算法分析：有效的學習方法

Preface
Chapter1 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
Chapter2 Searching and Selection Algorithms
2.1 Sequential Search
2.2 Binary Search
2.3 Selection
2.4 Programming Exercise
Chapter3 Sorting Algortithms
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
Chapter4 Numeric Algorithms
4.1 Calculating Polynomials
4.2 Matrix Multiplication
4.3 Linear Equations
Chapter5 Matching Algorithms
5.1 String Matching
5.2 Approximate String Matching
5.3 Programming Exercises
Chapter6 Graph Algorthms
6.1 Graph Background and Terminology
6.2 Data Structure Methods for Graphs
6.3 Depth-First and Breadth-Fist 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
Chapter9 Other Algorithmic Techniques
9.1 Greedy Approximation Algorithms
9.2 Probabilistic Algorithms
9.3 Dynamic Programming
9.4 Programming Exercises
Appendix A Random Numbe Table
Appendix B Pseudorandom Number Generation
Appendix C Results of Chapter Study Suggestion
Appendix D References
Index