Mark Allen Weiss，佛罗里达国际大学计算与信息科学学院教授、副院长，本科教育主任和研究生教育主任。他于1987年获得普林斯顿大学计算机科学博士学位，师从Bob Sedgewick。他曾经担任全美APAdvanced Placement考试计算机学科委员会的主席2000-2004。Weiss教授在数据结构和算法分析方面卓有建树，他的数据结构和算法分析的著作尤其畅销，并受到广泛好评．已被世界500余所大学用作教材。
Mark Allen Weiss，佛罗里达国际大学计算与信息科学学院教授、副院长，本科教育主任和研究生教育主任。他于1987年获得普林斯顿大学计算机科学博士学位，师从Bob Sedgewick。他曾经担任全美APAdvanced Placement考试计算机学科委员会的主席2000-2004。Weiss教授在数据结构和算法分析方面卓有建树，他的数据结构和算法分析的著作尤其畅销，并受到广泛好评．已被世界500余所大学用作教材。
目錄：
Contents
Chapter 1 Programming： A General Overview 1
1.1 Whats This Book About? 1
1.2 Mathematics Review 2
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 8
1.4 C Classes 12
1.4.1 Basic class Syntax 12
1.4.2 Extra Constructor Syntax and Accessors 13
1.4.3 Separation of Interface and Implementation 16
1.4.4 vector and string 19
1.5 C Details 21
1.5.1 Pointers 21
1.5.2 Lvalues, Rvalues, and References 23
1.5.3 Parameter Passing 25
1.5.4 Return Passing 27
1.5.5 std：：swap and std：：move 29
1.5.6 The Big-Five： Destructor, Copy Constructor, Move Constructor, Copy
Assignment operator=, Move Assignment operator= 30
1.5.7 C-style Arrays and Strings 35
1.6 Templates 36
1.6.1 Function Templates 37
1.6.2 Class Templates 38
1.6.3 Object, Comparable, and an Example 39
1.6.4 Function Objects 41
1.6.5 Separate Compilation of Class Templates 44
1.7 Using Matrices 44
1.7.1 The Data Members, Constructor, and Basic Accessors 44
1.7.2 operator[] 45
1.7.3 Big-Five 46
Summary 46
Exercises 46
References 48
Chapter 2 Algorithm Analysis 51
2.1 Mathematical Background 51
2.2 Model 54
2.3 What to Analyze 54
2.4 Running-Time Calculations 57
2.4.1 A Simple Example 58
2.4.2 General Rules 58
2.4.3 Solutions for the Maximum Subsequence
Sum Problem 60
2.4.4 Logarithms in the Running Time 66
2.4.5 Limitations of Worst-Case Analysis 70
Summary 70
Exercises 71
References 76
Chapter 3 Lists, Stacks, and Queues 77
3.1 Abstract Data Types ADTs 77
3.2 The List ADT 78
3.2.1 Simple Array Implementation of Lists 78
3.2.2 Simple Linked Lists 79
3.3 vector and list in the STL 80
3.3.1 Iterators 82
3.3.2 Example： Using erase on a List 83
3.3.3 const_iterators 84
3.4 Implementation of vector 86
3.5 Implementation of list 91
3.6 The Stack ADT 103
3.6.1 Stack Model 103
3.6.2 Implementation of Stacks 104
3.6.3 Applications 104
3.7 The Queue ADT 112
3.7.1 Queue Model 113
3.7.2 Array Implementation of Queues 113
3.7.3 Applications of Queues 115
Summary 116
Exercises 116
Chapter 4 Trees 121
4.1 Preliminaries 121
4.1.1 Implementation of Trees 122
4.1.2 Tree Traversals with an Application 123
4.2 Binary Trees 126
4.2.1 Implementation 128
4.2.2 An Example： Expression Trees 128
4.3 The Search Tree ADT?aBinary Search Trees 132
4.3.1 contains 134
4.3.2 findMin and findMax 135
4.3.3 insert 136
4.3.4 remove 139
4.3.5 Destructor and Copy Constructor 141
4.3.6 Average-Case Analysis 141
4.4 AVL Trees 144
4.4.1 Single Rotation 147
4.4.2 Double Rotation 149
4.5 Splay Trees 158
4.5.1 A Simple Idea That Does Not Work 158
4.5.2 Splaying 160
4.6 Tree Traversals Revisited 166
4.7 B-Trees 168
4.8 Sets and Maps in the Standard Library 173
4.8.1 Sets 173
4.8.2 Maps 174
4.8.3 Implementation of set and map 175
4.8.4 An Example That Uses Several Maps 176
Summary 181
Exercises 182
References 189
Chapter 5 Hashing 193
5.1 General Idea 193
5.2 Hash Function 194
5.3 Separate Chaining 196
5.4 Hash Tables without Linked Lists 201
5.4.1 Linear Probing 201
5.4.2 Quadratic Probing 202
5.4.3 Double Hashing 207
5.5 Rehashing 208
5.6 Hash Tables in the Standard Library 210
5.7 Hash Tables with Worst-Case O1 Access 212
5.7.1 Perfect Hashing 213
5.7.2 Cuckoo Hashing 215
5.7.3 Hopscotch Hashing 227
5.8 Universal Hashing 230
5.9 Extendible Hashing 233
Summary 236
Exercises 237
References 241
Chapter 6 Priority Queues Heaps 245
6.1 Model 245
6.2 Simple Implementations 246
6.3 Binary Heap 247
6.3.1 Structure Property 247
6.3.2 Heap-Order Property 248
6.3.3 Basic Heap Operations 249
6.3.4 Other Heap Operations 252
6.4 Applications of Priority Queues 257
6.4.1 The Selection Problem 258
6.4.2 Event Simulation 259
6.5 d-Heaps 260
6.6 Leftist Heaps 261
6.6.1 Leftist Heap Property 261
6.6.2 Leftist Heap Operations 262
6.7 Skew Heaps 269
6.8 Binomial Queues 271
6.8.1 Binomial Queue Structure 271
6.8.2 Binomial Queue Operations 271
6.8.3 Implementation of Binomial Queues 276
6.9 Priority Queues in the Standard Library 282
Summary 283
Exercises 283
References 288
Chapter 7 Sorting 291
7.1 Preliminaries 291
7.2 Insertion Sort 292
7.2.1 The Algorithm 292
7.2.2 STL Implementation of Insertion Sort 293
7.2.3 Analysis of Insertion Sort 294
7.3 A Lower Bound for Simple Sorting Algorithms 295
7.4 Shellsort 296
7.4.1 Worst-Case Analysis of Shellsort 297
7.5 Heapsort 300
7.5.1 Analysis of Heapsort 301
7.6 Mergesort 304
7.6.1 Analysis of Mergesort 306
7.7 Quicksort 309
7.7.1 Picking the Pivot 311
7.7.2 Partitioning Strategy 313
7.7.3 Small Arrays 315
7.7.4 Actual Quicksort Routines 315
7.7.5 Analysis of Quicksort 318
7.7.6 A Linear-Expected-Time Algorithm for Selection 321
7.8 A General Lower Bound for Sorting 323
7.8.1 Decision Trees 323
7.9 Decision-Tree Lower Bounds for Selection Problems 325
7.10 Adversary Lower Bounds 328
7.11 Linear-Time Sorts： Bucket Sort and Radix Sort 331
7.12 External Sorting 336
7.12.1 Why We Need New Algorithms 336
7.12.2 Model for External Sorting 336
7.12.3 The Simple Algorithm 337
7.12.4 Multiway Merge 338
7.12.5 Polyphase Merge 339
7.12.6 Replacement Selection 340
Summary 341
Exercises 341
References 347
Chapter 8 The Disjoint Sets Class 351
8.1 Equivalence Relations 351
8.2 The Dynamic Equivalence Problem 352
8.3 Basic Data Structure 353
8.4 Smart Union Algorithms 357
8.5 Path Compression 360
8.6 Worst Case for Union-by-Rank and Path Compression 361
8.6.1 Slowly Growing Functions 362
8.6.2 An Analysis by Recursive Decomposition 362
8.6.3 An O M log *N Bound 369
8.6.4 An O M |M, N Bound 370
8.7 An Application 372
Summary 374
Exercises 375
References 376
Chapter 9 Graph Algorithms 379
9.1 Definitions 379
9.1.1 Representation of Graphs 380
9.2 Topological Sort 382
9.3 Shortest-Path Algorithms 386
9.3.1 Unweighted Shortest Paths 387
9.3.2 Dijkstras Algorithm 391
9.3.3 Graphs with Negative Edge Costs 400
9.3.4 Acyclic Graphs 400
9.3.5 All-Pairs Shortest Path 404
9.3.6 Shortest Path Example 404
9.4 Network Flow Problems 406
9.4.1 A Simple Maximum-Flow Algorithm 408
9.5 Minimum Spanning Tree 413
9.5.1 Prims Algorithm 414
9.5.2 Kruskals Algorithm 417
9.6 Applications of Depth-First Search 419
9.6.1 Undirected Graphs 420
9.6.2 Biconnectivity 421
9.6.3 Euler Circuits 425
9.6.4 Directed Graphs 429
9.6.5 Finding Strong Components 431
9.7 Introduction to NP-Completeness 432
9.7.1 Easy vs. Hard 433
9.7.2 The Class NP 434
9.7.3 NP-Complete Problems 434
Summary 437
Exercises 437
References 445
Chapter 10 Algorithm Design Techniques 449
10.1 Greedy Algorithms 449
10.1.1 A Simple Scheduling Problem 450
10.1.2 Huffman Codes 453
10.1.3 Approximate Bin Packing 459
10.2 Divide and Conquer 467
10.2.1 Running Time of Divide-and-Conquer Algorithms 468
10.2.2 Closest-Points Problem 470
10.2.3 The Selection Problem 475
10.2.4 Theoretical Improvements for Arithmetic Problems 478
10.3 Dynamic Programming 482
10.3.1 Using a Table Instead of Recursion 483
10.3.2 Ordering Matrix Multiplications 485
10.3.3 Optimal Binary Search Tree 487
10.3.4 All-Pairs Shortest Path 491
10.4 Randomized Algorithms 494
10.4.1 Random-Number Generators 495
10.4.2 Skip Lists 500
10.4.3 Primality Testing 503
10.5 Backtracking Algorithms 506
10.5.1 The Turnpike Reconstruction Problem 506
10.5.2 Games 511
Summary 518
Exercises 5
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