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Cover type: Hardback

Edition: 3RD 09

Copyright: 2009

Publisher: MIT Press

Published: 2009

International: No

Edition: 3RD 09

Copyright: 2009

Publisher: MIT Press

Published: 2009

International: No

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Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. The book covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.

The first edition became a widely used text in universities worldwide as well as the standard reference for professionals. The second edition featured new chapters on the role of algorithms, probabilistic analysis and randomized algorithms, and linear programming. The third edition has been revised and updated throughout. It includes two completely new chapters, on van Emde Boas trees and multithreaded algorithms, and substantial additions to the chapter on recurrences (now called "Divide-and-Conquer"). It features improved treatment of dynamic programming and greedy algorithms and a new notion of edge-based flow in the material on flow networks. Many new exercises and problems have been added for this edition.

Thomas Cormen is Professor of Computer Science at Dartmouth College.

Charles Leiserson is Professor of Computer Science and Engineering at MIT. Ronald L.

Rivest is Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT. Clifford Stein is Professor of Industrial Engineering and Operations Research at Columbia University.

Table of Contents

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

(and more...)

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Summary

Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. The book covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.

The first edition became a widely used text in universities worldwide as well as the standard reference for professionals. The second edition featured new chapters on the role of algorithms, probabilistic analysis and randomized algorithms, and linear programming. The third edition has been revised and updated throughout. It includes two completely new chapters, on van Emde Boas trees and multithreaded algorithms, and substantial additions to the chapter on recurrences (now called "Divide-and-Conquer"). It features improved treatment of dynamic programming and greedy algorithms and a new notion of edge-based flow in the material on flow networks. Many new exercises and problems have been added for this edition.

Author Bio

Thomas Cormen is Professor of Computer Science at Dartmouth College.

Charles Leiserson is Professor of Computer Science and Engineering at MIT. Ronald L.

Rivest is Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT. Clifford Stein is Professor of Industrial Engineering and Operations Research at Columbia University.

Table of Contents

Table of Contents

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

(and more...)

Publisher Info

Publisher: MIT Press

Published: 2009

International: No

Published: 2009

International: No

The first edition became a widely used text in universities worldwide as well as the standard reference for professionals. The second edition featured new chapters on the role of algorithms, probabilistic analysis and randomized algorithms, and linear programming. The third edition has been revised and updated throughout. It includes two completely new chapters, on van Emde Boas trees and multithreaded algorithms, and substantial additions to the chapter on recurrences (now called "Divide-and-Conquer"). It features improved treatment of dynamic programming and greedy algorithms and a new notion of edge-based flow in the material on flow networks. Many new exercises and problems have been added for this edition.

Charles Leiserson is Professor of Computer Science and Engineering at MIT. Ronald L.

Rivest is Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT. Clifford Stein is Professor of Industrial Engineering and Operations Research at Columbia University.

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

(and more...)