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Algorithm Design (06 Edition)

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Algorithm Design (06 Edition) Cover

ISBN13: 9780321295354
ISBN10: 0321295358
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Synopses & Reviews

Publisher Comments:

Algorithm Design introduces algorithms by looking at the real-world problems that motivate them. The book teaches students a range of design and analysis techniques for problems that arise in computing applications. The text encourages an understanding of the algorithm design process and an appreciation of the role of algorithms in the broader field of computer science.

August 6, 2009 Author, Jon Kleinberg, was recently cited in the New York Times for his statistical analysis research in the Internet age.

Book News Annotation:

Intended for computer science students who have completed the CS1/CS2 sequence, this textbook introduces greedy algorithms, divide and conquer, dynamic programming, network flow, NP-complete problems, and three techniques for dealing with computationally intractable problems: identification of structured special cases, approximation algorithms, and local search heuristics. The authors are professors at Cornell University.
Annotation 2005 Book News, Inc., Portland, OR (booknews.com)

Book News Annotation:

Intended for computer science students who have completed the CS1/CS2 sequence, this textbook introduces greedy algorithms, divide and conquer, dynamic programming, network flow, NP-complete problems, and three techniques for dealing with computationally intractable problems: identification of structured special cases, approximation algorithms, and local search heuristics. The authors are professors at Cornell University. Annotation ©2005 Book News, Inc., Portland, OR (booknews.com)

Table of Contents

Algorithm Design

Jon Kleinberg and Eva Tardos

Table of Contents

1 Introduction: Some Representative Problems  

    1.1 A First Problem: Stable Matching  

    1.2 Five Representative Problems  

          Solved Exercises

          Excercises

          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

          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 Bipartiteness: 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 the Problem of Data Compression

   *4.9 Minimum-Cost Arborescences: A Multi-Phase Greedy Algorithm  

          Solved Exercises

          Excercises

          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

          Solved Exercises

          Exercises

          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  

            Solved Exercises

            Exercises

            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  

            Solved Exercises

            Exercises

            Notes and Further Reading

 

8 NP and Computational Intractability  

   8.1 Polynomial-Time Reductions  

   8.2 Reductions via "Gadgets": The Satisfiability 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 Problem 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

 

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

             Solved Exercises

             Exercises

             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

               Solved Exercises

               Exercises

               Notes and Further Reading

 

Epilogue: Algorithms that Run Forever 

References

Index

What Our Readers Are Saying

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naveensao_mscit, October 28, 2008 (view all comments by naveensao_mscit)
it is very good book if you want to design algorithms then definately read this one
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Product Details

ISBN:
9780321295354
Author:
Kleinberg, Jon
Publisher:
Addison-Wesley Professional
Author:
Tardos, Iva
Author:
Cram101 Textbook Reviews
Author:
Kleinberg, Jon
Author:
Tardos, Eva
Author:
Tardos, va
Subject:
Programming - Algorithms
Subject:
Software Engineering-Algorithms
Subject:
Education-General
Copyright:
Publication Date:
March 2005
Binding:
HARDCOVER
Grade Level:
College/higher education:
Language:
English
Illustrations:
Y
Pages:
864
Dimensions:
9.4 x 8.3 x 1.3 in 1424 gr

Related Subjects

Computers and Internet » Artificial Intelligence » General
Computers and Internet » Computers Reference » General
Computers and Internet » Software Engineering » Algorithms

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