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More About This Title Data Structures and Algorithms in Java 4/e
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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.
In addition to their research accomplishments, the authors also have extensive experience in the classroom. For example, Dr. Goodrich has taught data structures and algorithms courses, including Data Structures as a freshman-sophomore level course and Introduction to Algorithms as an upper level course. He has earned several teaching wards in this capacity. His teaching style is to involve the students in lively interactive classroom session that bring out the intuition and insights behind data structuring and algorithmic techniques. Dr. Tamassia has taught Data Structures and Algorithms as an introductory freshman-level course since 1988. One thing that has set his teaching style apart is his effective use of interactive hypermedia presentations integrated with the Web.
This instructional Web sites, datastructures.net and algorithmdesign.net, supported by Drs. Goodrich and Tamassia, are used as reference material by students, teachers, and professionals worldwide.
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2. Object-Oriented Design.
3. Arrays, Linked Lists, and Recursion.
4. Analysis Tools.
5. Stacks and Queues.
6. Lists and Iterators.
7. Trees.
8. Priority Queues.
9. Maps and Dictionaries.
10. Search Trees.
11. Sorting, Sets, Selection.
12. Text Processing.
13. Graphs.
14. Memory.
Appendix: Useful Mathematical Facts.
Bibliography.
Index.