算法设计

出版时间：2006-1  出版社：清华大学出版社  作者：[美]克菜因伯格（KleinbergJ.）évaTardos  页数：838  
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内容概要

　　《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。《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。《算法设计》(影印版)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。《算法设计》(影印版)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《算法设计》(影印版)的重要组成部分，也是《算法设计》(影印版)的突出特色之一。

作者简介

　　Jon Kleinberg is a professor of Computer Science at Cornell
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 Outstanding Innovation Award, the National Academy of
Sciences Award for Initiatives in Research, research fellowships
from the Packard and Sloan Foundations, and teaching awards from
the Cornell Engineering College and Computer Science
Department.
　　Kleinberg's research is centered around algorithms, particularly
those concerned with the structure of networks and information, and
with applications to information science, optimization, data
mining, and computational biology. His work on network analysis
using hubs and authorities helped form the foundation for the
current generation of Intern

书籍目录

About the Authors
Preface
Introduction: Some Representative Problems
1.1 A First Problem: Stable Matching
1.2 Five Representative Problems
Solved Exercises
Exercises
Notes and Further Reading
Basics of Algorithm Ana/ys/s
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 D/v/de and Corn/net
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
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 via Divide and Conquer
6.8 Shortest Paths in a Graph
6.9 Shortest Paths and Distance Vector Protocols
* 6.10 Negative Cycles in a Graph
Solved Exercises
Exercises
Notes and Further Reading
Network Flora
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
* 7.1.3 A Further Direction: Adding Costs to the Matching Problem
Solved Exercises
Exercises
Notes and Further Reading
NP and Computational Intractability
8.1 Polynomial-Time Reductions
8.2 Reductions via "Gadgets": The Safisfiability Problem
8.3 Efficient Certification and the Definition of NP
8.4 NP-Complete Problems
8.5 Sequencing Problems
8.6 Partitioning Problems
8.7 Graph Coloring
8.8 Numerical Problems
8.9 Co-NP and the Asymmetry of NP
8.10 A Partial Taxonomy of Hard Problems
Solved Exercises
Exercises
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
Solved Exercises
Exercises
Notes and Further Reading
10 Extending the Limits of Tractability
10.1 Finding Small Vertex Covers
10.2 Solving NP-Hard Problems on Trees
10.3 Coloring a Set of Circular Arcs
* 10.4 Tree Decompositions of Graphs
* 10.5 Constructing a Tree Decomposition
Solved Exercises
Exercises
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
Solved Exercises
Exercises
Notes and Further Reading
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
Solved Exercises
Exercises
Notes and Further Reading
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
Solved Exercises
Exercises
Notes and Further Reading
Epilogue: Algorithms That Run Forever
References
Index

章节摘录

版权页：   插图：   2.5 A More Complex Data Structure:Priority Queues Our primary goal in this book was expressed at the outset of the chapter:we seek algorithms that improve qualitatively on brute-force search,and in general we use polynomial-time solvability as the concrete formulation of this."pically,achieving a polynomial-time solution to a nontrivial problem is not something that depends on fine-grained implementation details; rather,the difference between exponential and polynomial is based on overcoming higher-level obstacles.Once one has an efficient algorithm to solve a problem,however,it is often possible to achieve further improvements in running time by being careful with the implementation details,and sometimes by using more complex data structures. Some complex data structures are essentially tailored for use in a single kind of algorithm,while others are more generally applicable.In this section,we describe one of the most broadly useful sophisticated data structures,the priority queue.Priority queues will be useful when we describe how to implement some of the graph algorithms developed later in the book.For our purposes here,it is a useful illustration of the analysis of a data structure that,unlike lists and arrays,must perform some nontrivial processing,each time it is invoked. The Problem In the implementation of the Stable Matching algorithm in Section 2.3,we discussed the need to maintain a dynamically changing set S (such as the set of all free men in that case).In such situations,we want to be able to add elements to and delete elements from the set S,and we want to be able to select an element from S when the algorithm calls for it.A priority queue is designed for applications in which elements have a priority value,or key,and each time we need to select an element from S,we want to take the one with highest priority.

编辑推荐

《算法设计》(影印版)为英文原版教材，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。    《算法设计》(影印版)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。

用户评论 (总计13条)

•   纸张质量明显不如第一次印刷。强烈建议在书的说明上标出是第多少次印刷。我发现一个规律：在国内，即使是正规的出版社的正规出版物，在多次印刷之后，纸张和墨的质量都会下降。
•   刚看了一点，感觉不错。可是英文版的看起来太慢了，经常看了下句忘了上句，愁！
•   卓越买的书没失望过。我是单从书的质量上讲啊。不是内容。至于内容，先保留评价。
•   书质量很好,字体清晰,
•   针对本科的算法学习。系统地讲述了提出问题、分析问题、设计算法、分析算法的完整的解决问题的思路，体现了严密的逻辑，侧重于解决问题能力的培养，而不是像本国教材一样侧重于讲授算法。
•   英文影印的，经典，还可以，希望能多推荐一些计算机有关的书
•   很实用 学会各种算法的必备书
•   跟<算法导论>配套着看.
•   哎，搬了个地方送货速度就慢下来了，急着用，也就不换了，，，书都磨损了。
•   一般的影印版，还行吧。。一般的影印版，还行吧。。
•   第一章的婚姻匹配问题把我吸引了，之后爱不释手
•   作者是网页排序算法HITS的发明者，网络流那章讲的比较好，其它的写的都挺不错的，不过不建议初学者选这本书。
•   对算法的讲解深入浅出，让你知道算法设计的前因后果，是一本好书！