Introduction to Algorithms, Third Edition
It is used as the textbook for algorithm courses at many universities and is commonly cited as a reference for algorithms in published papers, with over 8900 citations documented on CiteSeerX. The book sold half a million copies during its first 20 years.Its fame has led to the common use of the abbreviation "CLRS" (Cormen, Leiserson, Rivest, Stein), or, in the first edition, "CLR" (Cormen, Leiserson, Rivest).
The first edition of the textbook did not include Stein as an author, and thus the book became known by the initialism CLR. After the addition of the fourth author in the second edition, many began to refer to the book as "CLRS". This first edition of the book was also known as "The Big White Book (of Algorithms)." With the second edition, the predominant color of the cover changed to green, causing the nickname to be shortened to just "The Big Book (of Algorithms). A third edition was published in August 2009.
Contents
- I Foundations
- 1 The Role of Algorithms in Computing
- 2 Getting Started
- 3 Growth of Functions
- 4 Divide-and-Conquer
- 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 Tables
- 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 Fibonacci Heaps
- 20 van Emde Boas Trees
- 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 Multithreaded Algorithms
- 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
- D Matrices
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