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nice book(Rating: 5)
nice quality, excellent content, it is really significant for mathematics or computer science students to have this book~

COMPLETE ALGORITHM(Rating: 5)
It is Comprehensive, Complete and Sufficient (no need of other books).

Some chapters dive into theory, but lots of content, computations.

Graph theory is included extensively.

The best textbook/reference on algorithms(Rating: 5)
I used this book as a text when I taught algorithms to computer science majors and the only regret I ever had was that there was not enough time to cover all of it. With over 1,100 pages of material, trying to squeeze it all into one semester was unreasonable. Furthermore, I never had a student complain about the content, although being students, they did complain about the (physical) weight. My students found it readable; the expressions of the algorithms are in mathematical notation and a standard pseudocode. Generally speaking, a background in discrete mathematics, specifically summation notation, is needed throughout. Other necessary material to help in understanding specific sections is graph theory, linear algebra, set theory, combinatorics, probability and number theory.
Choosing an algorithm in theory versus choosing one in practice is often contradictory and that is an occasional point of emphasis in this book. Some algorithms are extremely fast nearly all the time yet incredibly slow in some pathological conditions. Many of my students programmed for an avionics company and their background led to some very informative conversations. In their work, predictability of the speed of execution is often more critical than being fast and some generally fast algorithms are banned.
The breadth of coverage of algorithms is extensive, the chapter headings are:

*) The role of algorithms in computing
*) Getting started
*) Growth of functions
*) Divide-and-conquer
*) Probabilistic analysis and randomized algorithms
*) Heapsort
*) Quicksort
*) Sorting in linear time
*) Medians and order statistics
*) Elementary data structures
*) Hash tables
*) Binary search trees
*) Red-black trees
*) Augmenting data structures
*) Dynamic programming
*) Greedy algorithms
*) Amortized analysis
*) B-trees
*) Fibonacci heaps
*) van Emde Boas trees
*) Data structures for disjoint sets
*) Elementary graph algorithms
*) Minimum spanning trees
*) Single-source shortest paths
*) All-pairs shortest paths
*) Maximum flow
*) Multithreaded algorithms
*) Matrix operations
*) Linear programming
*) Polynomials and the FFT
*) Number-theoretic algorithms
*) String matching
*) Computational geometry
*) NP-completeness
*) Approximation algorithms

The depth of the explanations is great enough that this book can be used as a reference and resource for programmers that need to learn the performance details of an algorithm. Coded implementations of most algorithms are available online but when you need to thoroughly understand an algorithm, if it is found in this book then with effort, it can be understood.

Absolutely love this book...(Rating: 5)
This book was already a master piece and the revised edition adds some welcome changes to it. I like the changes in the pseudo-code style. Also, the addition of more realistic examples is great. Totally recommend to any computer science person..

Introduction to Algorithms, 3rd Ed. By Thomas Cormen, Charles Leierson, Ronald Rivest, Clifford Stein. (MIT Press)(Rating: 3)
Algorithms, which perform some sequence of mathematical operations, form the core of computer programming. Intended as a text for computer programming courses, especially undergraduate courses in data structures and graduate courses in algorithms, an "Introduction to Algorithms" provides a comprehensive overview, that will be appreciated technical professionals, as well.

The major topics presented are sorting, data structures, graph algorithms and a variety of selected topics. Computer programmers can draw desired algorithms directly from the text or use the clear explanations of the underlying mathematics to develop custom algorithms. The algorithms are presented in pseudocode that can be adapted to programming languages, such as C++ and Java. The focus is on design rather than implementation.

While a solid background in advanced mathematics and probability theory is needed to fully appreciate the material, non-programmers and IT professionals (such as this reviewer) will appreciate the numerous tips provided for improving the efficiency and thus reducing the cost of developing applications.

Any Computer Science student would find this text an essential resource, even if not specifically required for course work. However, the advanced mathematical principles needed to grasp the material are presented as exercises, intended to be worked through in class, so no solutions are provided, which may frustrate self-studiers and limit its utility as a reference. Although surprisingly well written, a book of this size and complexity is bound to have some errors. See [...] for the error list and supplemental information about the book (including solutions to some, but not all exercises, and an explanation of the corny professor jokes sprinkled throughout the text).