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.

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