《Introduction to Algorithms》译作《算法导论》，是由Thomas H.Cormen, Charles E.Leiserson 等人编写的十分经典的计算机算法书籍。本书以相当的深度介绍了许多常用的数据结构和有效的算法，使得这些算法的设计和分析易于被各个层次的读者所理解。本书被china-pub会员评为“2007年我最喜爱的十大技术图书”之一，被《程序员》等机构评选为2006年最受读者喜爱的十大IT图书之一
Thomas h cormen Charles e. leiserson Ronald l. rivest Clifford Stein Introduction to Algorithms Third edition The mit press Cambridge, Massachusetts London, England C 2009 Massachusetts Institute of Technology 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: For information about special quantity discounts, please email special sales @mitpress. mit. edu This book was set in Times roman and mathtime pro 2 by the authors Printed and bound in the united States of america Library of Congress Cataloging-in-Publication Data Introduction to algorithms /Thomas H Cormen. let al. -3rd ed Includes bibliographical references and index isBn 978-0-262-03384-8(hardcover: alk. paper)-isBn 978-0-262-53305-8(pbk: alk. paper) 1. Computer programming. 2. Computer algorithms. I Cormen, Thomas H QA76.6.158582009 005.1-dc22 2009008593 1098765432 Contents P peace I Foundations Introduction The role of Algorithms in Computing 5 1.1Al t orithms 1.2 Algorithms as a technology 1/ 2 Getting Started 16 2.1 Insertion sort 16 2.2 Analyzing algorithms 23 2. 3 Designing algorithms 29 3 Growth of Functions 43 3.1 Asymptotic notation 43 3.2 Standard notations and common functions 53 4 Divide-and-Conquer 65 4.1 The maximum-subarray problem 68 4.2 Strassen's algorithm for matrix multiplication 75 4.3 The substitution method for solving recurrences 83 4. 4 The recursion-tree method for solving recurrences 88 4.5 The master method for solving recurrences 93 *4.6 Proof of the master theorem 97 Probabilistic Analysis and Randomized algorithms 114 1 The hiring problem 11 5.2 Indicator random variables 8 5. 3 Randomized algorithms 22 5.4 Probabilistic analysis and further uses of indicator random variables 130 Contents I Sorting and Order statistics Introduction 147 6 Heapsort 151 6. 1 Heaps 157 6.2 Maintaining the heap property 154 6.3 Building a heap 156 6. 4 The heapsort algorithm 159 6.5 Priority queues 162 7 Quicksort 170 7.1 Description of quicksort 170 7.2 Performance of quicksort 174 7.3 A randomized version of quicksort 179 7.4 Analysis of quicksort 180 8 Sorting in Linear Time 191 8.1 Lower bounds for sorting 19/ 8.2 Counting sort 194 8.3 Radix sort 97 8.4 Bucket sort 200 9 Medians and Order statistics 213 9.1 Minimum and maximum 214 9.2 Selection in expected linear time 215 9.3 Selection in worst-case linear time 220 II Data Structures Introduction 229 10 Elementary Data Structu 232 10.1 Stacks and queues 232 10.2 Linked lists 236 10.3 Implementing pointers and objects 24/ 10.4 Representing rooted trees 246 11 Hash Tables 253 11.1 Direct-address tables 254 11.2 Hash tables 256 11.3 Hash functions 262 11.4 Open addressing g269 11.5 Perfect hashing 277 ontents 12 Binary Search Trees 286 12. 1 What is a binary search tree? 286 12.2 Querying a binary search tree 289 12. 3 Insertion and deletion 294 12 4 Randomly built binary search trees 299 13 Red-Black Trees 308 13.1 Properties of red-black trees 308 13.2 Rotations 3/2 13.3 Insertion 315 13. 4 Deletion 323 14 Augmenting Data Structures 339 14.1 Dynamic order statistics 339 14.2 How to augment a data structure 345 14.3 Interval trees 348 lv Advanced Design and Analysis Techniques Introduction 357 15 Dynamic Programming 359 15.1 Rod cutting 360 15.2 Matrix-chain multiplication 370 15.3 Elements of dynamic programming 378 15.4 Longest common subsequence 390 15.5 Optimal binary search trees 397 16 Greedy Algorithms 414 16.1 An activity-selection problem 415 16.2 Elements of the greedy strategy 423 16. 3 Huffman codes 428 16.4 Matroids and greedy methods 437 16.5 A task-scheduling problem as a matroid 443 17 Amortized Analysis 457 17.1Ag ggregate analysis 452 17.2 The accounting method 456 17. 3 The potential method 459 17.4 Dynamic tables 463 Contents v Advanced Data structures Introduction 481 18 B-Trees 484 18.1 Definition of b-trees 488 18.2 Basic operations on B-trees 491 18.3 Deleting a key from a B-tree 499 19 Fibonacci Heaps 505 19.1 Structure of Fibonacci heaps 507 19.2 Mergeable-heap operations 510 19.3 Decreasing a key and deleting a node 518 19. 4 Bounding the maximum degree 523 20 van Emde boas trees 531 20.1 Preliminary approaches 532 20.2 A recursive structure 536 20. 3 The van emde boas tree 545 21 Data Structures for Disjoint Sets 561 21.1 Disjoint-Set operations 56/ 21.2 Linked-list representation of disjoint sets 564 21.3 Disjoint-set forests 568 21.4 Analysis of union by rank with path compression 573 vi Graph algorithms Introduction 587 22 Elementary Graph algorithms 589 22.1 Representations of graphs 589 22.2 Breadth-first search 594 22.3 Depth-first search 603 22.4 Topological sort 612 22.5 Strongly connected components 615 23 Minimum Spanning Trees 624 23. 1 Growing a minimum spanning tree 625 23.2 The algorithms of Kruskal and prim 631 Contents 24 Single-Source Shortest Paths 643 24.1 The Bellman-Ford algorithm 651 24.2 Single-source shortest paths in directed acyclic graphs 655 24.3 Dijkstra's algorithm 658 24.4 Difference constraints and shortest paths 664 24.5 Proofs of shortest-paths properties 671 25 All-Pairs shortest paths 684 25.1 Shortest paths and matrix multiplication 686 25.2 The Floyd-Warshall algorithm 693 25. 3 Johnson's algorithm for sparse graphs hs700 26 Maximum flow 708 26.1 Flow networks 709 26.2 The Ford-Fulkerson method 7/4 26.3 Maximum bipartite matching 732 26.4 Push-relabel algorithms 736 26.5 The relabel-to-front algorithm 748 vI Selected Topics Introduction 769 27 Multithreaded Algorithms 772 27.1 The basics of dynamic multithreading 774 27.2 Multithreaded matrix multiplication 792 27.3 Multithreaded merge sort 797 28 Matrix Operations 813 28.1 Solving systems of linear equations 813 28.2 Inverting matrices 827 28.3 Symmetric positive-definite matrices and least-squares approximation 832 29 Linear Programming 843 29.1 Standard and slack forms 850 29.2 Formulating problems as linear programs 859 29. 3 The simplex algorithm 864 29.4 Duality 879 29. 5 The initial basic feasible solution 886