McGraw-Hill - Introduction To Algorithms, 2nd Edition

McGraw-Hill - Introduction To Algorithms, 2nd Edition

(Parte 1 de 8)

Introduction to Algorithms, Second Edition

Thomas H. Cormen Charles E. Leiserson Ronald L. Rivest Clifford Stein The MIT Press Cambridge , Massachusetts London, England McGraw-Hill Book Company Boston Burr Ridge , IL Dubuque , IA Madison , WI New York San Francisco St. Louis Montréal Toronto

This book is one of a series of texts written by faculty of the Electrical Engineering and Computer Science Department at the Massachusetts Institute of Technology. It was edited and produced by The MIT Press under a joint production-distribution agreement with the McGraw-Hill Book Company.

Ordering Information: North America

Text orders should be addressed to the McGraw-Hill Book Company. All other orders should be addressed to The MIT Press.

Outside North America All orders should be addressed to The MIT Press or its local distributor. Copyright © 2001 by The Massachusetts Institute of Technology First edition 1990

All rights reserved. No part of this book may be reproduced in any form or by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher.

This book was printed and bound in the United States of America.

Introduction to algorithms / Thomas H. Cormen[et al.].-2nd ed.
p. cm.

Library of Congress Cataloging-in-Publication Data Includes bibliographical references and index.

ISBN 0-262-03293-7 (hc.: alk. paper, MIT Press).-ISBN 0-07-013151-1 (McGraw-Hill)

1. Computer programming. 2. Computer algorithms. I. Title: Algorithms. I. Cormen, Thomas H.

QA76.6 I5858 2001

005.1-dc21 2001031277

Preface

This book provides a comprehensive introduction to the modern study of computer algorithms. It presents many algorithms and covers them in considerable depth, yet makes their design and analysis accessible to all levels of readers. We have tried to keep explanations elementary without sacrificing depth of coverage or mathematical rigor.

Each chapter presents an algorithm, a design technique, an application area, or a related topic. Algorithms are described in English and in a "pseudocode" designed to be readable by anyone who has done a little programming. The book contains over 230 figures illustrating how the algorithms work. Since we emphasize efficiency as a design criterion, we include careful analyses of the running times of all our algorithms.

The text is intended primarily for use in undergraduate or graduate courses in algorithms or data structures. Because it discusses engineering issues in algorithm design, as well as mathematical aspects, it is equally well suited for self-study by technical professionals.

In this, the second edition, we have updated the entire book. The changes range from the addition of new chapters to the rewriting of individual sentences.

To the teacher

This book is designed to be both versatile and complete. You will find it useful for a variety of courses, from an undergraduate course in data structures up through a graduate course in algorithms. Because we have provided considerably more material than can fit in a typical one-term course, you should think of the book as a "buffet" or "smorgasbord" from which you can pick and choose the material that best supports the course you wish to teach.

You should find it easy to organize your course around just the chapters you need. We have made chapters relatively self-contained, so that you need not worry about an unexpected and unnecessary dependence of one chapter on another. Each chapter presents the easier material first and the more difficult material later, with section boundaries marking natural stopping points. In an undergraduate course, you might use only the earlier sections from a chapter; in a graduate course, you might cover the entire chapter.

We have included over 920 exercises and over 140 problems. Each section ends with exercises, and each chapter ends with problems. The exercises are generally short questions that test basic mastery of the material. Some are simple self-check thought exercises, whereas others are more substantial and are suitable as assigned homework. The problems are more elaborate case studies that often introduce new material; they typically consist of several questions that lead the student through the steps required to arrive at a solution.

We have starred (⋆) the sections and exercises that are more suitable for graduate students than for undergraduates. A starred section is not necessarily more difficult than an unstarred one, but it may require an understanding of more advanced mathematics. Likewise, starred exercises may require an advanced background or more than average creativity.

To the student

We hope that this textbook provides you with an enjoyable introduction to the field of algorithms. We have attempted to make every algorithm accessible and interesting. To help you when you encounter unfamiliar or difficult algorithms, we describe each one in a step-bystep manner. We also provide careful explanations of the mathematics needed to understand the analysis of the algorithms. If you already have some familiarity with a topic, you will find the chapters organized so that you can skim introductory sections and proceed quickly to the more advanced material.

This is a large book, and your class will probably cover only a portion of its material. We have tried, however, to make this a book that will be useful to you now as a course textbook and also later in your career as a mathematical desk reference or an engineering handbook.

What are the prerequisites for reading this book?

• You should have some programming experience. In particular, you should understand recursive procedures and simple data structures such as arrays and linked lists.

• You should have some facility with proofs by mathematical induction. A few portions of the book rely on some knowledge of elementary calculus. Beyond that, Parts I and VIII of this book teach you all the mathematical techniques you will need.

To the professional

The wide range of topics in this book makes it an excellent handbook on algorithms. Because each chapter is relatively self-contained, you can focus in on the topics that most interest you.

Most of the algorithms we discuss have great practical utility. We therefore address implementation concerns and other engineering issues. We often provide practical alternatives to the few algorithms that are primarily of theoretical interest.

If you wish to implement any of the algorithms, you will find the translation of our pseudocode into your favorite programming language a fairly straightforward task. The pseudocode is designed to present each algorithm clearly and succinctly. Consequently, we do not address error-handling and other software-engineering issues that require specific assumptions about your programming environment. We attempt to present each algorithm simply and directly without allowing the idiosyncrasies of a particular programming language to obscure its essence.

To our colleagues

We have supplied an extensive bibliography and pointers to the current literature. Each chapter ends with a set of "chapter notes" that give historical details and references. The chapter notes do not provide a complete reference to the whole field of algorithms, however. Though it may be hard to believe for a book of this size, many interesting algorithms could not be included due to lack of space.

Despite myriad requests from students for solutions to problems and exercises, we have chosen as a matter of policy not to supply references for problems and exercises, to remove the temptation for students to look up a solution rather than to find it themselves.

Changes for the second edition

What has changed between the first and second editions of this book? Depending on how you look at it, either not much or quite a bit.

A quick look at the table of contents shows that most of the first-edition chapters and sections appear in the second edition. We removed two chapters and a handful of sections, but we have added three new chapters and four new sections apart from these new chapters. If you were to judge the scope of the changes by the table of contents, you would likely conclude that the changes were modest.

The changes go far beyond what shows up in the table of contents, however. In no particular order, here is a summary of the most significant changes for the second edition:

• Cliff Stein was added as a coauthor. • Errors have been corrected. How many errors? Let's just say several.

• There are three new chapters: o Chapter 1 discusses the role of algorithms in computing. o Chapter 5 covers probabilistic analysis and randomized algorithms. As in the first edition, these topics appear throughout the book. o Chapter 29 is devoted to linear programming.

• Within chapters that were carried over from the first edition, there are new sections on the following topics: o perfect hashing (Section 1.5), o two applications of dynamic programming (Sections 15.1 and 15.5), and o approximation algorithms that use randomization and linear programming

(Section 35.4).

• To allow more algorithms to appear earlier in the book, three of the chapters on mathematical background have been moved from Part I to the Appendix, which is Part VIII. • There are over 40 new problems and over 185 new exercises.

• We have made explicit the use of loop invariants for proving correctness. Our first loop invariant appears in Chapter 2, and we use them a couple of dozen times throughout the book.

• Many of the probabilistic analyses have been rewritten. In particular, we use in a dozen places the technique of "indicator random variables," which simplify probabilistic analyses, especially when random variables are dependent.

• We have expanded and updated the chapter notes and bibliography. The bibliography has grown by over 50%, and we have mentioned many new algorithmic results that have appeared subsequent to the printing of the first edition.

We have also made the following changes:

• The chapter on solving recurrences no longer contains the iteration method. Instead, in

Section 4.2, we have "promoted" recursion trees to constitute a method in their own right. We have found that drawing out recursion trees is less error-prone than iterating recurrences. We do point out, however, that recursion trees are best used as a way to generate guesses that are then verified via the substitution method.

• The partitioning method used for quicksort (Section 7.1) and the expected linear-time order-statistic algorithm (Section 9.2) is different. We now use the method developed by Lomuto, which, along with indicator random variables, allows for a somewhat simpler analysis. The method from the first edition, due to Hoare, appears as a problem in Chapter 7.

• We have modified the discussion of universal hashing in Section 1.3.3 so that it integrates into the presentation of perfect hashing.

• There is a much simpler analysis of the height of a randomly built binary search tree in

Section 12.4.

• The discussions on the elements of dynamic programming (Section 15.3) and the elements of greedy algorithms (Section 16.2) are significantly expanded. The exploration of the activity-selection problem, which starts off the greedy-algorithms chapter, helps to clarify the relationship between dynamic programming and greedy algorithms.

• We have replaced the proof of the running time of the disjoint-set-union data structure in Section 21.4 with a proof that uses the potential method to derive a tight bound.

• The proof of correctness of the algorithm for strongly connected components in

Section 2.5 is simpler, clearer, and more direct.

• Chapter 24, on single-source shortest paths, has been reorganized to move proofs of the essential properties to their own section. The new organization allows us to focus earlier on algorithms.

• Section 34.5 contains an expanded overview of NP-completeness as well as new NP- completeness proofs for the hamiltonian-cycle and subset-sum problems.

Finally, virtually every section has been edited to correct, simplify, and clarify explanations and proofs.

Web site

Another change from the first edition is that this book now has its own web site: http://mitpress.mit.edu/algorithms/. You can use the web site to report errors, obtain a list of known errors, or make suggestions; we would like to hear from you. We particularly welcome ideas for new exercises and problems, but please include solutions.

We regret that we cannot personally respond to all comments.

Acknowledgments for the first edition

Many friends and colleagues have contributed greatly to the quality of this book. We thank all of you for your help and constructive criticisms.

MIT's Laboratory for Computer Science has provided an ideal working environment. Our colleagues in the laboratory's Theory of Computation Group have been particularly supportive and tolerant of our incessant requests for critical appraisal of chapters. We specifically thank Baruch Awerbuch, Shafi Goldwasser, Leo Guibas, Tom Leighton, Albert Meyer, David Shmoys, and Éva Tardos. Thanks to William Ang, Sally Bemus, Ray Hirschfeld, and Mark Reinhold for keeping our machines (DEC Microvaxes, Apple Macintoshes, and Sun

Sparcstations) running and for recompiling whenever we exceeded a compile-time limit. Thinking Machines Corporation provided partial support for Charles Leiserson to work on this book during a leave of absence from MIT.

Many colleagues have used drafts of this text in courses at other schools. They have suggested numerous corrections and revisions. We particularly wish to thank Richard Beigel, Andrew Goldberg, Joan Lucas, Mark Overmars, Alan Sherman, and Diane Souvaine.

Many teaching assistants in our courses have made significant contributions to the development of this material. We especially thank Alan Baratz, Bonnie Berger, Aditi Dhagat, Burt Kaliski, Arthur Lent, Andrew Moulton, Marios Papaefthymiou, Cindy Phillips, Mark Reinhold, Phil Rogaway, Flavio Rose, Arie Rudich, Alan Sherman, Cliff Stein, Susmita Sur, Gregory Troxel, and Margaret Tuttle.

Additional valuable technical assistance was provided by many individuals. Denise Sergent spent many hours in the MIT libraries researching bibliographic references. Maria Sensale, the librarian of our reading room, was always cheerful and helpful. Access to Albert Meyer's personal library saved many hours of library time in preparing the chapter notes. Shlomo Kipnis, Bill Niehaus, and David Wilson proofread old exercises, developed new ones, and wrote notes on their solutions. Marios Papaefthymiou and Gregory Troxel contributed to the indexing. Over the years, our secretaries Inna Radzihovsky, Denise Sergent, Gayle Sherman, and especially Be Blackburn provided endless support in this project, for which we thank them.

Many errors in the early drafts were reported by students. We particularly thank Bobby Blumofe, Bonnie Eisenberg, Raymond Johnson, John Keen, Richard Lethin, Mark Lillibridge, John Pezaris, Steve Ponzio, and Margaret Tuttle for their careful readings.

Colleagues have also provided critical reviews of specific chapters, or information on specific algorithms, for which we are grateful. We especially thank Bill Aiello, Alok Aggarwal, Eric Bach, Vašek Chvátal, Richard Cole, Johan Hastad, Alex Ishii, David Johnson, Joe Kilian, Dina Kravets, Bruce Maggs, Jim Orlin, James Park, Thane Plambeck, Hershel Safer, Jeff Shallit, Cliff Stein, Gil Strang, Bob Tarjan, and Paul Wang. Several of our colleagues also graciously supplied us with problems; we particularly thank Andrew Goldberg, Danny Sleator, and Umesh Vazirani.

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