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Algorithms in Java, Parts 1-4, 3rd Edition

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Description

  • Copyright 2003
  • Dimensions: 7-3/4" x 9-1/4"
  • Pages: 768
  • Edition: 3rd
  • Book
  • ISBN-10: 0-201-36120-5
  • ISBN-13: 978-0-201-36120-9
  • eBook (Watermarked)
  • ISBN-10: 0-672-33371-6
  • ISBN-13: 978-0-672-33371-2

For the first time, Sedgewick's seminal work on algorithms and data structures is available with implementations in Java. Michael Schidlowsky and Sedgewick have developed new Java code that both expresses the methods in a concise and direct manner, and also provides programmers with the practical means to test them on real applications. This particular book, Parts 1-4, represents the essential first half of Sedgewick's complete work. Its four parts are fundamentals, data structures, sorting, and searching. It has expanded coverage of arrays, linked lists, strings, trees, ADT's, and object-oriented programming.

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Table of Contents

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

Preface

This book is the first of three volumes that are intended to survey the most important computer algorithms in use today. This first volume (Parts 14) covers fundamental concepts (Part 1), data structures (Part 2), sorting algorithms (Part 3), and searching algorithms (Part 4); the (yet to be published) second volume (Part 5) covers graphs and graph algorithms; and the (yet to be published) third volume (Parts 68) covers strings (Part 6), computational geometry (Part 7), and advanced algorithms and applications (Part 8).

The books are useful as texts early in the computer science curriculum, after students have acquired basic programming skills and familiarity with computer systems, but before they have taken specialized courses in advanced areas of computer science or computer applications. The books also are useful for self-study or as a reference for people engaged in the development of computer systems or applications programs because they contain implementations of useful algorithms and detailed information on these algorithms performance characteristics. The broad perspective taken makes the series an appropriate introduction to the field.

Together the three volumes comprise the Third Edition of a book that has been widely used by students and programmers around the world for many years. I have completely rewritten the text for this edition, and I have added thousands of new exercises, hundreds of new figures, dozens of new programs, and detailed commentary on all the figures and programs. This new material provides both coverage of new topics and fuller explanations of many of the classic algorithms. A new emphasis on abstract data types throughout the books makes the programs more broadly useful and relevant in modern object-oriented programming environments. People who have read previous editions will find a wealth of new information throughout; all readers will find a wealth of pedagogical material that provides effective access to essential concepts.

These books are not just for programmers and computer science students. Everyone who uses a computer wants it to run faster or to solve larger problems. The algorithms that we consider represent a body of knowledge developed during the last 50 years that is the basis for the efficient use of the computer for a broad variety of applications. From N-body simulation problems in physics to genetic-sequencing problems in molecular biology, the basic methods described here have become essential in scientific research; and from database systems to Internet search engines, they have become essential parts of modern software systems. As the scope of computer applications becomes more widespread, so grows the impact of basic algorithms, particularly the fundamental graph algorithms covered in this volume. The goal of this book is to serve as a resource so that students and professionals can know and make intelligent use of graph algorithms as the need arises in whatever computer application they might undertake.

Scope

This book, Algorithms in Java, Third Edition, Parts 1-4, contains 16 chapters grouped into four major parts: fundamentals, data structures, sorting, and searching. The descriptions here are intended to give readers an understanding of the basic properties of as broad a range of fundamental algorithms as possible. The algorithms described here have found widespread use for years, and represent an essential body of knowledge for both the practicing programmer and the computerscience student. The second volume is devoted to graph algorithms, and the third consists of four additional parts that cover strings, geometry, and advanced topics. My primary goal in developing these books has been to bring together fundamental methods from diverse areas, to provide access to the best methods known for solving problems by computer.

You will most appreciate the material here if you have had one or two previous courses in computer science or have had equivalent programming experience: one course in programming in a high-level language, such as Java, C, or C++, and perhaps another course that teaches fundamental concepts of programming systems. This book is thus intended for anyone conversant with a modern programming language and with the basic features of modern computer systems. References that might help to fill in gaps in your background are suggested in the text. Most of the mathematical material supporting the analytic results is self-contained (or is labeled as beyond the scope of this book), so little specific preparation in mathematics is required for the bulk of the book, although mathematical maturity is definitely helpful.

Use in the Curriculum

There is a great deal of flexibility in how the material here can be taught, depending on the taste of the instructor and the preparation of the students. The algorithms described have found widespread use for years, and represent an essential body of knowledge for both the practicing programmer and the computer science student. There is sufficient coverage of basic material for the book to be used in a course on data structures and algorithms, and there is sufficient detail and coverage of advanced material for the book to be used for a course on graph algorithms. Some instructors may wish to emphasize implementations and practical concerns; others may wish to emphasize analysis and theoretical concepts.

An elementary course on data structures and algorithms might emphasize the basic data structures in Part Two and their use in the implementations in Parts Three and Four. A course on design and analysis of algorithms might emphasize the fundamental material in Part One and Chapter 5, then study the ways in which the algorithms in Parts Three and Four achieve good asymptotic performance. A course on software engineering might omit the mathematical and advanced algorithmic material, and emphasize how to integrate the implementations given here into large programs or systems. A course on algorithms might take a survey approach and introduce concepts from all these areas.

Earlier editions of this book that are based on other programming languages have been used at scores of colleges and universities as a text for the second or third course in computer science and as supplemental reading for other courses. At Princeton, our experience has been that the breadth of coverage of material in this book provides our majors with an introduction to computer science that can be expanded on in later courses on analysis of algorithms, systems programming, and theoretical computer science, while providing the growing group of students from other disciplines with a large set of techniques that these people can put to good use immediately.

The exercises--nearly all of which are new to this edition--fall into several types. Some are intended to test understanding of material in the text, and simply ask readers to work through an example or to apply concepts described. Others involve implementing and putting together the algorithms, or running empirical studies to compare variants of the algorithms and to learn their properties. Still others are a repository for important information at a level of detail that is not appropriate for the text. Reading and thinking about the exercises will pay dividends for every reader.

Algorithms of Practical Use

Anyone wanting to use a computer more effectively can use this book for reference or for self-study. People with programming experience can find information on specific topics throughout the book. To a large extent, you can read the individual chapters in the book independently of the others, although, in some cases, algorithms in one chapter make use of methods from a previous chapter.

The orientation of the book is to study algorithms likely to be of practical use. The book provides information about the tools of

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