| Preface Chapter 1 - Analysis Basics 1.1 What is Analysis? 1.2 What to Count and Consider 1.3 Mathematical Background 1.4 Rates of Growth 1.5 Divide and Conquer Algorithms 1.6 Recurrence Relations 1.7 Analyzing Programs Chapter 2 - Searching and Selection Algorithms 2.1 Sequential Search 2.2 Binary Search 2.3 Selection 2.4 Programming Exercise Chapter 3 - Sorting Algorithms 3.1 Insertion Sort 3.2 Bubble Sort 3.3 Shellsort 3.4 Radix Sort 3.5 Heapsort 3.6 Merge Sort 3.7 Quicksort 3.8 External Polyphase Merge Sort 3.9 Additional Exercises 3.10 Programming Exercises Chapter 4 - Numeric Algorithms 4.1 Calculating Polynomials 4.2 Matrix Multiplication 4.3 Linear Equations Chapter 5 - Matching Algorithms 5.1 String Matching 5.2 Approximate String Matching 5.3 Programming Exercises Chapter 6 - Graph Algorithms 6.1 Graph Background and Terminology 6.2 Data Structure Methods for Graphs 6.3 Depth-First and Breadth-First Traversal Algorithms 6.4 Minimum Spanning Tree Algorithm 6.5 Shortest-Path Algorithm 6.6 Biconnected Component Algorithm 6.7 Partitioning Sets 6.8 Programming Exercises Chapter 7 - Parallel Algorithms 7.1 Parallelism Introduction 7.2 The PRAM Model 7.3 Simple Parallel Operations 7.4 Parallel Searching 7.5 Parallel Sorting 7.6 Parallel Numerical Algorithms 7.7 Parallel Graph Algorithms Chapter 8 - Nondeterministic Algorithms 8.1 What is NP? 8.2 Typical NP Problems 8.3 What Makes Something NP? 8.4 Testing Possible Solutions Chapter 9 - Other Algorithmic Techniques 9.1 Greedy Approximation Algorithms 9.2 Probabilistic Algorithms 9.3 Dynamic Programming 9.4 Programming Exercises Appendix A Random Number Table Appendix B Pseudorandom Number Generation Appendix C Results of Chapter Study Suggestion Appendix D References Index |
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