Sedgewick has a real gift for explaining concepts in a way that makes them easy to understand. The use of real programs in page-size (or less) chunks that can be easily understood is a real plus. The figures, programs, and tables are a significant contribution to the learning experience of the reader; they make this book distinctive.
--William A. Ward, University of South Alabama

This edition of Robert Sedgewick's popular work provides current and comprehensive coverage of important algorithms for Java programmers. Michael Schidlowsky and Sedgewick have developed new Java implementations that both express the methods in a concise and direct manner and provide programmers with the practical means to test them on real applications.

Many new algorithms are presented, and the explanations of each algorithm are much more detailed than in previous editions. A new text design and detailed, innovative figures, with accompanying commentary, greatly enhance the presentation. The third edition retains the successful blend of theory and practice that has made Sedgewick's work an invaluable resource for more than 400,000 programmers!

This particular book, Parts 1-4, represents the essential first half of Sedgewick's complete work. It provides extensive coverage of fundamental data structures and algorithms for sorting, searching, and related applications. Although the substance of the book applies to programming in any language, the implementations by Schidlowsky and Sedgewick also exploit the natural match between Java classes and abstract data type (ADT) implementations.

Highlights

• Java class implementations of more than 100 important practical algorithms
• Emphasis on ADTs, modular programming, and object-oriented programming
• Extensive coverage of arrays, linked lists, trees, and other fundamental data structures
• Thorough treatment of algorithms for sorting, selection, priority queue ADT implementations, and symbol table ADT implementations (search algorithms)
• Complete implementations for binomial queues, multiway radix sorting, randomized BSTs, splay trees, skip lists, multiway tries, B trees, extendible hashing, and many other advanced methods
• Quantitative information about the algorithms that gives you a basis for comparing them
• More than 1,000 exercises and more than 250 detailed figures to help you learn properties of the algorithms

Whether you are learning the algorithms for the first time or wish to have up-to-date reference material that incorporates new programming styles with classic and new algorithms, you will find a wealth of useful information in this book.

Robert Sedgewick is the William O. Baker Professor of Computer Science at Princeton University. He is a Director of Adobe Systems and has served on the research staffs at Xerox PARC, IDA, and INRIA. He earned his Ph.D from Stanford University under Donald E. Knuth.

I. FUNDAMENTALS.

1. Introduction.

Algorithms.

A Sample Problem: Connectivity.

Union-Find Algorithms.

Perspective.

Summary of Topics.

2. Principles of Algorithm Analysis.

Implementation and Empirical Analysis.

Analysis of Algorithms.

Growth of Functions.

Big-Oh notation.

Basic Recurrences.

Examples of Algorithm Analysis.

Guarantees, Predictions, and Limitations.

II. DATA STRUCTURES.

3. Elementary Data Structures.

Building Blocks.

Arrays.

Linked Lists.

Elementary List Processing.

Memory Allocation for Lists.

Strings.

Compound Data Structures.

4. Abstract Data Types.

Collections of Items.

Pushdown Stack ADT.

Examples of Stack ADT Clients.

Stack ADT Implementations.

Generic Implementations.

Creation of a New ADT.

FIFO Queues and Generalized Queues.

Duplicate and Index Items.

First-Class ADTs.

Application-Based ADT Example.

Perspective.

5. Recursion and Trees.

Recursive Algorithms.

Divide and Conquer.

Dynamic Programming.

Trees.

Mathematical Properties of Trees.

Tree Traversal.

Recursive Binary-Tree Algorithms.

Graph Traversal.

Perspective.

III. SORTING.

6. Elementary Sorting Methods.

Rules of the Game.

Generic Sort Implementations.

Selection Sort.

Insertion Sort.

Bubble Sort.

Performance Characteristics of Elementary Sorts.

Algorithm Visualization.

Shellsort.

Sorting Linked Lists.

Key-Indexed Counting.

7. Quicksort 315.

The Basic Algorithm.

Performance Characteristics of Quicksort.

Stack Size.

Small Subfiles.

Median-of-Three Partitioning.

Duplicate Keys.

Strings and Vectors.

Selection.

8. Merging and Mergesort.

Two-Way Merging.

Abstract In-Place Merge.

Top-Down Mergesort.

Improvements to the Basic Algorithm.

Bottom-Up Mergesort.

Performance Characteristics of Mergesort.

Linked-List Implementations of Mergesort.

Recursion Revisited.

9. Priority Queues and Heapsort.

Elementary Implementations.

Heap Data Structure.

Algorithms on Heaps.

Heapsort.

Priority-Queue ADT.

Priority Queues for Index Items.

Binomial Queues.

10. Radix Sorting.

Bits, Bytes, and Words.

Binary Quicksort.

MSD Radix Sort.

Three-Way Radix Quicksort.

LSD Radix Sort.

Performance Characteristics of Radix Sorts.

Sublinear-Time Sorts.

11. Special-Purpose Sorts.

Batcher's Odd-Even Mergesort.

Sorting Networks.

Sorting In Place.

External Sorting.

Sort-Merge Implementations.

Parallel Sort-Merge.

IV. SEARCHING.

12. Symbol Tables and BSTs.

Symbol-Table Abstract Data Type.

Key-Indexed Search.

Sequential Search.

Binary Search.

Index Implementations with Symbol Tables.

Binary Search Trees.

Performance Characteristics of BSTs.

Insertion at the Root in BSTs.

BST Implementations of Other ADT Functions.

13. Balanced Trees.

Randomized BSTs.

Splay BSTs.

Top-Down 2-3-4 Trees.

Red-Black Trees.

Skip Lists.

Performance Characteristics.

14. Hashing.

Hash Functions.

Separate Chaining.

Linear Probing.

Double Hashing.

Dynamic Hash Tables.

Perspective.

15. Radix Search.

Digital Search Trees.

Tries.

Patricia Tries.

Multiway Tries and TSTs.

Text-String-Index Applications.

16. External Searching.

Rules of the Game.

Indexed Sequential Access.

B Trees.

Extendible Hashing.

Perspective.

Appendix.

Index. 0201361205T06262002