Clifford A. Shaffer教授于美国马里兰大学获得计算机科学博士学位，在弗吉尼亚理工大学计算机科学系任教超过30年，具有丰富的教学经验，并参与遗传学、生物信息学和计算生物学等交叉项目。著有多本数据结构和算法分析的教材。
Clifford A. Shaffer教授于美国马里兰大学获得计算机科学博士学位，在弗吉尼亚理工大学计算机科学系任教超过30年，具有丰富的教学经验，并参与遗传学、生物信息学和计算生物学等交叉项目。著有多本数据结构和算法分析的教材。
目錄：
Contents
Part I Preliminaries 预备知识
Chapter 1 Data Structures and Algorithms 数据结构和算法 3
1.1 A Philosophy of Data Structures 数据结构的原则 4
1.1.1 The Need for Data Structures 学习数据结构的必要性 4
1.1.2 Costs and Benefits 代价与效益 6
1.2 Abstract Data Types and Data Structures 抽象数据类型和数据结构 8
1.3 Design Patterns 设计模式 12
1.3.1 Flyweight 享元模式 13
1.3.2 Visitor 访问者模式 13
1.3.3 Composite 组合模式 14
1.3.4 Strategy 策略模式 15
1.4 Problems, Algorithms, and Programs 问题、算法和程序 16
1.5 Further Reading 深入学习导读 18
1.6 Exercises 习题 20
Chapter 2 Mathematical Preliminaries 数学预备知识 25
2.1 Sets and Relations 集合和关系 25
2.2 Miscellaneous Notation 常用数学术语 29
2.3 Logarithms 对数 31
2.4 Summations and Recurrences 级数求和与递归 32
2.5 Recursion 递归 36
2.6 Mathematical Proof Techniques 数学证明方法 38
2.6.1 Direct Proof 直接证明法 39
2.6.2 Proof by Contradiction 反证法 39
2.6.3 Proof by Mathematical Induction 数学归纳法 40
2.7 Estimation 估计 46
2.8 Further Reading 深入学习导读 47
2.9 Exercises 习题 48
Chapter 3 Algorithm Analysis 算法分析 55
3.1 Introduction 概述 55
3.2 Best, Worst, and Average Cases 最佳、最差和平均情况 61
3.3 A Faster Computer, or a Faster Algorithm 换一台更快的计算机，还是换一种更快的算法 62
3.4 Asymptotic Analysis 渐近分析 65
3.4.1 Upper Bounds 上限 65
3.4.2 Lower Bounds 下限 67
3.4.3 Notation 表示法 68
3.4.4 Simplifying Rules 化简法则 69
3.4.5 Classifying Functions 函数分类 70
3.5 Calculating the Running Time for a Program 程序运行时间的计算 71
3.6 Analyzing Problems 问题的分析 76
3.7 Common Misunderstandings 容易混淆的概念 77
3.8 Multiple Parameters 多参数问题 79
3.9 Space Bounds 空间代价 80
3.10 Speeding Up Your Programs 加速你的程序 82
3.11 Empirical Analysis 实证分析 85
3.12 Further Reading 深入学习导读 86
3.13 Exercises 习题 86
3.14 Projects 项目设计 90
Part II Fundamental Data Structures 基本数据结构
Chapter 4 Lists, Stacks, and Queues 线性表、栈和队列 95
4.1 Lists 线性表 96
4.1.1 Array-Based List Implementation 顺序表的实现 100
4.1.2 Linked Lists 链表 103
4.1.3 Comparison of List Implementations 线性表实现方法的比较 112
4.1.4 Element Implementations 元素的表示 114
4.1.5 Doubly Linked Lists 双链表 115
4.2 Stacks 栈 120
4.2.1 Array-Based Stacks 顺序栈 121
4.2.2 Linked Stacks 链式栈 123
4.2.3 Comparison of Array-Based and Linked Stacks 顺序栈与链式栈的比较 123
4.2.4 Implementing Recursion 递归的实现 125
4.3 Queues 队列 127
4.3.1 Array-Based Queues 顺序队列 128
4.3.2 Linked Queues 链式队列 133
4.3.3 Comparison of Array-Based and Linked Queues 顺序队列与链式队列的比较 133
4.4 Dictionaries 字典 133
4.5 Further Reading 深入学习导读 145
4.6 Exercises 习题 145
4.7 Projects 项目设计 148
Chapter 5 Binary Trees 二叉树 151
5.1 Definitions and Properties 定义及主要特性 151
5.1.1 The Full Binary Tree Theorem 满二叉树定理 153
5.1.2 A Binary Tree Node ADT 二叉树的抽象数据类型 155
5.2 Binary Tree Traversals 遍历二叉树 155
5.3 Binary Tree Node Implementations 二叉树的实现 160
5.3.1 Pointer-Based Node Implementations 使用指针实现二叉树 160
5.3.2 Space Requirements 空间代价 166
5.3.3 Array Implementation for Complete Binary Trees 使用数组实现完全二叉树 168
5.4 Binary Search Trees 二叉检索树 168
5.5 Heaps and Priority Queues 堆与优先队列 178
5.6 Huffman Coding Trees Huffman编码树 185
5.6.1 Building Huffman Coding Trees 建立Huffman编码树 186
5.6.2 Assigning and Using Huffman Codes Huffman编码及其用法 192
5.6.3 Search in Huffman Trees 在Huffman树中搜索 195
5.7 Further Reading 深入学习导读 196
5.8 Exercises 习题 196
5.9 Projects 项目设计 200
Chapter 6 Non-Binary Trees 树 203
6.1 General Tree Definitions and Terminology 树的定义与术语 203
6.1.1 An ADT for General Tree Nodes 树结点的ADT 204
6.1.2 General Tree Traversals 树的遍历 205
6.2 The Parent Pointer Implementation 父指针表示法 207
6.3 General Tree Implementations 树的实现 213
6.3.1 List of Children 子结点表表示法 214
6.3.2 The Left-ChildRight-Sibling Implementation 左子结点右兄弟结点表示法 215
6.3.3 Dynamic Node Implementations 动态结点表示法 215
6.3.4 Dynamic Left-ChildRight-Sibling Implementation 动态左子结点右兄弟结点表示法 218
6.4 K-ary Trees K叉树 218
6.5 Sequential Tree Implementations 树的顺序表示法 219
6.6 Further Reading 深入学习导读 223
6.7 Exercises 习题 223
6.8 Projects 项目设计 226
Part III Sorting and Searching 排序与检索
Chapter 7 Internal Sorting 内排序 231
7.1 Sorting Terminology and Notation 排序术语 232
7.2 Three n2 Sorting Algorithms 三种代价为n2的排序算法 233
7.2.1 Insertion Sort 插入排序 233
7.2.2 Bubble Sort 冒泡排序 235
7.2.3 Selection Sort 选择排序 237
7.2.4 The Cost of Exchange Sorting 交换排序算法的时间代价 238
7.3 Shellsort Shell排序 239
7.4 Mergesort 归并排序 241
7.5 Quicksort 快速排序 244
7.6 Heapsort 堆排序 251
7.7 Binsort and Radix Sort 分配排序和基数排序 252
7.8 An Empirical Comparison of Sorting Algorithms 对各种排序算法的实验比较 259
7.9 Lower Bounds for Sorting 排序问题的下限 261
7.10 Further Reading 深入学习导读 265
7.11 Exercises 习题 265
7.12 Projects 项目设计 269
Chapter 8 File Processing and External Sorting 文件管理和外排序 273
8.1 Primary versus Secondary Storage 主存储器和辅助存储器 273
8.2 Disk Drives 磁盘 276
8.2.1 Disk Drive Architecture 磁盘结构 276
8.2.2 Disk Access Costs 磁盘访问代价 280
8.3 Buffers and Buffer Pools 缓冲区和缓冲池 282
8.4 The Programmers View of Files 程序员的文件视图 290
8.5 External Sorting 外排序 291
8.5.1 Simple Approaches to External Sorting 外排序的简单方法 294
8.5.2 Replacement Selection 置换选择排序 296
8.5.3 Multiway Merging 多路归并 300
8.6 Further Reading 深入学习导读 303
8.7 Exercises 习题 304
8.8 Projects 项目设计 307
Chapter 9 Searching 检索 311
9.1 Searching Unsorted and Sorted Arrays 检索未排序和已排序的数组 312
9.2 Self-Organizing Lists 自组织线性表 317
9.3 Bit Vectors for Representing Sets 集合检索 323
9.4 Hashing 散列方法 324
9.4.1 Hash Functions 散列函数 325
9.4.2 Open Hashing 开散列方法 330
9.4.3 Closed Hashing 闭散列方法 331
9.4.4 Analysis of Closed Hashing 闭散列方法分析 339
9.4.5 Deletion 删除 344
9.5 Further Reading 深入学习导读 345
9.6 Exercises 习题 345
9.7 Projects 项目设计 348
Chapter 10 Indexing 索引技术 351
10.1 Linear Indexing 线性索引 353
10.2 ISAM 索引顺序访问方法 356
10.3 Tree-based Indexing 基于树的索引 358
10.4 2-3 Trees 2-3树 360
10.5 B-Trees B树 364
10.5.1 B -Trees B 树 368
10.5.2 B-Tree Analysis B树分析 374
10.6 Further Reading 深入学习导读 375
10.7 Exercises 习题 375
10.8 Projects 项目设计 377
Part IV Advanced Data Structures 高级数据结构
Chapter 11 Graphs 图 381
11.1 Terminology and Representations 术语和表示法 382
11.2 Graph Implementations 图的实现 386
11.3 Graph Traversals 图的遍历 390
11.3.1 Depth-First Search 深度优先搜索 393
11.3.2 Breadth-First Search 广度优先搜索 394
11.3.3 Topological Sort 拓扑排序 394
11.4 Shortest-Paths Problems 最短路径问题 399
11.4.1 Single-Source Shortest Paths 单源最短路径 400
11.5 Minimum-Cost Spanning Trees 最小支撑树 402
11.5.1 Prims Algorithm
Preface
We study data structures so that we can learn to write more efficient programs. But why must programs be efficient when new computers are faster every year? The reason is that our ambitions grow with our capabilities. Instead of rendering efficiency needs obsolete, the modern revolution in computing power and storage capability merely raises the efficiency stakes as we attempt more complex tasks.
The quest for program efficiency need not and should not conflict with sound design and clear coding. Creating efficient programs has little to do with programming tricks but rather is based on good organization of information and good algorithms. A programmer who has not mastered the basic principles of clear design is not likely to write efficient programs. Conversely, concerns related to development costs and maintainability should not be used as an excuse to justify inefficient performance. Generality in design can and should be achieved without sacrificing performance, but this can only be done if the designer understands how to measure performance and does so as an integral part of the design and implementation process. Most computer science curricula recognize that good programming skills begin with a strong emphasis on fundamental software engineering principles. Then, once a programmer has learned the principles of clear program design and implementation, the next step is to study the effects of data organization and algorithms on program efficiency.
Approach： This book describes many techniques for representing data. These techniques are presented within the context of the following principles：
1. Each data structure and each algorithm has costs and benefits. Practitioners need a thorough understanding of how to assess costs and benefits to be able to adapt to new design challenges. This requires an understanding of the principles of algorithm analysis, and also an appreciation f
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