本书是近年来关于算法设计和分析的不可多得的优秀教材。本书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。
美国康乃尔大学计算机系教授Jon Kleinberg和éva Tardos合著的《算法设计》是最近几年当中关于算法设计和分析的不可多得的优秀教材。它适用于本科高年级学生以及研究生的算法课。它还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，自然地富有启发性地引出相应的算法思想，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。而且，每章习题之前都有几道精选的给出详解的例题，这对解答其后的系统极有帮助。
——黄连生 清华大学计算机系
“Algorithm Design”是我看到过的关于算法设计最好的教材之一。
——屈婉玲 北京大学信息学院
算法设计一书的前8章以及后面若干章节，构成本科生算法设计导论课程的基础。后续的章节适合于更高级研究。本书包含200多道有趣简明的作业问题?其中一些问题直接来自诸如Yahoo!和Oracle这样的公司。每个问题都经过测试，表明这些问题的有效性和精确性。
——霍红卫 西安电子科技大学计算机学院
本书的特色在于努力剖析问题本质，分析较透彻，详尽地分析了问题描述的方式，针对问题在不同情况下的求解方式进行深入的阐述。
——宋友 北京航空航天大学软件学院
本书通过实际问题的求解过程来引入算法设计思想和分析方法，对每种技术选择了多个典型范例进行分析，使读者更深入地掌握算法设计的理论和技巧，是一本难得的算法教材。
——林永钢 北京理工大学计算机学院
目 录
1 Introduction: Some Representative Problems
1.1 A First Problem: Stable Matching
1.2 Five Representative Problems
1.3 Solved Exercises
1.4 Excercises
1.5 Notes and Further Reading
2 Basics of Algorithms Analysis
2.1 Computational Tractability
2.2 Asymptotic Order of Growth Notation
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
2.6 Solved Exercises
2.5 Exercises
2.7 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 Bipartiteness: An Application of Breadth-First Search
3.5 Connectivity in Directed Graphs
3.6 Directed Acyclic Graphs and Topological Ordering
3.7 Solved Exercises
3.8 Exercises
3.9 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 the Problem of Data Compression
4.9 (*) Minimum-Cost Arborescences: A Multi-Phase Greedy Algorithm
4.10 Solved Exercises
4.11 Excercises
4.12 Notes and Further Reading
5 Divide and Conquer
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
5.7 Solved Exercises
5.8 Exercises
5.9 Notes and Further Reading
6 Dynamic Programming
6.1 Weighted Interval Scheduling: A Recursive Procedure
6.2 Weighted Interval Scheduling: Iterating over Sub-Problems
6.3 Segmented Least Squares: Multi-way Choices
6.4 Subset Sums and Knapsacks: Adding a Variable
6.5 RNA Secondary Structure: Dynamic Programming Over Intervals
6.6 Sequence Alignment
6.7 Sequence Alignment in Linear Space
6.8 Shortest Paths in a Graph
6.9 Shortest Paths and Distance Vector Protocols
6.10 (*) Negative Cycles in a Graph
6.11 Solved Exercises
6.12 Exercises
6.13 Notes and Further Reading
7 Network Flow
7.1 The Maximum Flow Problem and the Ford-Fulkerson Algorithm
7.2 Maximum Flows and Minimum Cuts in a Network
7.3 Choosing Good Augmenting Paths
7.4 (*) The Preflow-Push Maximum Flow Algorithm
7.5 A First Application: The Bipartite Matching Problem
7.6 Disjoint Paths in Directed and Undirected Graphs
7.7 Extensions to the Maximum Flow Problem
7.8 Survey Design
7.9 Airline Scheduling
7.10 Image Segmentation
7.11 Project Selection
7.12 Baseball Elimination
7.13 (*) A Further Direction: Adding Costs to the Matching Problem
7.14 Solved Exercises
7.15 Exercises
7.16 Notes and Further Reading
8 NP and Computational Intractability
8.1 Polynomial-time Reductions
8.2 Efficient Certification and the Definition of NP
8.3 NP-Complete Problems
8.4 Sequencing Problems
8.5 Partitioning Problems
8.6 Graph Coloring
8.7 Numerical Problems
8.8 co-NP and the Asymmetry of NP
8.9 A Partial Taxonomy of Hard Problems
8.10 Solved Exercises
8.11 Exercises
8.12 Notes and Further Reading
9 PSPACE: A Class of Problems Beyond NP
9.1 PSPACE
9.2 Some Hard Problems in PSPACE
9.3 Solving Quantified Problems and Games in Polynomial Space
9.4 Solving the Planning Problem in Polynomial Space
9.5 Proving Problems PSPACE-Complete
9.6 Solved Exercises
9.7 Exercises
9.8 For Further Reading
10 Extending the Limits of Tractability
10.1 Finding Small Vertex Covers
10.2 Solving NP-hard Problem on Trees
10.3 Coloring a Set of Circular Arcs
10.4 (*) Tree Decompositions of Graphs
10.5 (*) Constructing a Tree Decomposition
10.6 Solved Exercises
10.7 Exercises
10.8 Notes and Further Reading
11 Approximation Algorithms
11.1 Greedy Algorithms and Bounds on the Optimum: A Load Balancing Problem
11.2 The Center Selection Problem
11.3 Set Cover: A General Greedy Heuristic
11.4 The Pricing Method: Vertex Cover
11.5 Maximization via the Pricing method: The Disjoint Paths Problem
11.6 Linear Programming and Rounding: An Application to Vertex Cover
11.7 (*) Load Balancing Revisited: A More Advanced LP Application
11.8 Arbitrarily Good Approximations: the Knapsack Problem
11.9 Solved Exercises
11.10 Exercises
11.11 Notes and Further Reading
12 Local Search
12.1 The Landscape of an Optimization Problem
12.2 The Metropolis Algorithm and Simulated Annealing
12.3 An Application of Local Search to Hopfield Neural Networks
12.4 Maximum Cut Approximation via Local Search
12.5 Choosing a Neighbor Relation
12.6 (*) Classification via Local Search
12.7 Best-Response Dynamics and Nash Equilibria
12.8 Solved Exercises
12.9 Exercises
12.10 Notes and Further Reading
13 Randomized Algorithms
13.1 A First Application: Contention Resolution
13.2 Finding the Global Minimum Cut
13.3 Random Variables and their Expectations
13.4 A Randomized Approximation Algorithm for MAX-3-SAT
13.5 Randomized Divide-and-Conquer: Median-Finding and Quicksort
13.6 Hashing: A Randomized Implementation of Dictionaries
13.7 Finding the Closest Pair of Points: A Randomized Approach
13.8 Randomized Caching
13.9 Chernoff Bounds
13.10 Load Balancing
13.11 (*) Packet Routing
13.12 Background: Some Basic Probability Definitions
13.13 Solved Exercises
13.14 Exercises
13.15 Notes and Further Reading
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