1 Introduction: Some Representative Problems/引言：某些有代表性的问题
1.1 A First Problem: Stable Matching/第 一个问题：稳定匹配
1.2 Five Representative Problems/五个有代表性的问题
Solved Exercises/带解答的练习
Exercises /练习
Notes and Further Reading/注释和进一步阅读
2 Basics of Algorithm Analysis/算法分析基础
2.1 Computational Tractability/计算可解性
2.2 Asymptotic Order of Growth/增长的渐近阶
2.3 Implementing the Stable Matching Algorithm Using Lists and Arrays/用列表和数组实现稳定匹配算法
2.4 A Survey of Common Running Times/常用运行时间概述
2.5 A More Complex Data Structure: Priority Queues/更复杂的数据结构：优先队列
Solved Exercises/带解答的练习
Exercises /练习
Notes and Further Reading/注释和进一步阅读
3 Graphs/图
3.1 Basic Definitions and Applications/基本定义与应用
3.2 Graph Connectivity and Graph Traversal/图的连通性与图的遍历
3.3 Implementing Graph Traversal Using Queues and Stacks/用优先队列与栈实现图的遍历
3.4 Testing Bipartiteness: An Application of Breadth-First Search/二分性测试：宽度优先搜索的应用
3.5 Connectivity in Directed Graphs/有向图中的连通性
3.6 Directed Acyclic Graphs and Topological Ordering/有向无环图与拓扑排序
Solved Exercises/带解答的练习
Exercises /练习
Notes and Further Reading/注释和进一步阅读
4 Greedy Algorithms/贪心算法
4.1 Interval Scheduling: The Greedy Algorithm Stays Ahead/区间调度：贪心算法领先
4.2 Scheduling to Minimize Lateness: An Exchange Argument/最小延迟调度：交换论证
4.3 Optimal Caching: A More Complex Exchange Argument/最优高速缓存：更复杂的交换论证
4.4 Shortest Paths in a Graph/图的最短路径
4.5 The Minimum Spanning Tree Problem/最小生成树问题
4.6 Implementing Kruskal’s Algorithm: The Union-Find Data Structure/实现Kruskal算法：Union-Find数据结构
4.7 Clustering/聚类
4.8 Huffman Codes and Data Compression/赫夫曼码与数据压缩
4.9 Minimum-Cost Arborescences: A Multi-Phase Greedy Algorithm/最小费用有向树：多阶段贪心算法
Solved Exercises/带解答的练习
Exercises /练习
Notes and Further Reading/注释和进一步阅读
5 Divide and Conquer/分治策略
5.1 A First Recurrence: The Mergesort Algorithm/第 一个递推式：归并排序算法
5.2 Further Recurrence Relations/更多的递推关系
5.3 Counting Inversions/计数逆序
5.4 Finding the Closest Pair of Points/找最接邻近的点对
5.5 Integer Multiplication/整数乘法
5.6 Convolutions and the Fast Fourier Transform/卷积与快速傅里叶变换
Solved Exercises/带解答的练习
Exercises /练习
Notes and Further Reading/注释和进一步阅读
6 Dynamic Programming/动态规划
6.1 Weighted Interval Scheduling: A Recursive Procedure/带权的区间调度：递归过程
6.2 Principles of Dynamic Programming: Memoization or Iteration over Subproblems/动态规划原理：备忘录或者子问题迭代
6.3 Segmented Least Squares: Multi-way Choices/分段的最小二乘：多重选择
6.4 Subset Sums and Knapsacks: Adding a Variable/子集和与背包：加一个变量
6.5 RNA Secondary Structure: Dynamic Programming over Intervals/RNA二级结构：在区间上的动态规划
6.6 Sequence Alignment/序列比对
6.7 Sequence Alignment in Linear Space via Divide and Conquer/通过分治策略在线性空间的序列比对
6.8 Shortest Paths in a Graph/图中的最短路径
6.9 Shortest Paths and Distance Vector Protocols/最短路径和距离向量协议
6.10 Negative Cycles in a Graph/图中的负圈
Solved Exercises/带解答的练习
Exercises /练习
Notes and Further Reading/注释和进一步阅读
7 Network Flow/网络流
7.1 The Maximum-Flow Problem and the Ford-Fulkerson Algorithm/最大流问题与Ford-Fulkerson算法
7.2 Maximum Flows and Minimum Cuts in a Network/网络中的最大流与最小割
7.3 Choosing Good Augmenting Paths/选择好的增广路径
7.4 The Preflow-Push Maximum-Flow Algorithm/前向流推动最大流算法
7.5 A First Application: The Bipartite Matching Problem/第 一个应用：二分匹配问题
7.6 Disjoint Paths in Directed and Undirected Graphs/有向与无

这是一本关于算法设计和分析的教材。本书围绕算法设计进行组织，对每种算法技术选择了多个典型范例进行分析，把算法的理论跟实际存在的问题结合起来，具有很大的启发性。本书侧重算法设计思路，不再赘述算法复杂度的分析，每章都从实际问题出发，经过深入的具体分析引出相应的算法的设计思想，并对算法的正确性和复杂性进行合理的分析和论证。本书覆盖面很宽，且含有200多道精彩的习题，还扩展了PSPACE问题、参数复杂性等内容。
本书适合作为计算机及相近专业本科高年级学生以及研究生算法课程的参考教材，也适合作为对信息学奥林匹克竞赛感兴趣的高中生的指导书籍。对算法分析和设计感兴趣的IT专业技术人员也可以将本书作为案头必备的参考书或工程实践手册。