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Algorithm Design Hardcover – March 26, 2005

ISBN-13: 978-0321295354 ISBN-10: 0321295358 Edition: 1st

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Product Details

  • Hardcover: 864 pages
  • Publisher: Addison-Wesley; 1 edition (March 26, 2005)
  • Language: English
  • ISBN-10: 0321295358
  • ISBN-13: 978-0321295354
  • Product Dimensions: 9.4 x 8.2 x 1.4 inches
  • Shipping Weight: 3.5 pounds (View shipping rates and policies)
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (41 customer reviews)
  • Amazon Best Sellers Rank: #149,634 in Books (See Top 100 in Books)

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Customer Reviews

The book is very good and has great examples to explain all of the difficult concepts.
tesh
This book has good coverage of contents, reads easily, involving an excellent and unambiguous writing style.
Amshuman
Not much to write about this, as it is a book and the book is great for any Computer Science student.
S. Surender

Most Helpful Customer Reviews

57 of 61 people found the following review helpful By Szymon Rozga on July 18, 2005
Format: Hardcover
The flow in this book is excellent. The authors do a great job in organizing this book in logical chapter. The chapters are organized into techniques to find solutions to particular problems, like for example, Greedy Algorithms, Divide and Conquer, and Dynamic Programming.

Each chapter contains a few representative problems of the technique or topic discussed. These are discussed in great detail, which is helpful to initially grasp the concepts. Furthermore, the end of each chapter contains a number of solved exercises. These are written up in less detail than the chapter problems, because they are usually slight variations or applications of the representative problems. I found these to be very helpful to me, as to build up a stronger grasp of the problem at hand.

Furthemore, the progressive search for a solution, such as for the Weighted Interval Scheduling problem using dynamic programming, is essential to understanding the process through which we can find such algorithms. The book is well written, in a clear, understandable language. The supplementary chapters on Basics of Algorithm Analysis and Graph Theory are a great started for people who have not been exposed to those concepts previously.

Network flows are covered extensively with their applications. I suppose this section of the course was enhanced because our instructor's research interests are Network Flows and she threw example after example at us. There are a great number of problems at the end of this chapter to practice.

(...)
One of the strenghs of this book, is that when the authors determine the running time of a particular algorithm, they write about how to implement it, with which data structures and why.
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33 of 34 people found the following review helpful By Przemyslaw Drochomirecki on June 11, 2007
Format: Hardcover Verified Purchase
Best undergraduate handbook about algorithms i've seen so far.
Examples are much less artificial than in CLRS (Introduction to Algorithms). Most of them are highly practical, e.g. using Kruskal's MST algorithm as a simple clustering device.
It's worth mentioning that E.Tardos is a world-class calibre specialist in graph algorithms. When you feel unsatisfied with network flows chapter, you can read her survey of network flows (written with two other graph titans - Goldberg and Tarjan)
The division into chapters is good, yet classical. There are also exercises after each chapter, lots of them, good for preparation if you have algorithm-oriented job interview (Google, Yahoo, Microsoft etc.).

What's next? Read Tarjan's evergreen classic - Data Structures and Network Algorithms.
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36 of 41 people found the following review helpful By W Boudville HALL OF FAMETOP 1000 REVIEWERVINE VOICE on July 30, 2005
Format: Hardcover
The text offers an interesting blend of rigour and informality. The numerous proofs in each chapter have that rigour. Yet what may be more important is how the text remains accessible to a primarily undergraduate audience.

The book is not just a compendium of common algorithms in computer science, and proofs about them. The authors place a stronger emphasis on motivating how to develop an intuitive understanding of the problems that the algorithms address, and of how to shape new algorithms. Or, possibly, apply or modify existing algorithms to new problems.

If you compare the text to Knuth's classic "Art of Computer Programming", then you might find Kleinberg and Tardos more accessible. (At least for undergraduate readership.)

Also, the extensive exercises at the end of each chapter often have contexts germane to the Web. For example, the links in web pages are used to motivate problems in graph theory, where we have directed (unidirectional) graphs, due to the one way nature of links. More generally, the recent, contextual nature of the problems may appeal to some students. Knuth had many exercises listed in his books, but they can be too abstract for most students.

The text also has an interesting chapter on NP problems. The authors address a very practical situation. Even if you find that you have a problem that is NP complete, it is not necessarily the end of the story. For real life reasons, you may have to find an approximate solution that is computationally feasible to evaluate. The chapter offers suggestions and examples that may be of help. (More formal texts might merely stop at proving NP completeness.)
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26 of 31 people found the following review helpful By Ariel Yaroshevich on February 26, 2011
Format: Hardcover
Broad coverage. Poor organization. I used to use it as a text book for a first course in algorithms, in the Open University of Israel while I was teaching there.
Absolutely inferior to Cormen for an introductory course on algorithms.
Hardly adequate for self study.
The authors structure the book according to an "algorithmic design strategy" concept - that is, Greedy, Divide and Conquer, Dynamic programming, etc... but but soon enough they're out of ideas, as the later chapters just follow the standard topics of any other text book: network flow, NP-completeness, approximation algorithms, heuristic search etc... (although, as said - the coverage is pretty broad).

The problem with such an approach, as much as it might be appealing for a mature computer scientist, is, that for a student, grasping the "paradigm" or an "algorithmic strategy" is almost impossible, especially when he learned about BFS, DFS and Dijkstra's algorithms about two weeks ago. It just makes him confused.

The authors try to develop the "right" algorithm in an incremental way, which may be an appropriate in a class room, but in a text book it is, once again, just confusing. You can never know if the current version of the algorithm is "the canonical one", or just another thought experiment. Even when the final version of the algorithm is presented - the pseudo code is excruciatingly terse, the complexity isn't clearly stated in the same paragraph, but mentioned in some remote part of the discussion.

Even the pseudo code typography looks like a Telnet printout from the 70s. How I missed the elegant notation of Cormen, not to mention Knuth...
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