In its new edition, Introduction to Algorithms continues to provide a comprehensive introduction to the modern study of algorithms. The revision has been updated to reflect changes in the years since the book's original publication. New chapters on the role of algorithms in computing and on probabilistic analysis and randomized algorithms have been included. Sections throughout the book have been rewritten for increased clarity, and material has been added wherever a fuller explanation has seemed useful or new information warrants expanded coverage.

As in the classic first edition, this new edition of Introduction to Algorithms presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers. Further, the algorithms are presented in pseudocode to make the book easily accessible to students from all programming language backgrounds.

Each chapter presents an algorithm, a design technique, an application area, or a related topic. The chapters are not dependent on one another, so the instructor can organize his or her use of the book in the way that best suits the course's needs. Additionally, the new edition offers a 25% increase over the first edition in the number of problems, giving the book 155 problems and over 900 exercises that reinforce the concepts the students are learning.

In its new edition, Introduction to Algorithms continues to provide a comprehensive introduction to the modern study of algorithms. The revision has been updated to reflect changes in the years since the book's original publication. New chapters on the role of algorithms in computing and on probabilistic analysis and randomized algorithms have been included. Sections throughout the book have been rewritten for increased clarity, and material has been added wherever a fuller explanation has seemed useful or new information warrants expanded coverage.

As in the classic first edition, this new edition of Introduction to Algorithms presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers. Further, the algorithms are presented in pseudocode to make the book easily accessible to students from all programming language backgrounds.

Each chapter presents an algorithm, a design technique, an application area, or a related topic. The chapters are not dependent on one another, so the instructor can organize his or her use of the book in the way that best suits the course's needs. Additionally, the new edition offers a 25% increase over the first edition in the number of problems, giving the book 155 problems and over 900 exercises that reinforcethe concepts the students are learning.

Preface

I Foundations

1 The Role of Algorithms in Computing

2 Getting Started

3 Growth of Functions

4 Recurrences

5 Probabilistic Analysis and Randomized Algorithms

II Sorting and Order Statistics

6 Heapsort

7 Quicksort

8 Sorting in Linear Time

9 Medians and Order Statistics

III Data Structures

10 Elementary Data Structures

11 Hash Table

12 Binary Search Trees

13 Red-Black Trees

14 Augmenting Data Structures

IV Advanced Design and Analysis Techniques

15 Dynamic Programming

16 Greedy Algorithms

17 Amortized Analysis

V Advanced Data Structures

18 B-Trees

19 Binomial Heaps

20 Fibonacci Heaps

21 Data Structures for Disjoint Sets

VI Graph Algorithms

22 Elementary Graph Algorithms

23 Minimum Spanning Trees

24 Single-Source Shortest Paths

25 All-Pairs Shortest Paths

26 Maximum Flow

VII Selected Topics

27 Sorting Networks

28 Matrix Operations

29 Linear Programming

30 Polynomials and the FFT

31 Number-Theoretic Algorithms

32 String Matching

33 Computational Geometry

34 NP-Completeness

35 Approximation Algorithms

VIII Appendix: Mathematical Background

A Summations

B Sets, Etc.

C Counting and Probability

Bibliography

Index (created by the authors)