The 2/e offers an innovative approach to data structures and algorithms by incorporating the object-oriented design paradigm using C++. Takes highly visual approach and extensive suite of Web-based learning giving students the opportunity to see visual justifications of key analytic concepts.
The 2/e offers an innovative approach to data structures and algorithms by incorporating the object-oriented design paradigm using C++. Takes highly visual approach and extensive suite of Web-based learning giving students the opportunity to see visual justifications of key analytic concepts.
Michael Goodrich received his Ph.D. in computer science from Purdue University in 1987. He is currently a professor in the Department of Computer Science at University of California, Irvine. Previously, he was a professor at Johns Hopkins University. He is an editor for the International Journal of Computational Geometry & Applications and Journal of Graph Algorithms and Applications. Roberto Tamassia received his Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign in 1988. He is currently a professor in the Department of Computer Science at Brown University. He is editor-in-chief for the Journal of Graph Algorithms and Applications and an editor for Computational Geometry: Theory and Applications. He previously served on the editorial board of IEEE Transactions on Computers.
Inhaltsangabe
1. A C++ Primer. 1.1 Basic C++ Programming Elements. 1.2 Expressions. 1.3 Control Flow. 1.4 Functions. 1.5 Classes. 1.6 C++ Program and File Organization. 1.7 Writing a C++ Program. 1.8 Exercises. 2. Object-Oriented Design. 2.1 Goals, Principles, and Patterns. 2.2 Inheritance and Polymorphism. 2.3 Templates. 2.4 Exceptions. 2.5 Exercises. 3. Arrays, Linked Lists, and Recursion. 3.1 Using Arrays. 3.2 Singly Linked Lists. 3.3 Doubly Linked Lists. 3.4 Circularly Linked and List Reversal. 3.5 Recursion. 3.6 Exercises. 4. Analysis Tools. 4.1 The Seven Functions Used in This Book. 4.2 Analysis of Algorithms. 4.3 Simple Justification Techniques. 4.4 Exercises. 5. Stacks, Queues, and Deques. 5.1 Stacks. 5.2 Queues. 5.3 Double-Ended Queues. 5.4 Exercises. 6. List and Iterator ADTs. 6.1 Vectors. 6.2 Lists. 6.3 Sequences. 6.4 Case Study: Bubble-Sort on a Sequence. 6.5 Exercises. 7. Trees. 7.1 General Trees. 7.2 Tree Traversal Algorithms. 7.3 Binary Trees. 7.4 Exercises. 8. Heaps and Priority Queues. 8.1 The Priority Queue Abstract Data Type. 8.2 Implementing a Priority Queue with a List. 8.3 Heaps. 8.4 Adaptable Priority Queues. 8.5 Exercises. 9. Hash Tables, Maps, and Skip Lists. 9.1 Maps. 9.2 Hash Tables. 9.3 Ordered Maps. 9.4 Skip Lists. 9.5 Dictionaries. 9.6 Exercises. 10. Search Trees. 10.1 Binary Search Trees. 10.2 AVL Trees. 10.3 Splay Trees. 10.4 (2,4) Trees. 10.5 Red-Black Trees. 10.6 Exercises. 11. Sorting, Sets, and Selection. 11.1 Merge-Sort. 11.2 Quick-Sort. 11.3 Studying Sorting through and Algorithmic Lens. 11.4 Sets and Union/Find Structures. 11.5 Selection. 11.6 Exercises. 12. Strings and Dynamic Programming. 12.1 String Operations. 12.2 Dynamic Programming. 12.3 Pattern Matching Algorithms. 12.4 Text Compression and the Greedy Method. 12.5 Tries. 12.6 Exercises. 13. Graph Algorithms. 13.1 Graphs. 13.2 Data Structures for Graphs. 13.3 Graph Traversals. 13.4 Directed Graphs. 13.5 Shortest Paths. 13.6 Minimum Spanning Trees. 13.7 Exercises. 14. Memory Management and B-Trees. 14.1 Memory Management. 14.2 External Memory and Caching. 14.3 External Searching and B-Trees. 14.4 External-Memory Sorting. 14.5 Exercises. A Useful Mathematical Facts. Bibliography. Index.
