## Description

### Instructor’s Manual Introduction To Algorithms 2nd Edition Thomas H. Cormen, Clara Lee, Erica Lin

The first edition won the award for Best 1990 Professional and Scholarly Book in Computer Science and Data Processing by the Association of American Publishers.

There are books on algorithms that are rigorous but incomplete and others that cover masses of material but lack rigor. Introduction to Algorithms 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 the standard reference for professionals and a widely used text in universities worldwide. The second edition features new chapters on the role of algorithms, probabilistic analysis and randomized algorithms, and linear programming, as well as extensive revisions to virtually every section of the book. In a subtle but important change, loop invariants are introduced early and used throughout the text to prove algorithm correctness. Without changing the mathematical and analytic focus, the authors have moved much of the mathematical foundations material from Part I to an appendix and have included additional motivational material at the beginning.

ISBN-13: 978-0262032933

ISBN-10: 0262032937

### Table Of Contents:

Revision History R-1

Preface P-1

Chapter 2: Getting Started

Lecture Notes 2-1

Solutions 2-16

Chapter 3: Growth of Functions

Lecture Notes 3-1

Solutions 3-7

Chapter 4: Recurrences

Lecture Notes 4-1

Solutions 4-8

Chapter 5: Probabilistic Analysis and Randomized Algorithms

Lecture Notes 5-1

Solutions 5-8

Chapter 6: Heapsort

Lecture Notes 6-1

Solutions 6-10

Chapter 7: Quicksort

Lecture Notes 7-1

Solutions 7-9

Chapter 8: Sorting in Linear Time

Lecture Notes 8-1

Solutions 8-9

Chapter 9: Medians and Order Statistics

Lecture Notes 9-1

Solutions 9-9

Chapter 11: Hash Tables

Lecture Notes 11-1

Solutions 11-16

Chapter 12: Binary Search Trees

Lecture Notes 12-1

Solutions 12-12

Chapter 13: Red-Black Trees

Lecture Notes 13-1

Solutions 13-13

Chapter 14: Augmenting Data Structures

Lecture Notes 14-1

Solutions 14-9

iv Contents

Chapter 15: Dynamic Programming

Lecture Notes 15-1

Solutions 15-19

Chapter 16: Greedy Algorithms

Lecture Notes 16-1

Solutions 16-9

Chapter 17: Amortized Analysis

Lecture Notes 17-1

Solutions 17-14

Chapter 21: Data Structures for Disjoint Sets

Lecture Notes 21-1

Solutions 21-6

Chapter 22: Elementary Graph Algorithms

Lecture Notes 22-1

Solutions 22-12

Chapter 23: Minimum Spanning Trees

Lecture Notes 23-1

Solutions 23-8

Chapter 24: Single-Source Shortest Paths

Lecture Notes 24-1

Solutions 24-13

Chapter 25: All-Pairs Shortest Paths

Lecture Notes 25-1

Solutions 25-8

Chapter 26: Maximum Flow

Lecture Notes 26-1

Solutions 26-15

Chapter 27: Sorting Networks

Lecture Notes 27-1

Solutions 27-8

Index I-1

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