Sara Baase is professor of computer Science at San Diego University and has been teaching CS for 25years.Dr.Baase is a three-time recipient of the San State University Alumni Associations Outsatanding Faculty Award，adn she has written a number of textbooks in the areas of algorithms，assembly language，and social and ethical issues relate to computing.She earned her doctorate at the University of California，Berkeley.Allen Van Celder is professor of computer Science at the University of California at Santa Cruz，where he has been teaching CS for 12 years.He received his Ph.D.in Computer Science at Stanford University and is a past recipient of the Presidential Young Investigator Award.
圖書目錄
Contents Preface vii 1 Analyzing Algorithms and Problems: Principles and Examples 1 1.1 Introduction 2 1.2 Java as an Algorithm Language 3 1.3 Mathematical Background 11 1.4 Analyzing Algorithms and Problems 30 1.5 Classifying Functions by Their Asymptotic Growth Rates 43 1.6 Searching an Ordered Array 53 Exercises 61 Notes and References 67 2 Data Abstraction and Basic Data Structures 69 2.1 Introduction 70 2.2 ADT Specification and Design Techniques 71 2.3 Elementary ADTs--Lists and Trees 73 2.4 Stacks and Queues 86 2.5 ADTs for Dynamic Sets 89 Exercises 95 Notes and References 100 3 Recursion and induction 101 3.1 introduction 102 3.2 Recursive Procedures 102 3.3 What is a Proof? 108 3.4 Induction Proofs 111 3.5 Proving Correctness of Procedures 1 18 3.6 Recurrence Equations 130 3.7 Recursion Trees 134 Exercises 141 Notes and References 146 4 Sorting 149 4.1 Introduction 150 4.2 Insertion Sort 151 4.3 Divide and Conquer 157 4.4 Quicksort 159 4.5 Merging Sorted Sequences 171 4.6 Mergesort 174 4.7 Lower Bounds for Sorting by Comparison of Keys 178 4.8 Heapsort 182 4.9 Comparison of Four Sorting Algorithms 197 4.10 Shellsort 197 4.11 Radix Sorting 201 Exercises 206 Programs 221 Notes and References 221 5 Selection and Adversary Arguments 223 5.1 Introduction 224 5.2 Finding max and min 226 5.3 Finding the Second-Largest Key 229 5.4 The Selection Problem 233 5.5 A Lower Bound for Finding the Median 238 5.6 Designing Against an Adversary 240 Exercises 242 Notes and References 246 6 Dynamic Sets and Searching 249 6.1 Introduction 250 6.2 Array Doubling 250 6.3 Amortized Time Analysis 251 6.4 Red-Black Trees 253 6.5 Hashing 275 6.6 Dynamic Equivalence Relations and Union-Find Programs 283 6.7 Priority Queues with a Decrease Key Operation 295 Exercises 302 Programs 309 Notes and References 309 7 Graphs and Graph Traversals 313 7.1 Introduction 314 7.2 Definitions and Representations 314 7.3 Traversing Graphs 328 7.4 Depth-First Search on Directed Graphs 336 7.5 Strongly Connected Components of a Directed Graph 357 7.6 Depth-First Search on Undirected Graphs 364 7.7 Biconnected Components of an Undirected Graph 366 Exercises 375 Programs 384 Notes and References 385 8 Graph Optimization Problems and Greedy Algorithms 387 8.1 Introduction 388 8.2 Prim's Minimum Spanning Tree Algorithm 388 8.3 Single-Source Shortest Paths 403 8.4 Kruskal's Minimum Spanning Tree Algorithm 412 Exercises 416 Programs 421 Notes and References 422 9 Transitive Closure, All-Pairs Shortest Paths 425 9.1 Introduction 426 9.2 The Transitive Closure of a Binary Relation 426 9.3 Warshall's Algorithm for Transitive Closure 430 9.4 All-Pairs Shortest Paths in Graphs 433 9.5 Computing Transitive Closure by Matrix Operations 436 9.6 Multiplying Bit Matrices-Kronrod's Algorithm 439 Exercises 446 Programs 449 Notes and References 449 10 Dynamic Programming 451 10.1 Introduction 452 10.2 Subproblem Graphs and Their Traversal 453 10.3 Multiplying a Sequence of Matrices 457 10.4 Constructing Optimal Binary Search Trees 466 10.5 Separating Sequences of Words into Lines 471 10.6 Developing a Dynamic Programming Algorithm 474 Exercises 475 Programs 481 Notes and References 482 11 String Matching 483 11.1 Introduction 484 11.2 A Straightforward Solution 485 11.3 The Knuth-Moms-Pratt Algorithm 487 11.4 The Boyer-Moors Algorithm 495 11.5 Approximate String Matching 504 Exercises 508 Programs 512 Notes and References 512 12 Polynomials and Matrices 515 12.1 Introduction 516 12.2 Evaluating Polynomial Functions 516 12.3 Vector and Matrix Multiplication 522 12.4 The Fast Fourier Transform and Convolution 528 Exercises 542 Programs 546 Notes and References 546 13 NP-Complete Problems 547 13.1 Introduction 548 13.2 T and ac 548 13.3 NP-Complete Problems 559 13.4 Approximation Algorithms 570 13.5 Bin Packing 572 13.6 The Knapsack and Subset Sum Problems 577 13.7 Graph Coloring 581 13.8 The Traveling Salesperson Problem 589 13.9 Computing with DNA 592 Exercises 600 Notes and References 608 14 Parallel Algorithms 611 14.1 Introduction 612 14.2 Parallelism, the PRAM, and Other Models 612 14.3 Some Simple PRAM Algorithms 616 14.4 Handling Write Conflicts 622 14.5 Merging and Sorting 624 14.6 Finding Connected Components 628 14.7 A Lower Bound for Adding n Integers 641 Exercises 643 Notes and References 647 A Java Examples and Techniques 649 A.1 introduction 650 A.2 A Java Main Program 651 A.3 A Simple input Library 656 A.4 Documenting Java Classes 658 A.5 Generic Order and the "Comparable" Interface 659 A.6 Subclasses Extend the Capability of Their Superclass 663 A.7 Copy via the "Cloneable" Interface 667 Bibliography 669 Index 679
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