《國外計算機科學教材系列：數(shù)據(jù)結(jié)構(gòu)與算法分析（C++版）（第3版）（英文版）》采用程序員最愛用的面向?qū)ο驝++語言來描述數(shù)據(jù)結(jié)構(gòu)和算法，并把數(shù)據(jù)結(jié)構(gòu)原理和算法分析技術(shù)有機地結(jié)合在一起，系統(tǒng)介紹了各種類型的數(shù)據(jù)結(jié)構(gòu)和排序、檢索的各種方法。作者非常注意對每一種數(shù)據(jù)結(jié)構(gòu)的不同存儲方法及有關(guān)算法進行分析比較。書中還引入了一些比較高級的數(shù)據(jù)結(jié)構(gòu)與先進的算法分析技術(shù)，并介紹了可計算性理論的一般知識。本版的重要改進在于引入了參數(shù)化的模板，從而提高了算法中數(shù)據(jù)類型的通用性，支持高效的代碼重用。
《國外計算機科學教材系列：數(shù)據(jù)結(jié)構(gòu)與算法分析（C++版）（第3版）（英文版）》概念清楚，邏輯性強，內(nèi)容新穎，適合作為大專院校計算機軟件專業(yè)與計算機應用專業(yè)學生的雙語教學教材和參考書，也適合計算機工程技術(shù)人員參考。
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 for the significant effects of the physical medium employed (e.g., data stored on disk versus main memory).
2. Related to costs and benefits is the notion of tradeoffs. For example, it is quite common to reduce time requirements at the expense of an increase in space requirements, or vice versa. Programmers face tradeoff issues regularly in all phases of software design and implementation, so the concept must become deeply ingrained.
3. Programmers should know enough about common practice to avoid reinventing the wheel. Thus, programmers need to learn the commonly used data structures, their related algorithms, and the most frequently encountered design patterns found in programming.
4. Data structures follow needs. Programmers must learn to assess application needs first, then find a data structure with matching capabilities. To do this requires competence in Principles 1, 2, and 3.
As I have taught data structures through the years, I have found that design issues have played an ever greater role in my courses. This can be traced through the various editions of this textbook by the increasing coverage for design patterns and generic interfaces. The first edition had no mention of design patterns. The second edition had limited coverage of a few example patterns, and introduced the dictionary ADT and comparator classes. With the third edition, there is explicit coverage of some design patterns that are encountered when programming the basic data structures and algorithms covered in the book.
Using the Book in Class: Data structures and algorithms textbooks tend to fall into one of two categories: teaching texts or encyclopedias. Books that attempt to do both usually fail at both. This book is intended as a teaching text. I believe it is more important for a practitioner to understand the principles required to select or design the data structure that will best solve some problem than it is to memorize a lot of textbook implementations.
Preface
Part I Preliminaries
Chapter 1 Data Structures and Algorithms
1.1 A Philosophy of Data Structures
1.1.1 The Need for Data Structures
1.1.2 Costs and Benefits
1.2 Abstract Data Types and Data Structures
1.3 Design Patterns
1.3.1 Flyweight
1.3.2 Visitor
1.3.3 Composite
1.3.4 Strategy
1.4 Problems， Algorithms， and Programs
1.5 Further Reading
1.6 Exercises
Chapter 2 Mathematical Preliminaries
2.1 Sets and Relations
2.2 Miscellaneous Notation
2.3 Logarithms
2.4 Summations and Recurrences
2.5 Recursion
2.6 Mathematical Proof Techniques
2.6.1 Direct Proof
2.6.2 Proof by Contradiction
2.6.3 Proof by Mathematical Induction
2.7 Estimation
2.8 Further Reading
2.9 Exercises
Chapter 3 Algorithm Analysis
3.1 Introduction
3.2 Best， Worst， and Average Cases
3.3 A Faster Computer， or a Faster Algorithm？
3.4 Asymptotic Analysis
3.4.1 Upper Bounds
3.4.2 Lower Bounds
3.4.3 Notation
3.4.4 Simplifying Rules
3.4.5 Classifying Functions
3.5 Calculating the Running Time for a Program
3.6 Analyzing Problems
3.7 Common Misunderstandings
3.8 Multiple Parameters
3.9 Space Bounds
3.10 Speeding Up Your Programs
3.11 Empirical Analysis
3.12 Further Reading
3.13 Exercises
3.14 Projects
Part II Fundamental Data Structures
Chapter 4 Lists， Stacks， and Queues
4.1 Lists
4.1.1 Array-Based List Implementation
4.1.2 Linked Lists
4.1.3 Comparison of List Implementations
4.1.4 Element Implementations
4.1.5 Doubly Linked Lists
4.2 Stacks
4.2.1 Array-Based Stacks
4.2.2 Linked Stacks
4.2.3 Comparison of Array-Based and Linked Stacks
4.2.4 Implementing Recursion
4.3 Queues
4.3.1 Array-Based Queues
4.3.2 Linked Queues
4.3.3 Comparison of Array-Based and Linked Queues
4.4 Dictionaries
4.5 Further Reading
4.6 Exercises
4.7 Projects
Chapter 5 Binary Trees
5.1 Definitions and Properties
5.1.1 The Full Binary Tree Theorem
5.1.2 A Binary Tree Node ADT
5.2 Binary Tree Traversals
5.3 Binary Tree Node Implementations
5.3.1 Pointer-Based Node Implementations
5.3.2 Space Requirements
5.3.3 Array Implementation for Complete Binary Trees
5.4 Binary Search Trees
5.5 Heaps and Priority Queues
5.6 Huffman Coding Trees
5.6.1 Building Huffman Coding Trees
5.6.2 Assigning and Using Huffman Codes
5.6.3 Search in Huffman Trees
5.7 Further Reading
5.8 Exercises
5.9 Projects
Chapter 6 Non-Binary Trees
6.1 General Tree Definitions and Terminology
6.1.1 An ADT for General Tree Nodes
6.1.2 General Tree Traversals
6.2 The Parent Pointer Implementation
6.3 General Tree Implementations
6.3.1 List of Children
6.3.2 The Left-Child/Right-Sibling Implementation
6.3.3 Dynamic Node Implementations
6.3.4 Dynamic “Left-Child/Right-Sibling” Implementation
6.4 K-ary Trees
6.5 Sequential Tree Implementations
6.6 Further Reading
6.7 Exercises
6.8 Projects
Part III Sorting and Searching
Chapter 7 Internal Sorting
7.1 Sorting Terminology and Notation
7.2 Three (n2) Sorting Algorithms
7.2.1 Insertion Sort
7.2.2 Bubble Sort
7.2.3 Selection Sort
7.2.4 The Cost of Exchange Sorting
7.3 Shellsort
7.4 Mergesort
7.5 Quicksort
7.6 Heapsort
7.7 Binsort and Radix Sort
7.8 An Empirical Comparison of Sorting Algorithms
7.9 Lower Bounds for Sorting
7.10 Further Reading
7.11 Exercises
7.12 Projects
Chapter 8 File Processing and External Sorting
8.1 Primary versus Secondary Storage
8.2 Disk Drives
8.2.1 Disk Drive Architecture
8.2.2 Disk Access Costs
8.3 Buffers and Buffer Pools
8.4 The Programmer’s View of Files
8.5 External Sorting
8.5.1 Simple Approaches to External Sorting
8.5.2 Replacement Selection
8.5.3 Multiway Merging
8.6 Further Reading
8.7 Exercises
8.8 Projects
Chapter 9 Searching
9.1 Searching Unsorted and Sorted Arrays
9.2 Self-Organizing Lists
9.3 Bit Vectors for Representing Sets
9.4 Hashing
9.4.1 Hash Functions
9.4.2 Open Hashing
9.4.3 Closed Hashing
9.4.4 Analysis of Closed Hashing
9.4.5 Deletion
9.5 Further Reading
9.6 Exercises
9.7 Projects
Chapter 10 Indexing
10.1 Linear Indexing
10.2 ISAM
10.3 Tree-based Indexing
10.4 2-3 Trees
10.5 B-Trees
10.5.1 B+-Trees
10.5.2 B-Tree Analysis
10.6 Further Reading
10.7 Exercises
10.8 Projects
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