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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.Read more ›
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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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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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This review is for the Kindle edition of "Algorithm Design" by Kleinberg and Tardos Algorithm Design
This book is wonderfully organized. I used it for an Algorithms course and it's just very well laid out, with a nice progression of topics. If you want to gain a good "overall" picture of algorithms, this book is perfect. As with any kind of math, if you want to go much deeper, you'll need specialized textbooks for particular topics, but for a reasonably complete, holistic, one-semester course, you'll love this book. I should also point out that there are several well-crafted exercises in each chapter to cement your understanding and give your grey matter a good workout!
IMPORTANT: The Kindle edition of this book is a horribly travesty to the non-digital edition. The typesetting is crude (Amazon, you can do way better!), important figures/diagrams are scaled to tiny sizes, and formulas just plain look incorrect. My best guess is that this was re-typeset by hand, by a non-technical person using MS Word, so they simply messed it up badly. Personally, I am kicking myself for having paid nearly $90 for this, when I could've bought the "real" textbook for $110.
I was sorely tempted to give it a one-star review, but the content is top-notch, brilliantly put-together and an asset to any student of Computer Science. To recap: DO NOT BUY THE KINDLE EDITION!
If you do buy the Kindle edition after reading this review, you'll only have yourself to blame.