Algorithms

About

Features

• Numerous algorithm traces throughout the book.

  • Enables students to check their understanding of the algorithm.

• Over 1,000 end-of-section exercises—With answers to 1/3 of them in the back of the book.

  • Helps students practice solving problems.

• More applications than other algorithms texts.

  • Provides students with applications to computer science to motivate the material.

• Elaborate world wide web site—With up-to-date support for book. An icon occurs throughout the book to indicate more explanations and examples available on the web.

  • Provides students with expanded explanations of particular topics and additional information on algorithms.

• Upper bounds for worst-case times proven sharp.

  • Provides students with proved sharp bounds.

• Lower bounds integrated into sections that discuss problems—e.g. after presentation of several sorting algorithms, text discusses lower bound for comparison-based sorting.

  • Provides students with easy to follow organisation.

• Methods used to solve NP-complete problems—Including approximation, brute force, parameterised complexity, and heuristics.

  • Provides students with comprehensive chapter on topics with significant importance in algorithms.

• Recent results—Such as Pearson's polynomial-time algorithm for the coin-changing problem and parameterised complexity.

  • Provides students with up-to-date presentation that helps motivate the material.

• Figures and tables illustrate concepts—Figure captions provide additional explanations and insight.

  • Shows students how algorithms work to elucidate proofs.

Description

For upper-level undergraduate and graduate courses in algorithms.

Filling the void left by other algorithms books, Algorithms and Data Structures provides an approach that emphasises design techniques. The text includes application of algorithms, examples, end-of-section exercises, end-of-chapter exercises, hints and solutions to selected exercises, figures and notes to help the reader master the design and analysis of algorithms.

Sample Content

Table of Contents

• Mathematical Prerequisites
• Data Structures
• Searching Techniques
• Divide-and-Conquer
• Sorting and Selection
• Greedy Algorithms
• Dynamic Programming
• Text Searching
• Computational Algebra
• P and NP
• Coping with NP-Completeness
• Parallel Algorithms
• References
• Solutions to Selected Exercises
• Index

Updates

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