Introduction To AlgorithmsThis title 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. |
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Contents
Introduction | 3 |
Getting Started | 15 |
Growth of Functions | 41 |
Recurrences | 62 |
Probabilistic Analysis and Randomized Algorithms | 91 |
Introduction | 123 |
Quicksort | 145 |
Sorting in Linear Time | 165 |
Data Structures for Disjoint Sets | 498 |
22 | 525 |
23 | 561 |
27 | 701 |
30 | 822 |
String Matching | 906 |
Computational Geometry | 933 |
NPCompleteness | 966 |
Medians and Order Statistics | 183 |
Introduction | 197 |
Hash Tables | 221 |
Introduction | 321 |
18 | 431 |
Binomial Heaps | 455 |
Fibonacci Heaps | 476 |
Approximation Algorithms | 1022 |
B Sets Etc | 1070 |
с Counting and Probability | 1094 |
1127 | |
1145 | |
Other editions - View all
Introduction to Algorithms and Java CD-ROM Thomas Cormen,Charles Leiserson,Ronald Rivest,Clifford Stein No preview available - 2003 |
Introduction to Algorithms and Java CD-ROM Thomas Cormen,Charles Leiserson,Ronald Rivest,Clifford Stein No preview available - 2003 |
Introduction to Algorithms and Java CD-ROM Thomas Cormen,Charles Leiserson,Ronald Rivest,Clifford Stein No preview available - 2003 |
Common terms and phrases
algorithm amortized cost array assume asymptotic B-tree binary search tree binomial heap bitonic Chapter compute constant constraints contains data structure define DELETE denote depth-first search directed graph edge elements equation example Exercise Fibonacci heap Figure flow network given graph G greedy hash function hash table implement input insertion sort integer iteration key[x Lemma linear program linked list loop invariant loop of lines matrix maximum merge sort method minimum spanning tree modulo multiplication negative-weight cycle node nonnegative NP-complete O(lg O(n lg objective value operations optimal solution output partition performed permutation pointer points polynomial polynomial-time problem procedure Proof prove pseudocode queue quicksort random recursive call red-black tree relabel root list Section sequence shortest path simplex slack form solve stack subarray subproblems subset subtree Suppose Theorem variables vector vertex vertices weight worst-case running