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Are you looking for something different in your Algorithms text? Are you looking for an Algorithms text that offers theoretical analysis techniques as well as design patterns and experimental methods for the engineering of algorithms? Michael Goodrich and Roberto Tamassia, authors of the successful, Data Structures and Algorithms in Java, 2/e, have written Algorithm Design, a text designed to provide a comprehensive introduction to the design, implementation and analysis of computer algorithms and data structures from a modern perspective.Written for an undergraduate, junior-senior algorithms course this text offers several implementation case studies and uses Internet applications to motivate many topics such as hashing, sorting and searching.
Michael T. Goodrich is a mathematician and computer scientist. He is a Chancellor's Professor and the chair of Department of Computer Science, of Donald Bren School of Information and Computer Sciences, a school of University of California, Irvine. Roberto Tamassia is the author of Algorithm Design: Foundations, Analysis, and Internet Examples, published by Wiley.
I Fundamental Tools 11 Algorithm Analysis 31.1 Methodologies for Analyzing Algorithms 51.2 Asymptotic Notation 131.3 A Quick Mathematical Review 211.4 Case Studies in Algorithm Analysis 311.5 Amortization 341.6 Experimentation 421.7 Exercises 472 Basic Data Structures 552.1 Stack sand Queues 572.2 Vectors, Lists, and Sequences 652.3 Trees 752.4 Priority Queues and Heaps 942.5 Dictionaries and Hash Tables 1142.6 Java Example: Heap 1282.7 Exercises 1313 Search Trees and Skip Lists 1393.1 Ordered Dictionaries and Binary Search Trees 1413.2 AVL Trees 1523.3 Bounded-Depth Search Trees 1593.4 Splay Trees 1853.5 Sk i p Lists 1953.6 Java Example: AVL and Red-Black Trees 2023.7 Exercises 2124 Sorting, Sets, and Selection 2174.1 Merge-Sort 2194.2 The Set Abstract Data Type 2254.3 Quick -Sort 2354.4 A Lower Bound on Comparison-Based Sorting 2394.5 Buck et-Sort and Radix-Sort 2414.6 Comparison of Sorting Algorithms 2444.7 Selection 2454.8 Java Example: In-Place Quick -Sort 2484.9 Exercises 2515 Fundamental Techniques 2575.1 The GreedyMethod 2595.2 Divide-and-Conquer 2635.3 Dynamic Programming 2745.4 Exercises 282II Graph Algorithms 2856 Graphs 2876.1 The Graph Abstract Data Type 2896.2 Data Structures for Graphs 2966.3 Graph Traversal 3036.4 Directed Graphs 3166.5 Java Example: Depth-First Search 3296.6 Exercises 3357 Weighted Graphs 3397.1 Single-Source Shortest Paths 3417.2 All-Pairs Shortest Paths 3547.3 Minimum Spanning Trees 3607.4 Java Example: Dijk stra’s Algorithm 3737.5 Exercises 3768 Network Flow and Matching 3818.1 Flows and Cuts 3838.2 Maximum Flow 3878.3 Maximum BipartiteMatching 3968.4 Minimum-Cost Flow 3988.5 Java Example: Minimum-Cost Flow 4058.6 Exercises 412III Internet Algorithmics 4159 Text Processing 4179.1 Strings and PatternMatching Algorithms 4199.2 Tries 4299.3 Text Compression 4409.4 Text Similarity Testing 4439.5 Exercises 44710 Number Theory and Cryptography 45110.1 Fundamental Algorithms Involving Numbers 45310.2 Cryptographic Computations 47110.3 Information Security Algorithms and Protocols 48110.4 The Fast Fourier Transform 48810.5 Java Example: FFT 50010.6 Exercises 50811 Network Algorithms 51111.1 ComplexityMeasures and Models 51311.2 Fundamental Distributed Algorithms 51711.3 Broadcast and Unicast Routing 53011.4 Multicast Routing 53511.5 Exercises 541IV Additional Topics 54512 Computational Geometry 54712.1 Range Trees 54912.2 Priority Search Trees 55612.3 Quadtrees and k-D Trees 56112.4 The Plane Sweep Technique 56512.5 Convex Hulls 57212.6 Java Example: Convex Hull 58312.7 Exercises 58713 NP-Completeness 59113.1 P and NP 59313.2 NP-Completeness 59913.3 Important NP-Complete Problems 60313.4 Approximation Algorithms 61813.5 Back track i ng and Branch-and-Bound 62713.6 Exercises 63814 Algorithmic Frameworks 64314.1 External-Memory Algorithms 64514.2 Parallel Algorithms 65714.3 Online Algorithms 66714.4 Exercises 680A Useful Mathematical Facts 685Bibliography 689Index 698