The latest edition of the essential text and professional reference, with substantial new material on such topics as vEB trees, multithreaded algorithms, dynamic programming, and edge-based flow.
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, substantial additions to the chapter on recurrence (now called “Divide-and-Conquer”), and an appendix on matrices. 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 exercises and problems have been added for this edition. The international paperback edition is no longer available; the hardcover is available worldwide.
Author(s): Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein
Edition: 3
Publisher: MIT Press
Year: 2009
Language: English
Pages: 1320
City: Cambridge, MA
Cover
Title Page
Copyright Page
Table of Contents
Preface
I Foundations
Introduction
1 The Role of Algorithms in Computing
1.1 Algorithms
1.2 Algorithms as a technology
2 Getting Started
2.1 Insertion sort
2.2 Analyzing algorithms
2.3 Designing algorithms
3 Growth of Functions
3.1 Asymptotic notation
3.2 Standard notations and common functions
4 Divide-and-Conquer
4.1 The maximum-subarray problem
4.2 Strassen’s algorithm for matrix multiplication
4.3 The substitution method for solving recurrences
4.4 The recursion-tree method for solving recurrences
4.5 The master method for solving recurrences
4.6 Proof of the master theorem
5 Probabilistic Analysis and Randomized Algorithms
5.1 The hiring problem
5.2 Indicator random variables
5.3 Randomized algorithms
5.4 Probabilistic analysis and further uses of indicator random variables
II Sorting and Order Statistics
Introduction
6 Heapsort
6.1 Heaps
6.2 Maintaining the heap property
6.3 Building a heap
6.4 The heapsort algorithm
6.5 Priority queues
7 Quicksort
7.1 Description of quicksort
7.2 Performance of quicksort
7.3 A randomized version of quicksort
7.4 Analysis of quicksort
8 Sorting in Linear Time
8.1 Lower bounds for sorting
8.2 Counting sort
8.3 Radix sort
8.4 Bucket sort
9 Medians and Order Statistics
9.1 Minimum and maximum
9.2 Selection in expected linear time
9.3 Selection in worst-case linear time
III Data Structures
Introduction
10 Elementary Data Structures
10.1 Stacks and queues
10.2 Linked lists
10.3 Implementing pointers and objects
10.4 Representing rooted trees
11 Hash Tables
11.1 Direct-address tables
11.2 Hash tables
11.3 Hash functions
11.4 Open addressing
11.5 Perfect hashing
12 Binary Search Trees
12.1 What is a binary search tree?
12.2 Querying a binary search tree
12.3 Insertion and deletion
12.4 Randomly built binary search trees
13 Red-Black Trees
13.1 Properties of red-black trees
13.2 Rotations
13.3 Insertion
13.4 Deletion
14 Augmenting Data Structures
14.1 Dynamic order statistics
14.2 How to augment a data structure
14.3 Interval trees
IV Advanced Design and Analysis Techniques
Introduction
15 Dynamic Programming
15.1 Rod cutting
15.2 Matrix-chain multiplication
15.3 Elements of dynamic programming
15.4 Longest common subsequence
15.5 Optimal binary search trees
16 Greedy Algorithms
16.1 An activity-selection problem
16.2 Elements of the greedy strategy
16.3 Huffman codes
16.4 Matroids and greedy methods
16.5 A task-scheduling problem as a matroid
17 Amortized Analysis
17.1 Aggregate analysis
17.2 The accounting method
17.3 The potential method
17.4 Dynamic tables
V Advanced Data Structures
Introduction
18 B-Trees
18.1 Definition of B-trees
18.2 Basic operations on B-trees
18.3 Deleting a key from a B-tree
19 Fibonacci Heaps
19.1 Structure of Fibonacci heaps
19.2 Mergeable-heap operations
19.3 Decreasing a key and deleting a node
19.4 Bounding the maximum degree
20 van Emde Boas Trees
20.1 Preliminary approaches
20.2 A recursive structure
20.3 The van Emde Boas tree
21 Data Structures for Disjoint Sets
21.1 Disjoint-set operations
21.2 Linked-list representation of disjoint sets
21.3 Disjoint-set forests
21.4 Analysis of union by rank with path compression
VI Graph Algorithms
Introduction
22 Elementary Graph Algorithms
22.1 Representations of graphs
22.2 Breadth-first search
22.3 Depth-first search
22.4 Topological sort
22.5 Strongly connected components
23 Minimum Spanning Trees
23.1 Growing a minimum spanning tree
23.2 The algorithms of Kruskal and Prim
24 Single-Source Shortest Paths
24.1 The Bellman-Ford algorithm
24.2 Single-source shortest paths in directed acyclic graphs
24.3 Dijkstra’s algorithm
24.4 Difference constraints and shortest paths
24.5 Proofs of shortest-paths properties
25 All-Pairs Shortest Paths
25.1 Shortest paths and matrix multiplication
25.2 The Floyd-Warshall algorithm
25.3 Johnson’s algorithm for sparse graphs
26 Maximum Flow
26.1 Flow networks
26.2 The Ford-Fulkerson method
26.3 Maximum bipartite matching
26.4 Push-relabel algorithms
26.5 The relabel-to-front algorithm
VII Selected Topics
Introduction
27 Multithreaded Algorithms
27.1 The basics of dynamic multithreading
27.2 Multithreaded matrix multipication
27.3 Multithreaded merge sort
28 Matrix Operations
28.1 Solving systems of linear equations
28.2 Inverting matrices
28.3 Symmetric positive-definite matrices and least-squares approximation
29 Linear Programming
29.1 Standard and slack forms
29.2 Formulating problems as linear programs
29.3 The simplex algorithm
29.4 Duality
29.5 The initial basic feasible solution
30 Polynomials and the FFT
30.1 Representing polynomials
30.2 The DFT and FFT
30.3 Efficient FFT implementations
31 Number-Theoretic Algorithms
31.1 Elementary number-theoretic notions
31.2 Greatest common divisor
31.3 Modular arithmetic
31.4 Solving modular linear equations
31.5 The Chinese remainder theorem
31.6 Powers of an element
31.7 The RSA public-key cryptosystem
31.8 Primality testing
31.9 Integer factorization
32 String Matching
32.1 The naive string-matching algorithm
32.2 The Rabin-Karp algorithm
32.3 String matching with finite automata
32.4 The Knuth-Morris-Pratt algorithm
33 Computational Geometry
33.1 Line-segment properties
33.2 Determining whether any pair of segments intersects
33.3 Finding the convex hull
33.4 Finding the closest pair of points
34 NP-Completeness
34.1 Polynomial time
34.2 Polynomial-time verification
34.3 NP-completeness and reducibility
34.4 NP-completeness proofs
34.5 NP-complete problems
35 Approximation Algorithms
35.1 The vertex-cover problem
35.2 The traveling-salesman problem
35.3 The set-covering problem
35.4 Randomization and linear programming
35.5 The subset-sum problem
VIII Appendix: Mathematical Background
Introduction
A Summations
A.1 Summation formulas and properties
A.2 Bounding summations
B Sets, Etc.
B.1 Sets
B.2 Relations
B.3 Functions
B.4 Graphs
B.5 Trees
C Counting and Probability
C.1 Counting
C.2 Probability
C.3 Discrete random variables
C.4 The geometric and binomial distributions
C.5 The tails of the binomial distribution
D Matrices
D.1 Matrices and matrix operations
D.2 Basic matrix properties
Bibliography
Index
