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內容簡介

　　《數據結構與算法分析：C語言描述》曾被評為20世紀很好的30部計算機著作之一，作者在數據結構和算法分析方麵卓有建樹，他的數據結構和算法分析的著作尤其暢銷，並受到廣泛好評，已被世界500餘所大學選作教材。
　　在《數據結構與算法分析：C語言描述》中，作者精煉並強化瞭他對算法和數據結構方麵創新的處理方法。通過C程序的實現，著重闡述瞭抽象數據類型的概念，並對算法的效率、性能和運行時間進行瞭分析。
　　《數據結構與算法分析：C語言描述》特色：著重討論瞭算法設計技巧，包括貪婪算法、分治算法、動態規劃、隨機化算法以及迴溯算法。係統介紹瞭當前流行的論題和新的數據結構，如斐波那契堆、斜堆、二項隊列、跳躍錶和伸展樹。詳細討論瞭攤還分析，考查書中介紹的一些高級數據結構。增加瞭高級數據結構及其實現的內容，包括紅黑樹、自頂嚮下伸展樹、treap樹、k-d樹、配對堆等。整閤瞭堆排序平均情況分析的一些新結果。

作者簡介

　　Mark Allen Weiss 1987年在普林斯頓大學獲得計算機科學博士學位。師 從Roberl Sedgewick，現任美國佛羅裏達國際大學計算與信息科學學院教授。他曾擔任全美AP（Advanced Placement）考試計算機學科委員會主席。其主要研究方嚮是數據結構、算法和教育學。

內頁插圖

目錄

Introduction 1
1.1. Whats the Book About? 1
1.2. Mathematics Review 3
1.2.1. Exponents 3
1.2.2. Logarithms 3
1.2.3. Series 4
1.2.4. Modular Arithmetic 5
1.2.5. The P Word 6
1.3. A Brief Introduction to Recursion
Summary 12
Exercises 12
References 13

2 Algorithm Analysis 15
2.1. Mathematical Background 15
2.2. Model 18
2.3. What to Analyze 18
2.4. Running Tune Calculations 20
2.4.1. A Simple Example 21
2.4.2. General Rules 21
2.4.3. Solutions for the Maximum Subsequence Sum Problem 24
2.4.4. Logarithms in the Running Tune 28
2.4.5. Checking Your Analysis 33
2.4.6. A Grain of Salt 33
Summary 34
Exercises 35
References 39

3 Lists, Stacks, and Queues 41
3.1. Abstract Data Types (AnTs) 41
3.2. The List ADT 42
3.2.1. Simple Array Implementation of Lists 43
3.2.2. Linked Lists 43
3.2.3. Programming Details 44
3.2.4. Common Errors 49
3.2.5. Doubly Linked Lists 51
3.2.6. Circularly Unked Lists 52
3.2.7. Examples 52
3.2.8. Cursor Implementation of Linked Lists 57
3.3. The Stack ADT 62
3.3.1. Stack Model 62
3.3.2. Implementation of Stacks 63
3.3.3. Applications 71
3.4. The Queue ADT 79
3.4.1. Queue Model 79
3.4.2. Array Implementation of Queues 79
3.4.3. Applications of Queues 84
Summary 85
Exercises 85

4 Trees 89
4.1. Preliminaries 89
4.1.1. Implementation of Trees 90
4.1.2. Tree Traversals with an Application 91
4.2. Binary Trees 95
4.2.1. Implementation 96
4.2.2. Expression Trees 97
4.3. The Search Tree ADT-Binary Search Trees 100
4.3.1. MakeEmpty 101
4.3.2. Find 101
4.3.3. FindMin and FindMax 103
4.3.4. Insert 104
4.3.5. Delete 105
4.3.6. Average-Case Analysis 107
4.4. AvI Trees 110
4.4.1. Single Rotation 112
4.4.2. Double Rotation 115
4.5. Splay Trees 123
4.5.1. A Simple Idea (That Does Not Work) 124
4.5.2. Splaying 126
4.6. Tree Traversals (Revisited) 132
4.7. B-Trees 133
Summary 138
Exercises 139
References 146

5 Hashing 149
5.1. General Idea 149
5.2. Hash Function 150
5.3. Separate Chaining 152
5.4. Open Addressing 157
5.4.1. Linear Probing 157
5.4.2. Quadratic Probing 160
5.4.3. Double Hashing 164
5.5. Rehashing 165
5.6. Extendible Hashing 168
Summary 171
Exercises 172
References 175

6 Priority Queues (Heaps) 177
6.1. Model 177
6.2. Simple Implementations 178
6.3. Binary Heap 179
6.3.1. Strocture Property 179
6.3.2. Heap Order Property 180
6.3.3. Basic Heap Operations 182
6.3.4. Other Heap Operations 186
6.4. Applications of Priority Queues 189
6.4.1. The Selection Problem 189
6.4.2. Event Simulation 191
6.5. d-Heaps 192
6.6. Leftist Heaps 193
6.6.1. Leftist Heap Properly 193
6.6.2. Leftist Heap Operations 194
6.7. Skew Heaps 200
6.8. Binomial Queues 202
6.8.1. Binomial Queue Structure 202
6.8.2. Binomial Queue Operations 204
6.8.3. Implementation of Binomial Queues 205
Summary 212
Exercises 212
References 216

7 Sorting 219
7.1. Preliminaries 219
7.2. Insertion Sort 220
7.2.1. The Algorithm 220
7.2.2. Analysis of Insertion Sort 221
7.3. A Lower Bound for Simple Sorting Algorithms 221
7.4. SheUsort 222
7.4.1. Worst-Case Analysis of Shellsort 224
7.5. Heapsort 226
7.5.1. Analysis of Heapsort 228
7.6. Mergesort 230
7.6.1. Analysis of Mergesort 232
7.7. Quicksort 235
7.7.1. Picking the Pivot 236
7.7.2. Partitioning Strategy 237
7.7.3. Small Arrays 240
7.7.4. Actual Quicksort Routines 240
7.7.5. Analysis of Quicksort 241
7.7.6. A Linear-Expected-Time Algorithm for Selection 245
7.8. Sorting Large Structures 247
7.9. A General Lower Bound for Sorting 247
7.9.1. Decision Trees 247
7.10. Bucket Sort 250
7.11. External Sorting 250
7.11.1. Why We Need New Algorithms 251
7.11.2. Model for External Sorting 251
……
8 The Disjoint Set ADT
9 Graph Algorithms
10 Algorithm Design Techniques
11 Amortized Analysis
12 Advanced Data Structures and Implementation

精彩書摘

　　This example illustrates what we call randomized algorithms. At least onceduring the algorithm， a random number is used to make a decision. The runningtime of the algorithm depends not only on the particular input, but also on therandom numbers that occur.
　　The worst-case running time of a randomized algorithm is almost always thesame as the worst-case running time of the nonrandomized algorithm. The importantdifference is that a good randomized algorithm has no bad inputs, but only badrandom numbers （relative to the particular input）. This may seem like only aphilosophical difference, but actually it is quite important, as the following exampleshows.
　　Consider two variants of quicksort. Variant A uses the first element as pivot,while variant B uses a randomly chosen element as pivot. In both cases, the worst-case running time is （N2）, because it is possible at each step that the largestelement is chosen as pivot. The difference between these worst cases is that there is aparticular input that can always be presented to variant A to cause the bad runningtime. Variant A will run in （N2） time every single time it is given an already-sortedlist. If variant B is presented with the same input twice, it will have two differentrunning times, depending on what random numbers occur.

前言/序言

　　This book describes data structures， methods of organizing large amounts of data,and algorithm analysis, the estimation of the running time of algorithms. As com-puters become faster and faster, the need for programs that can handle large amountsof input becomes more acute. Paradoxically, this requires more careful attention toefficiency, since inefficiencies in programs become most obvious when input sizes arelarge. By analyzing an algorithm before it is actually coded, students can decide if aparticular solution will be feasible. For example, in this text students look at specificproblems and see how careful implementations can reduce the time constraint forlarge amounts of data from 16 years to less than a second. Therefore, no algorithmor data structure is presented without an explanation of its running time. In somecases, minute details that affect the running time of the implementation are explored.

　　Once a solution method is determined, a program must still be written. Ascomputers have become more powerful, the problems they must solve have becomelarger and more complex, requiring development of more intricate programs. Thegoal of this text is to teach students good programming and algorithm analysis skillssimultaneously so that they can develop such programs with the maximum amountof efficiency.

　　This book is suitable for either an advanced data structures （CS7） course ora first-year graduate course in algorithm analysis. Students should have some know-ledge of intermediate programming, including such topics as pointers and recursion,and some background in discrete math.



數據結構與算法分析——C語言描述（英文版·第2版） [Date Structures and Algorithm Analysis in C] 下載 mobi epub pdf txt 電子書

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