topics and fuller explanations of many of the classic algorithms. A new emphasis on abstract data types throughout the book makes the programs more broadly useful and relevant in modern object-oriented programming environments. People who have read old editions of the book will find a wealth of new information throughout; all readers will find a wealth of pedagogical material that provides effective access to essential concepts.
Due to the large amount of new material, we have split the new edition into two volumes (each about the size of the old edition) of which this is the first. This volume covers fundamental concepts, data structures, sorting algorithms, and searching algorithms; the second volume covers advanced algorithms and applications, building on the basic abstractions and methods developed here. Nearly all the material on fundamentals and data structures in this edition is new.
This book is not just for programmers and computer-science students. Nearly everyone who uses a computer wants it to run faster or to solve larger problems. The algorithms in this book represent a body of knowledge developed over the last 50 years that has become indispensible in the efficient use of the computer, for a broad variety of applications. From N-body simulation problems in physics to genetic-sequencing problems in molecular biology, the basic methods described here have become essential in scientific research; and from database systems to Internet search engines, they have become essential parts 0f modern software systems. As the scope of computer applications becomes more widespread, so grows the impact of many of the basic methods covered here. The goal of this book is to serve as a resource for students and professionals interested in knowing and making intelligent use of these fundamental algorithms as basic tools for whatever computer application they might undertake.
Scope
The book contains 16 chapters grouped into four major parts: fundamentals, data structures, sorting, and searching. The descriptions here are intended to give readers an understanding of the basic properties of as broad a range of fundamental algorithms as possible. Ingenious methods ranging from binomial queues to patricia tries are described, all related to basic paradigms at the heart of computer science. The second volume consists of four additional parts that cover strings, geometry, graphs, and advanced topics. My primary goal in developing these books has been to bring together the fundamental methods from these diverse areas, to provide access to the best methods known for solving problems by computer.
You will most appreciate the material in this book if you have had one or two previous courses in computer science or have had equivalent programming experience: one course in programming in a high-level language such as C, Java, or C++, and perhaps another course that teaches fundamental concepts of programming systems. This book is thus intended for anyone conversant with a modern programming language and with the basic features of modern computer systems.References that might help to fill in gaps in your background are suggested in the text.
Most of the mathematical material supporting the analytic results is self-contained (or is labeled as beyond the scope of this book), so little specific preparation in mathematics is required for the bulk of the book, although mathematical maturity is definitely helpful.
Use in the Curriculum
There is a great deal of flexibility in how the material here can be taught, depending on the taste of the instructor and the preparation of the students. The algorithms described here have found widespread use for years, and represent an essential body of knowledge for both the practicing programmer and the computer-science student. There is sufficient coverage of basic material for the book to be used for a course on data structures, and there is sufficient detai! and coverage of advanced material for the book to be used for a course on algorithms.Some instructors may wish to emphasize implementations and practical concerns; others may wish to emphasize analysis and theoretical concepts.
A complete set of slide masters for use in lectures, sample programming assignments, interactive exercises for students, and other course materials may be found via the book's home page.
An elementary course on data structures and algorithms might emphasize the basic data structures in Part '2 and their use in the implementations in Parts 3 and 4. A course on design and analysis of algorithms might emphasize the fundamental material in Part 1 and Chapter 5, then study the ways in which the algorithms in Parts 3 and 4 achieve good asymptotic performance. A course on software engineering might omit the mathematical and advanced algorithmic material, and emphasize how to integrate the implementations given here into large programs or systems. A course on algorithms might take a survey approach and introduce concepts from all these areas.
Earlier editions of this book have been used in recent years at scores of colleges and universities around the world as a text for the second or third course in computer science and as supplemental reading for other courses. At Princeton, our experience has been that the breadth of coverage of material in this book provides our majors with an introduction to computer science that can be expanded upon in later courses on analysis of algorithms, systems programming and
theoretical computer science, while providing the growing group of students from other disciplines with a large set of techniques that these people can immediately put to good use.
The exercises--most of which are new to this edition--fall into several types. Some are intended to test understanding of material in the text, and simply ask readers to work through an example or to apply concepts described in the text. Others involve implementing and putting together the algorithms, or running empirical studies to compare variants of the algorithms and to learn their properties. Still others are a repository for important information at a level of detail that is not appropriate for the text. Reading and thinking about the exercises will pay dividends for every reader. ..
Algorithms of Practical Use
Anyone wanting to use a computer more effectively can use this book for reference or for self-study. People with programming experience can find information on specific topics throughout the book. To a large extent, you can read the individual chapters in the book independently of the others, although, in some cases, algorithms in one chapter make use of methods from a previous chapter.
The orientation of the book is to study algorithms likely to be of practical use. The book provides information about the tools of the trade to the point that readers can confidently implement, debug, and put to work algorithms to solve a problem or to provide functionality in an application. Full implementations of the methods discussed are included, as are descriptions of the operations of these programs on a consistent set of examples. Because we work with real code, rather than write pseudo-code, the programs can be put to practical use quickly. Program listings are available from the book's home page.
Indeed, one practical application of the algorithms has been to produce the hundreds of figures throughout the book. Many algorithms are brought to light on an intuitive level through the visual dimension provided by these figures.
Characteristics of the algorithms and of the situations in which they might he,useful are discussed in detail. Although not emphasized, connections to the analysis of algorithms and theoretical computer science are developed 4n context. When appropriate, erapirical and analytic results are presented to illustrate why certain algorithms are preferred. When interesting, the relationship of the practical algorithms being discussed to purely theoretical results is described. Specific information on performance characteristics of algorithms and implementations is synthesized, encapsulated, and discussed throughout the book.
