算法設(shè)計(jì)與分析基礎(chǔ)（第2版影印版）

Preface
1 Introduction
　1.1 What is an Algorithm?
　Exercises 1.1
　1.2 Fundamentals of Algorithmic Problem Solving
　 Understanding the Problem
　 Ascertaining the Capabilities of a Computational Device
　 Choosing between Exact and Approximate Problem Solving
　 Deciding on Appropriate Data Structures
　 Algorithm Design Techniques
　 Methods of Specifying an Algorithm
　 Proving an Algorithm's Correctness
　 Analyzing an Algorithm
　 Coding an Algorithm
　 Exercises 1.2
　1.3 Important Problem Types
　 Sorting
　 Searching
　 String Processing
　 Graph Problems
　 Combinatorial Problems
　 Geometric Problems
　 Numerical Problems
　 Exercises 1.3
　1.4 Fundamental Data Structures
　 Linear Data Structures
　 Graphs
　 Trees
　 Sets and Dictionaries
　 Exercises 1.4
　 Summary
2 Fundamentals of the Analysis of Algorithm Efficiency
　2.1 Analysis Framework
　 Measuring an Input's Size
　 Units for Measuring Running -[]me
　 Orders of Growth
　 Worst-Case, Best-Case, and Average-Case Efficlencies
　 Recapitulation of the Analysis Framework
　 Exercises 2.1
　2.2 Asymptotic Notations and Basic Efficiency Classes
　 Informal Introduction
　 O-notation
　 9-notation
　 Onotation
　 Useful Property Involving the Asymptotic Notations
　 Using Limits for Comparing Orders of Growth
　 Basic Efficiency Classes
　 Exercises 2.2
　 2.3 Mathematical Analysis of Nonrecursive Algorithms
　 Exercises 2.3
　 2.4 Mathematical Analysis of Recursive Algorithms
　 Exercises 2.4
　 2.5 Example: Fibonacci Numbers
　 Explicit Formula for the nth Fibonacci Number
　 Algorithms for Computing Fibonacci Numbers
　 Exercises 2.5
3 Brute Force
4 Divide-and-Conquer
5 Decrease-and-Conquer
6 Transform-and-Conquer
7 Space and lime Tradeoffs
8 Dynamic Programming
9 Greedy Technique
10 Iterative Improvement
11 Limitations of Algorithm Power
12 Coping with the Limitations of Algorithm Power
Epilogue
APPENDIX A
Useful Formulas for the Analysis of Algorithms
APPENDIX B
Short Tutorial on Recurrence Relations
Bibliography
Hints to Exercises
Index