Synopses & Reviews
In this updated fifth edition, software engineers will find accessible coverage of fundamental data structures using a consistent object-oriented framework. The discussions throughout the book now feature the latest information on Java JDK 1.6. This includes updates to sections on Java programming basics, arrays, stacks and queues, lists, priority queues, sorting methods, maps, and memory. The coverage of the analysis language has been simplified and more examples of algorithm analysis are presented. Java implementations are provided of fundamental algorithms and of sample applications of data structures. In addition, software engineers will find all the Java source code, Java animations, and interactive applets for data structures and algorithms, and an educational version of the net.datastructures package on the books Web site.
Synopsis
* This newest edition examines fundamental data structures by following a consistent object-oriented framework that builds intuition and analysis skills of data structures and algorithms
* Presents new figures, simpler language, and more practical motivations from real-world scenarios
* Numerous illustrations, Web-based animations, and simplified mathematical analyses help readers quickly learn important concepts
Please find missing images here:
http://bcs.wiley.com/he-bcs/Books?action=resource&bcsId=5449&itemId=0470383267&resourceId=20377
Synopsis
* This newest edition examines fundamental data structures by following a consistent object-oriented framework that builds intuition and analysis skills of data structures and algorithms
* Presents new figures, simpler language, and more practical motivations from real-world scenarios
* Numerous illustrations, Web-based animations, and simplified mathematical analyses help readers quickly learn important concepts
About the Author
Professors Goodrich and Tamassia are well-recognized researchers in data structures and algorithms. Michael Goodrich received his Ph.D. in Computer Science from Purdue University. He is currently a professor in the Department of Computer Science at the University of California, Irvine. Roberto Tamassia received his Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign. He is currently a professor in the Department Science at Brown University. Both professors are winners of numerous teaching awards for their teaching of data structures and algorithms.
Table of Contents
1 Java Primer.1.1 Getting Started: Classes, Types, and Objects.
1.2 Methods.
1.3 Expressions.
1.4 Control Flow.
1.5 Arrays.
1.6 Simple Input and Output.
1.7 An Example Program.
1.8 Nested Classes and Packages.
1.9 Writing a Java Program.
1.10 Exercises.
2 Object-Oriented Design.
2.1 Goals, Principles, and Patterns.
2.2 Inheritance and Polymorphism.
2.3 Exceptions.
2.4 Interfaces and Abstract Classes.
2.5 Casting and Generics.
2.6 Exercises.
3 Indices, Nodes, and Recursion.
3.1 Using Arrays.
3.2 Singly Linked Lists.
3.3 Doubly Linked Lists.
3.4 Circularly Linked Lists and Linked-List Sorting.
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 Array Lists.
6.2 Node Lists.
6.3 Iterators.
6.4 List ADTs and the Collections Framework.
6.5 Case Study: The Move-to-Front Heuristic.
6.6 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 an 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.