本書是近年來關(guān)于算法設(shè)計(jì)和分析的不可多得的優(yōu)秀教材。本書圍繞算法設(shè)計(jì)技術(shù)組織素材，對每種算法技術(shù)選擇了多個(gè)典型范例進(jìn)行分析。本書將直觀性與嚴(yán)謹(jǐn)性完美地結(jié)合起來。每章從實(shí)際問題出發(fā)，經(jīng)過具體、深入、細(xì)致的分析，自然且富有啟發(fā)性地引出相應(yīng)的算法設(shè)計(jì)思想，并對算法的正確性、復(fù)雜性進(jìn)行恰當(dāng)?shù)姆治觥⒄撟C。本書覆蓋的面較寬，凡屬串行算法的經(jīng)典論題都有涉及，并且論述深入有新意。全書共200多道豐富而精彩的習(xí)題是本書的重要組成部分，也是本書的突出特色之一。
本書適用于本科高年級學(xué)生以及研究生算法課的教材，也很適于具有計(jì)算機(jī)或相近專業(yè)本科水平的人自學(xué)算法的需要。
美國康乃爾大學(xué)計(jì)算機(jī)系教授Jon Kleinberg和éva Tardos合著的《算法設(shè)計(jì)》是最近幾年當(dāng)中關(guān)于算法設(shè)計(jì)和分析的不可多得的優(yōu)秀教材。它適用于本科高年級學(xué)生以及研究生的算法課。它還很適于具有計(jì)算機(jī)或相近專業(yè)本科水平的人自學(xué)算法的需要。
本書將直觀性與嚴(yán)謹(jǐn)性完美地結(jié)合起來。每章從實(shí)際問題出發(fā)，經(jīng)過具體、深入、細(xì)致的分析，自然地富有啟發(fā)性地引出相應(yīng)的算法思想，并對算法的正確性、復(fù)雜性進(jìn)行恰當(dāng)?shù)姆治觥⒄撟C。本書覆蓋的面較寬，凡屬串行算法的經(jīng)典論題都有涉及，并且論述深入有新意。
全書共200多道豐富而精彩的習(xí)題是本書的重要組成部分，也是本書的突出特色之一。而且，每章習(xí)題之前都有幾道精選的給出詳解的例題，這對解答其后的系統(tǒng)極有幫助。
——黃連生 清華大學(xué)計(jì)算機(jī)系
“Algorithm Design”是我看到過的關(guān)于算法設(shè)計(jì)最好的教材之一。
——屈婉玲 北京大學(xué)信息學(xué)院
算法設(shè)計(jì)一書的前8章以及后面若干章節(jié)，構(gòu)成本科生算法設(shè)計(jì)導(dǎo)論課程的基礎(chǔ)。后續(xù)的章節(jié)適合于更高級研究。本書包含200多道有趣簡明的作業(yè)問題?其中一些問題直接來自諸如Yahoo!和Oracle這樣的公司。每個(gè)問題都經(jīng)過測試，表明這些問題的有效性和精確性。
——霍紅衛(wèi) 西安電子科技大學(xué)計(jì)算機(jī)學(xué)院
本書的特色在于努力剖析問題本質(zhì)，分析較透徹，詳盡地分析了問題描述的方式，針對問題在不同情況下的求解方式進(jìn)行深入的闡述。
——宋友 北京航空航天大學(xué)軟件學(xué)院
本書通過實(shí)際問題的求解過程來引入算法設(shè)計(jì)思想和分析方法，對每種技術(shù)選擇了多個(gè)典型范例進(jìn)行分析，使讀者更深入地掌握算法設(shè)計(jì)的理論和技巧，是一本難得的算法教材。
——林永鋼 北京理工大學(xué)計(jì)算機(jī)學(xué)院
目 錄
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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