| 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. |
| Preface 1 Analyzing Algorithms and Problems: Principles and Examples 1.1 Introduction 1.2 Java as an Algorithm Language 1.3 Mathematical Background 1.4 Analyzing Algorithms and Problems 1.5 Classifying Functions by Their Asymptotic Growth Rates 1.6 Searching an Ordered Array Exercises Notes and References 2 Data Abstraction and Basic Data Structures 2.1 Introduction 2.2 ADT Specification and Design Techniques 2.3 Elementary ADTs--Lists and Trees 2.4 Stacks and Queues 2.5 ADTs for Dynamic Sets Exercises Notes and References 3 Recursion and induction 3.1 introduction 3.2 Recursive Procedures 3.3 What is a Proof? 3.4 Induction Proofs 3.5 Proving Correctness of Procedures 3.6 Recurrence Equations 3.7 Recursion Trees Exercises Notes and References 4 Sorting 4.1 Introduction 4.2 Insertion Sort 4.3 Divide and Conquer 4.4 Quicksort 4.5 Merging Sorted Sequences 4.6 Mergesort 4.7 Lower Bounds for Sorting by Comparison of Keys 4.8 Heapsort 4.9 Comparison of Four Sorting Algorithms 4.10 Shellsort 4.11 Radix Sorting Exercises Programs Notes and References 5 Selection and Adversary Arguments 5.1 Introduction 5.2 Finding max and min 5.3 Finding the Second-Largest Key 5.4 The Selection Problem 5.5 A Lower Bound for Finding the Median 5.6 Designing Against an Adversary Exercises Notes and References 6 Dynamic Sets and Searching 6.1 Introduction 6.2 Array Doubling 6.3 Amortized Time Analysis 6.4 Red-Black Trees 6.5 Hashing 6.6 Dynamic Equivalence Relations and Union-Find Programs 6.7 Priority Queues with a Decrease Key Operation Exercises Programs Notes and References 7 Graphs and Graph Traversals 8 Graph Optimization Problems and Greedy Algorithms 9 Transitive Closure， All-Pairs Shortest Paths 10 Dynamic Programming 11 String Matching 12 Polynomials and Matrices 13 NP-Complete Problems 14 Parallel Algorithms A Java Examples and Techniques Bibliography Index |
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