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Anonymous
Posted December 7, 2000
I have used this book in my Masters level algorithms course before. Unfortunately, it seems to be the best available. The book is way too long. The mathematical introduction is useless. There are few examples and little differentiation between what works in practice and why.
1 out of 1 people found this review helpful.
Was this review helpful? Yes NoThank you for your feedback. Report this reviewThank you, this review has been flagged.Anonymous
Posted September 17, 2002
this book weighs way too much and keeps me from spending time with my new wife. However, it is a fabulous book for what it actually is. More pictures would be helpful!
0 out of 1 people found this review helpful.
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Posted November 4, 2008
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Posted December 31, 2009
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More About This Textbook
Overview
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.
Paperback not available in U.S. and Canada.
Editorial Reviews
Booknews
Both rigorous and complete, this introduction covers traditional material and modern developments: elementary data structures, sorting graph algorithms and NP-completeness are included along with material on Fibonacci heaps, parallel algorithms, network flow algorithms, computational geometry, and number-theoretic algorithms. Annotation c. Book News, Inc., Portland, OR booknews.comProduct Details
Related Subjects
Meet the Author
Thomas H. Cormen is Assistant Professor in the Department of Computer Science at Dartmouth College.
Charles E. Leiserson is Professor and head of the Supercomputing Technologies Group at the Laboratory for Computer Science at MIT.
Ronald L. Rivest is the Webster Professor of Electrical Engineering and Computer Science in the Department of Electrical Engineering and Computer Science and Associate Director of the Laboratory for Computer Science at MIT.
Table of Contents
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...)