大学计算机教育国外著名教材系列：算法设计（影印版）
About the Authors
Preface
1 Introduction：Some Representative Problems
1.1 A First Problem：Stable Matching
1.2 Five Representative Problems
Solved Exercises
Exercises
Notes and Further Reading
2 Basics of Algorithm Analysis
2.1 Computational Tractability
2.2 Asymptotic Order of Growth
2.3 Implementing the Stable Matching Algorithm Using Lists and Arrays
2.4 A Survey of Common Running Times
2.5 A More Complex Data Structure：Priority Queues
Solved Exercises
Exercises
Notes and Further Reading
3 Graphs
3.1 Basic Definitions and Applications
3.2 Graph Connectivity and Graph Traversal
3.3 Implementing Graph Traversal Using Queues and Stacks
3.4 Testing Bipaniteness：An Application of Breadth-First Search
3.5 Connectivity in Directed Graphs
3.6 Directed Acyclic Graphs and Topological Ordering
Solved Exercises
Exercises
Notes and Further Reading
4 Greedy Algorithms
4.1 Interval Scheduling：The Greedy Algorithm Stays Ahead
4.2 Scheduling to Minimize Lateness：An Exchange Argument
4.3 Optimal Caching：A More Complex Exchange Argument
4.4 Shortest Paths in a Graph
4.5 The Minimum Spanning Tree Problem
4.6 Implementing Kruskal's Algorithm：The Union-Find Data Structure
4.7 Clustering
4.8 Huffman Codes and Data Compression
* 4.9 Minimum-Cost Arborescences：A Multi-Phase Greedy Algorithm
Solved Exercises
Exercises
Notes and Further Reading
5 Divide and Cornquer
5.1 A First Recurrence：The Mergesort Algorithm
5.2 Further Recurrence Relations
5.3 Counting Inversions
5.4 Finding the Closest Pair of Points
5.5 Integer Multiplication
5.6 Convolutions and the Fast Fourier Transform
Solved Exercises
Exercises
Notes and Further Reading
6 Dynamic Programming
6.1 Weighted Interval Scheduling：A Recursive Procedure
6.2 Principles of Dynamic Programming：Memoization or Iteration over Subproblems
6.3 Segmented Least Squares：Multi-way Choices
……
7 Network Flow
8 NP and Computational Intractability
9 PSPACE：A Class of Problems beyond NP
10 Extending the Limits of Tractability
11 Approximation Algorithms
12 Local Search
13 Randomized Algorithms
Epilogue：Algorithms That Run Forever
References
Index
Preface
1 Introduction：Some Representative Problems
1.1 A First Problem：Stable Matching
1.2 Five Representative Problems
Solved Exercises
Exercises
Notes and Further Reading
2 Basics of Algorithm Analysis
2.1 Computational Tractability
2.2 Asymptotic Order of Growth
2.3 Implementing the Stable Matching Algorithm Using Lists and Arrays
2.4 A Survey of Common Running Times
2.5 A More Complex Data Structure：Priority Queues
Solved Exercises
Exercises
Notes and Further Reading
3 Graphs
3.1 Basic Definitions and Applications
3.2 Graph Connectivity and Graph Traversal
3.3 Implementing Graph Traversal Using Queues and Stacks
3.4 Testing Bipaniteness：An Application of Breadth-First Search
3.5 Connectivity in Directed Graphs
3.6 Directed Acyclic Graphs and Topological Ordering
Solved Exercises
Exercises
Notes and Further Reading
4 Greedy Algorithms
4.1 Interval Scheduling：The Greedy Algorithm Stays Ahead
4.2 Scheduling to Minimize Lateness：An Exchange Argument
4.3 Optimal Caching：A More Complex Exchange Argument
4.4 Shortest Paths in a Graph
4.5 The Minimum Spanning Tree Problem
4.6 Implementing Kruskal's Algorithm：The Union-Find Data Structure
4.7 Clustering
4.8 Huffman Codes and Data Compression
* 4.9 Minimum-Cost Arborescences：A Multi-Phase Greedy Algorithm
Solved Exercises
Exercises
Notes and Further Reading
5 Divide and Cornquer
5.1 A First Recurrence：The Mergesort Algorithm
5.2 Further Recurrence Relations
5.3 Counting Inversions
5.4 Finding the Closest Pair of Points
5.5 Integer Multiplication
5.6 Convolutions and the Fast Fourier Transform
Solved Exercises
Exercises
Notes and Further Reading
6 Dynamic Programming
6.1 Weighted Interval Scheduling：A Recursive Procedure
6.2 Principles of Dynamic Programming：Memoization or Iteration over Subproblems
6.3 Segmented Least Squares：Multi-way Choices
……
7 Network Flow
8 NP and Computational Intractability
9 PSPACE：A Class of Problems beyond NP
10 Extending the Limits of Tractability
11 Approximation Algorithms
12 Local Search
13 Randomized Algorithms
Epilogue：Algorithms That Run Forever
References
Index
Jon Kleinberg is a professor of Computer Science atCornell University.He received his Ph.D.from M.I.T.in 1996.He is the recipient of an NSF Career Award，an ONR Young Investigator Award，an IBM Outstand-ing Innovation Award，the National Academy of Sci-ences Award for Initiatives in Research，research fel-lowships from the Packard and Sloan Foundations，and teaching awards from the Cornell EngineeringCollege and Computer Science Department.
Kleinberg's research is centered around algorithms，particularly those con-cerned with the structure of networks and information，and with applicationsto information science，optimization，data mining，and computational biol-ogy.His work on network analysis using hubs and authorities helped form thefoundation for the current generation of Internet search engines.Eva Tardos is a professor of Computer Science at Cor-nell University.She received her Ph.D.from EotvosUniversity in Budapest，Hungary in 1984.She is amember of the American Academy of Arts and Sci-ences，and an ACM Fellow;she is the recipient of anNSF Presidential Young Investigator Award，the Fulk-erson Prize，research fellowships from the Guggen-heim，Packard，and Sloan Foundations，and teach-ing awards from the Cornell Engineenng College andComputer Science Department.Tardos's research interests are focused on the design and analysis ofalgorithms for problems on graphs or networks.She is most known for herwork on network-flow algorithms and approximation algorithms for networkproblems.Her recent work focuses on algorithmic game theory，an emergingarea concerned with designing systems and algorithms for selfish users.
Kleinberg's research is centered around algorithms，particularly those con-cerned with the structure of networks and information，and with applicationsto information science，optimization，data mining，and computational biol-ogy.His work on network analysis using hubs and authorities helped form thefoundation for the current generation of Internet search engines.Eva Tardos is a professor of Computer Science at Cor-nell University.She received her Ph.D.from EotvosUniversity in Budapest，Hungary in 1984.She is amember of the American Academy of Arts and Sci-ences，and an ACM Fellow;she is the recipient of anNSF Presidential Young Investigator Award，the Fulk-erson Prize，research fellowships from the Guggen-heim，Packard，and Sloan Foundations，and teach-ing awards from the Cornell Engineenng College andComputer Science Department.Tardos's research interests are focused on the design and analysis ofalgorithms for problems on graphs or networks.She is most known for herwork on network-flow algorithms and approximation algorithms for networkproblems.Her recent work focuses on algorithmic game theory，an emergingarea concerned with designing systems and algorithms for selfish users.
《大学计算机教育国外著名教材系列：算法设计（影印版）》是近年来关于算法设计和分析的不可多得的优秀教材。《大学计算机教育国外著名教材系列：算法设计（影印版）》围绕算法设计技术组织素材，对每种算法技术选择了多个典型范例进行分析。本书将直观性与严谨性完美地结合起来。每章从实际问题出发，经过具体、深入、细致的分析，自然且富有启发性地引出相应的算法设计思想，并对算法的正确性、复杂性进行恰当的分析、论证。本书覆盖的面较宽，凡属串行算法的经典论题都有涉及，并且论述深入有新意。全书共200多道丰富而精彩的习题是本书的重要组成部分，也是本书的突出特色之一。
本书适用于本科高年级学生以及研究生算法课的教材，也很适于具有计算机或相近专业本科水平的人自学算法的需要。
本书适用于本科高年级学生以及研究生算法课的教材，也很适于具有计算机或相近专业本科水平的人自学算法的需要。
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