算法设计与分析基础（第2版 影印版）
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作者： （美）莱维丁（Levitin，A.）著
出 版 社： 清华大学出版社
出版时间： 2007-11-1字数：版次： 1页数： 562印刷时间： 2007/11/01开本： 16开印次： 1纸张： 胶版纸I S B N ： 9787302164029包装： 平装内容简介
本书采用了一种算法设计技术的新分类方法，不但比传统分类法包容性更强，而且更直观，也更有效，因此广受好评。
这种分类框架条理清晰，契合教育学原理，非常适合算法教学。网上提供了详尽的教学指南供教师和学生下载，书中还为学生安排了习题提示和每章小结。为了提高学习兴趣，书中应用了许多流行的谜题和游戏，需要重点思考的地方则往往会用反问来提醒注意。
作者简介
(美) Anany Levitin是Villanova大学计算科学系的教授。他的论文A New Road Map of Algorithm Design Techniques：Picking Up Where the Traditi。onal Classification Leaves Off(《算法设计技术新途径：弥补传统分类法的缺感》)受到业内人士极高的评价。在SIGCSE会议上，作者做过多次关于算法教学的演讲。
目录
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
