Algorithms Books

Cinnamon Gardens is set in 1927 Colombo Sri Lanka, in an elite section of the city. The novel traces the lives of two relatives, Annalukshmi and Bala. Annalukshmi is a bookish teacher who is the eldest of three daughters. She is facing pressure to marry but she wishes to remain a teacher. Unfortunately for Annalukshmi, having a career and marriage are mutually exclusive. Bala is the son of a prominent Colombo citizen who is a closet homosexual. When his former lover from London appears in Sri Lanka to work on a commission about the future of British colonial rule in Sri Lanka, he is forced to confront his past and come to terms with the fact that he gave up his true identity to satisfy his father. The book traces the political issues of the time as well as critiques the social mores and is a very interesting look at the conflicts inherent in colonial society.

I could almost smell the spices and taste the scent of flower-laden air. The social web of demands is sensitive and true to the environment. The book is romantic and sensuous, hopeful and pitifully sad. It teems with atmosphere, politics, intrigue and disappointment and sacrifices made to maintain a better world. The ending was unsatisfactory. One star character showed her maddening and perhaps arrogant side. Somehow, the decisons made didn't ring true. Nevertheless, such are one's choices, and I recommend this novel without hesitation.
I have been told Cinnamon Gardens was recommended by a well-respeccted university as reading material prior to an academic and cultural trip the institution was leading there.
To understand Sri Lanka today, you must read this.

A novel very well written in which the individual stories of each one of the personages interacting themselves in their emocional or family relationship represent each one of the political, religious or social tendencies of the colonial Ceylon in the 1920s.
Cinnamon Garden, an aristocratic neighborhood in Colombo, Ceylon is the setting of this story where cingaleses, tamils, mixed raced and brittish live together and debate themselves between their millenary customs or the new one introduced by the colonialist, the christianism or the traditional religions, the social liberalization or the cast prejudice, the colonial status quo or independency, traditionalism or progress, a debate that confront each one of the personages and ruled their lives.
A story masterly written recommended for those readers who enjoy a novel where history and manners mixes with a good trama.

This novel has the culmination of different kinds of human rights. Gay rights, women's rights, class rights, labour rights, ethnic rights, religious rights, and the right to self-determination. Everything that the multicultural, multireligious, multiethnic Sri Lanka represents. Taking place in the 1920's, against the backdrop of British colonialism and Ceylon's struggle to gain independence, the author cleverly weaves the story of Balen and Annalukshmi. Balen, a gay Tamil man who is unable to go against the constraints of family and society, finally comes to terms with his true self. Annalukshmi, a career minded Christian Tamil woman who is coerced into arranged marriage, finds her peace in an unexpected turn of events.

CINNAMON GARDENS is basically a period romance novel set in 1920s Ceylon that has intelligence and a literary sensibility. In the character of Annalukshmi, author has created a spunky heroine obviously modeled on Jane Austen's Elizabeth Bennett. The book is at its best when it blatantly imitates Austen, with its matchmaking and calculating future incomes. But what really animates the book is the struggle of the characters to conform to society's expectations without sacrificing their own individual happiness. Some, like most of the women and the closet homosexual Balendran, cannot break free of the bonds of tradition. Others, like Balendran's brother and the lower caste people break the bonds but pay the price. It is very compellingly written. I read it rather quickly. However, I was ultimately disappointed. I felt that the ending was a big concession to current political correctness and ended with a group of modern cliches. The current cliché is that when a woman is confronted by a choice she must make between two men, the politically correct decision is to decide she doesn't need either of them because she's a strong W-O-M-A-N. And I didn't buy any of it. I also question whether a person of Balendran's particular time, place and upbringing would really "come out" to one of the characters he comes out to. I wish the author had just given the reader what he promised at the beginning. I would have appreciated a straightforward romance a lot more than what I ultimately got. I felt a little like I had been the victim of bait-and-switch by the ending. But the author is very talented and I would like to read his other work.

The material is very superficially laid out and for a book with the word "Practical" in the sub-title it contains almost no practical examples of data mining.

Highly recommend this book for a practical introduction to the theory and applications of Machine Learning. Great book if you are looking to ACTUALLY implement some machine learning systems, prefer to learn via diagrams, a "how-stuff-works"-style explanation, and skip much of the equations and heavy math that fills similar books.
Obviously, this book is a perfect companion to the Weka machine toolbox, which is quickly becoming a standard, invaluable research toolbox for many.

This book was pretty good. I have to admit that for the first hundred or so pages, I was feeling very impatient. All of that information could have been conveyed in about 25 pages, and been much easier to read. But there are some very good examples in here, and it is worth reading. If you are looking for something more technical, try "Pattern Recognition and Machine Learning", by Christopher M. Bishop or "The Elements of Statistical Learning" by Hastie, Tibshirani, and Friedman.

I am very happy with amazon purchases as they always come quick, as described. I love the free supersavings shipping program. Prices are charged in the middle (not the chepest, not the highest) but I know I can always rely on Amazon! Every time I have something to buy online, I go to Amazon.

This book reminds me of the programming books by Deitel&Deitel. It's wordy and superficial, making lots of people feel like they understand the subject. Unfortunately, it takes *much* more than what's in this book to really understand Data Mining. Compare this book to the book by Hastie, Friedman and Tibshiranie, which really goes into the statistics involved in Data Mining.
There is no magic: real Data Mining needs lots of Statistics. You can learn to use Weka, but in order to do real work you'll need to understand what goes behind its nice user interface, and I think this book is not enough.

If you are looking for a relatively rigorous but very readable data mining book, this is simply the best! It covers most of the modern techniques and is beautifully printed with high quality graphics.

I'm a machine learning person, and this book provides pretty thorough state-of-art and up-to-date (relatively well) summary of statistical methods being used in lots of pattern classification fields. One thing that does not exist in the book is generative models, although this book is the best of the kind that describes discriminitive models.

This book describes most of the important topics in machine learning. Most machine learning books just present a criterion and and an optimization algorithm. For instance, LDA is often presented as: here is the Fisher criterion, it seems like a good thing to maximize. "The Elements of Statistical Learning" also presents that this is the right criterion if the distributions of the data for each class are Gaussian with the same covariance. This book puts all the algorithms in the same statistical language, which makes them easy to compare and choose between.

I also appreciate the emphasis this book puts on algorithms that are more recently popular/effective. I very much appreciate the discussions of logistic regression vs. LDA, ridge and lasso regression, boosting/additive logistic regression and additive trees, decision and regression trees, ...

The only qualm I have with this book is that it is rather biased toward the authors' own research. It is difficult from reading this book alone to differentiate between classical techniques and the authors' recent proposed algorithms.

Data mining is a field developed by computer scientists but many of its crucial elements are imbedded in important and subtle statistical concepts. Statisticians can play an important role in the development of this field but as was the case with artificial intelligence, expert systems and neural networks the statistical research community has been slow to respond. Hastie, Tibshirani and Friedman are changing this.
Friedman has been a major player in pattern recognition of high dimensional data, in tree classification, regularized discriminant analysis and multivariate adaptive regression splines. He has also done some exciting new research on boosting methods.

Hastie and Tibshirani invented additive models which are very general types of regression models. Tibshirani invented the lasso method and is a leader among the researchers on bootstrap. Hastie invented principal curves and surfaces.

These tools and the expertise of these authors make them naturals to contribute to advances in data mining. They come with great expertise and see data mining from the statistical perspective. They see it as part of a more general process of statistical learning from data.

The book is well written and illustrated with many pretty color graphs and figures. Color adds a dimension in pattern recognition and the authors exploit it in this book. It is really the first of its kind that treats data mining from a statistical perspective and is so comprehensive and up-to-date.

The important statistical tools that are covered in this book include under the category of supervised learning; regression, discriminant analysis, kernel methods, model assessment and selection, bootstrapping, maximum likelihood and Bayesian inference, additive models, classification and regression trees, multivariate adaptive regression splines, boosting, regularization methods, nearest neighbor classification, k means clustering algorithms and neural networks. These methods are illustrated using real problems.

Similarly under the category of unsupervised learning, clustering and association are covered. They cover the latest developments in principal components and principal curves, multidimensional scaling, factor analysis and projection pursuit.

This book is innovative and fresh. It is an important contribution that will become a classic. The level is between intermediate and advanced. Good for an advanced special topics course for graduate students in statistics. A comparable text is the text by Mannila, Hand and Smyth.

This book made effective use of color and maintained a competitive price. This had a major impact on publishers like Wiley that could not sell a book at this size and initial price. Wiley is still looking for a book comparable to this one that they can use to compete with Springer-Verlag. I know this information because I heard from the Wiley acquisitions editor that I worked with on my two books.

i really like this book. i haven't finished reading yet. it's extremely dense. by that, i mean every page, every paragraph is packed full of information. it makes for slow but very rewarding reading. i bought the book because

i wanted to learn something about the topic. i've got a math and statistics background, but i haven't dealt with the broad topic of data mining or statistical learning. the book suits my needs very very well.

it's clearly written. i haven't found any grammatical or technical errors. it's pacing is ambitious, but i find i can follow it. i do think some math and statistics background is required to make the book readable and useful.

i wouldn't hesitate to recommend it to someone with the appropriate background.

Dieses outlet bietet eine sehr gute Zusammenfassung der meissten Aspekte von C++. Es ist kein Anfängertutorial und auch kein Referenzbuch, eignet sich aber sehr gut zum Nachschlagen.

Die Erklärungen sind, im Vergleich mit anderen Büchern, kurz und sehr gut ausgeführt, zusätzlich bietet es noch viele Beispiele zur Vertiefung. Es ist in einfachem, leicht verständlichem Englisch geschrieben. Durch das praktische Format kann man es auch leichter mitnehmen als 1000 seitige Wälzer.

Besonders für die guten Erklärungen und den Preis vergebe ich die vollen Punkte.

The book is good but it doesnt cover struct's and unions. unions are worth there weight in gold in data i/o programming. The book has not got a mistake on it as the "Shaun G Pearson of nova scotia put it". Its using the newer C++ library and is part of it. (...) I suppose the book doesn't want to go to far into C stuff. C is essential to writing clear and concise code in C++. But at the price the book is at its a good buy. I've got it.

Very informative introduction to C++. I was new to C++ and most textbooks are hard to understand and hence requires a teacher to explain everything. I started reading it halfway through the course and got a C+ when I was expecting an F.

This is an excellent supplement to most of the college level textbooks in C++ programming. Many authors of C++ textbooks are taken up by the apparent need to illustrate complex object oriented concepts (lest Java be seen as the alpha and omega in object oriented languages)with long winded "case studies" or "programming projects" that seem contrived (see any book by Deitel for examples of such.)Some other authors think that their exercises and examples are places to showcase their ingenuity and possession of programming tricks (see Daniel Liang's "Displaying A Pyramid Of Numbers" in Chapter 3 of Introduction to JAVA Programming, Comprehensive Version, 5th Edition, as an example of such.) But what about simple exercises to see if you've mastered the basics of arrays, loops, and simple functions? Let's face it, it will be a couple of years before you'll be asked to work on the code for an ATM machine (Deitel), in the meantime buy this book and focus on the basics.

Who referred to this book having mistakes:

They seem to be stuck in the stone ages of C programming.

Since the new ANSI standard came out, (which compilers are still attempting to catch up to), header files have not used a .h extension, C Standard library headers have been renamed and so on.

Every negative point the person makes indicates a complete lack of knowledge, especially accurate knowledge of the C++ standard.

As such that review should be completely and utterly disregarded.

This book is definitely head and shoulders above the crap that people like Herb Schildt have been putting out.

Thanks.

The editorial review is kinda deceiving, so I'll just write what's missing on it: The book tries to balance between theory, practical examples and interviews.

Which may be great to some, but not that great to others.

The book is basicaly structured in this way:
30-50 pages of Theory
5-15 pages of a practical example (something about the theory on an actual game)
15-40 pages of Interview (with some famous game designer... which might be good if the reader knows their games, and might be bad if the reader doesn't, since not much of it is exactly "game designer" content).

That structure is repeated through over and over the book's 677 pages.
But don't get me wrong, the content is still very good. Cover lots of stuff from developing the game concept, to more technical stuff like AI, Multi-playing, Level design and playtesting.

So, a good book that covers lots of stuff on game design without going too deep in specific stuff.

I am currently using this as my Game Design textbook. As a student, it provides not only mere theory, but valuable interviews with upstanding designers. It is clear, and a joy to read.

I got this book to help in a class since it was a course requirement. It has alot of good info and intangibles about game design that is very helpful to anybody that would want to know.

This is a great walk through for a beginner. I would recommend it for its overview and insight into games and making games. I played a few more games because of it and was able to hone my eye to know why I enjoy and what I look for in games. Great first book; if only for the interviews.

Being an avid gamer, and dreaming of working in the video game industry, I pretty much knew everyhing there was in this book. However, if you're not like me, then I think you will find this book useful.
The book goes from the beginning stages of video game design up to the completion of a video game. It gives really good information about what makes a game good and not tired and done before.
The interviews of the game designers are kinda helpful; it really depends on if you already know the designers work and are familiar with it.
This is a non-technical book though, so it won't tell you how to code a game or make models. It basically tells you all of the intangibles you can't learn in a class or really anywhere.

It's worth the read.

This book bored me to tears. It reads like a text book, theres rarely an example. I don't get any explanation of what tools to use, how to set them up, or anything that i can actually work on while reading the book.

For a 'programming book' i felt this fell way short.

I did learn a lot of concepts by reading it, i just didn't learn any real C++ programming techniques, because they didn't give me enough background to get started in any compilers.

I find it telling that the author's bio does not mention any games that he has worked on, because it quickly becomes apparent when reading it that this is *not* the advice of a seasoned developer, and I doubt that he has worked on anything more than very small demos. The book does cover a broad range of techniques and algorithms that are applicable to game programming, but it seems that the author's knowledge of these techniques is of an academic nature - gleaned from reading other books or newsgroups and a little dabbling in his spare time, not from professional game development. As such, it provides an introduction to quite a lot of topics, but as soon as it comes to adding more detail on implementation or practical advice, he suddenly becomes very vague and occasionally just plain wrong. Furthermore, his programming style is very academic (smells of unix) - not of the standard expected of a professional programmer.

The book may serve as a good warm-up for a student wishing to enter the industry, but for anybody with a year or two under their belt it is unlikely that they will find anything here to improve their skill set. This does not necessarily make it a bad book, as there are plenty of people out there who want to get into the industry and don't know where to start, and more in-depth books would probably create information overload. However, the book would have got a higher score if it had included the word "introduction" in the title somewhere. As it is pitched, as a reference book for the industry, it is a definite disappointment.

The content in the book definetly adheres to the title of the book. It covers major design patterns used in the current video game industry, and always keeping in mind to stick with the logic behind many game aspects, and not just merely giving out code. Tho, not meant for completely new video game programmer wannabes, it does anticipate and appease the needs of one who would want to start their own video game project. Before going on and buying this book, the reader should be very experienced with key programming concepts such data structures, ai, and programming in general. Furthermore, readers should have already created simple two 2d games such as tetris, a 2d scroller, and other related game material. For instance, many topics in the book are better understood by someone that has some game programming experience, because they can actually relate to the books content and understand it. In conclusion, it deserves 4 stars simply because of the fact that it lives up to its name and the target audience!

This is an ambitious and expensive book. In my opinion it isn't really worth the cost. I have two major criticisms: first, there is no CD with code samples, and no color plates for showing off graphics. Second, it is *very* poorly edited, and the code formatting is so bad as to be almost unreadable.
For example, on page 576 on reducing texture memory requirements the author talks about how using RGB4 is generally okay if you don't have fine color gradients. This is correct. He then refers to an unnamed figure (which we find on the next page) but swaps the left and right side (presumably) defeating his point. He then later refers to the figure, by number, but with completely incorrect content. The actual picture referred to was apparently removed, and the wrong one got its number. This is not an isolated incident. Page 329 has a nasty (yet typical for integer divides) off-by-one error.
Further, I find he often uses pseudo-code where you would want details, and real code where you would want pseudo-code. And the coding style used is also poor, especially when one thinks it is meant to be an example to new programmers. Often #define's are written exactly like normal variables and are used when enums would be much better, upper case and lower case are regularly switched when referring to the same variable, long, unreadable, all lowercase variable names are used, constants are not brought out of loops, braces are not matched up, erratic use of whitespace, etc.
I also found discussion often sadly lacking. The book is admittedly already large, but much could be cut out that isn't interesting or germane (e.g. pages of badly formatted code, or mixing force-feedback effects for DirectX which belongs in a DirectX book). For example, in discussing A* searching no mention is made of using pessimistic heuristics, which in practice can dramatically improve performance. In a very brief section of Design Patterns, no discussion is made about why the author thinks its better to subclass strategies rather than use function pointers.
Some statements are just wrong: "As anyone familiar with algorithm theory knows, sorting a list of N numbers needs at most N log N comparisons, and no algorithm can perform better than that (in a worst case scenario)." Well, in fact, if you can meet certain criteria, you *can* sort in O(N) time (even in the worst case), and many "standard" algorithms require N*N comparisons in the worst case.
There are some good points. The history of games was quite interesting, as was the review of tiling, sprite, and palette techniques. And, if you're a complete beginner to programming, there's a fair bit of useful information, and I'd give the book 3 stars instead of two. Even experienced game programmers will probably learn something, or at least be pushed in that direction. The explanation of BSPs was quite good, for example.

All in all, disappointing. For reference, I've programmed the PlayStation (one) for Electronic Arts, and more recently done gaming stuff for mobile phones, and have a background and interest in gaming, 3D graphics, and AI.

I have read about half of this book so far. I was very excited about this book while reading it because it does contain a lot of useful general information. However, if you are looking for actual code examples...this book falls short.

I am writing my own game engine right now and I went to the book to use an algorithm it had listed...except the algorithm doesn't actually work! i tried to debug the algorithm for hours before giving up and writing my own. (Which was a good exercise in itself) Once again I turned to the book for an algorithm it listed, but the explaination of it was so bad I decided to write that one myself too.

So...if you are a programmer already this book will be useful to you because the code snippets provided are less important that overall concepts which is why i bought it in the first place. But if you are a beginner programmer looking to learn how to code, I wouldn't recommend this book to you.

This book is undoubtedly useful for any practicing video engineer and for anyone seeking to familiarize themselves with video standards and technology. It covers a broad range of topics under one cover. Depth is lacking in most parts, but then this book is not intended to be used for theoretical study. It is a handy guide to have at one's desk.

My biggest and only complaint is about the way the material is presented, which, in my opinion, is highly unstructured and makes the book much more difficult to read than it needs to be. In almost every chapter, the author asks the reader to refer to the material in both future as well past chapters for details. As an example, the section on nonlinear image coding, on p.12, refers to the material in different chapters on pages, 197, 198, 203 and 257!

Nonetheless, despite this writing flaw, the book is recommended.

Not an easy read for beginners. If you are serious about video (i.e. work related), get this, it is recommended reading in the field.

I buy and read a lot of technical books--there are hundreds in my library--and this is one of the best in any field. An adequate technical book has to be accurate, reasonably organized, provide a good selection of material, and be understandable. A good book will engage your interest and impart general knowledge. But a truly great technical book will leave you with deep insights that make you smarter. This is one of those books.

The text is lucid and the illustrations are uniformly excellent. I particularly like the layout, which leaves a column to the left of the main body text for references, notes and comments. The author deeply understands the material and conveys that knowledge beautifully. This is one of about 5 technical books I've ever come across that's hard to put down.

A minor production nitpick is that the paper shear was was a bit dull when they cut my copy, leading to slightly rough edges on the left-side (even-numbered) pages, but it wasn't bad enough to exchange the book. Otherwise the binding and paper color and quality are top notch.

I was new to video and was looking for a good book and a colleague suggested this as comprehensive and easy to read book. Each chapter explains a single concept and is around 10 pages. Complexity of the subject matter gradually increases. I read one or two chapters in each sitting and took a month to complete.

About back and forth references criticism: Author explains further concepts in few sentences whenever they occur. He also suggests the page numbers where more details are available. e.g. He mentions very early what resolution meant, and but refers to complete chapter on resolution. I did not need to back and forth, as the initial explanations were sufficient enough for reading the current chapter. I attempted to read few other books before this. I felt those books assumed few fundamentals and they were good reference manuals than introductory books. I had to google for few words even in the first chapters. On the other hand, this book is both introductory and also a mini reference.

I recommend this book in the following order of importance.
* If you are new computer video engineer, it is a must have book. Without reading it, it takes few months to understand the subject and you may have gaps in learning.
* If you are a digital TV and video electronics engineer, it is good to have. It extends your knowledge and is a good reference to standards and compressions.
* If you are moving from computer imaging background to video engineering, it is good to have. It explains concepts related to both graphics and video with similarities and contrasts and helps in easy migration.
* If you are personal video maker and at the same time tech savvy person, it is good to read at least once. It gives good background picture and also explains every buzzword you hear in frys and online.
* If you are general computer or electronics engineer, it is nice to have in your library.

Missing details for computer video engineers are video file formats and container techniques.

I wanted to learn about Digital Video and bought this book. I had some very basic understanding of related concepts. Even with that I found it difficult to follow this book.

I think Charles Poynton knows about his subject well but the way back and forth references are used without explaining a concept completely makes you feel you have not understood something completely. For example,

Page 50 of book will start introducing a concept on lets say interlacing. And then it will have a note saying that "I will explain more on page 400". When you go to page 400, it will have a note saying based on the fact on page 300....

So if you want to buy this book and read it, better make sure that you have good basics on Digital Video and related concepts.

Thanks

I have to agree with all of johnnied7 criticisms. This book is pitched at a level too advanced for an introduction. It also reads and is structured like a research paper. Not recommended.

I have an engineering degree, and I found this to be a little tough to follow for two reasons:

1. Not enough step by step prodecure especially at the beginning. Mitchell is too quick to start with the math formulas. It turns out that Genetic Algorithms are fairly straight forward and easy to follow, but you have to read this book twice before you "get it" because Mitchell clouds the discussion with proofs and mathematical representations of systems. It is tough to follow.

2. Mitchell does a poor job of selecting meaningful examples to illustrate the points. A nice simple set of examples where the average person easily picture the system would have been delightful. Instead this author chooses to illustrate the Genetic Algorithms through uncommon neural networks amoung other exotic applications. I found myself struggling to understand both the example (I didn't know a thing about neural networks!) and the genetic algorithm.

When buying an Introduction type book, I expected it to be more 'down to earth'. this book is for advanced minds!

This book primarily deals with the theoretical side of genetic algorithms. If you are looking for practical knowledge of how to implement a GA you should look elsewhere. For all intents and purposes this is a textbook. It's heavy on theory and proofs, but doesn't always explain everything in depth (that's what class time is for). There are problems at the end of each chapter that can be assigned to students.

There are case studies of many academic projects that seem to drone on forever and aren't really that useful in helping you learn how to write your own GA. Chapter 1 gives an overview and provides all of the appropriate terminology. Chapter 5 gives an high-level overview of how to implement a GA. Those are the 2 must-read chapters, all of the others can be used as torture for CS students.

To recap, if you're teaching a class in artificial intelligence this book is good. If you're trying to figure out how to implement a GA to solve a practical problem not so good. That evens out to 3 stars for my rating. I recommend searching the web, there are a few good sites on GA programming.

First it must be said that the book is not an introduction that the non-scientist will easily understand. Some knowledge of computer programming is assumed. It acknowledges this in the last paragraph of the preface. Many of the notations in the book are unfamiliar to business or financial readers. There is no mathematics beyond algebra so the aforementioned prerequisites are the main hills to climb.

Mitchell's book is an overview of genetic algorithm analysis techniques as of 1996. The author gives a history of pre-computer evolutionary strategies and a summary of John Holland's pioneering work. A description of the basic terminology is presented and examples of problems solved using a GA (such as the prisoner's dilemma). The second chapter discusses evolving programs in Lisp and cellular automata. Also included in this chapter is a discussion of predicting dynamical systems. This was the section that has the most interest for me. Also interesting was the summary in this chapter about putting GAs into a neural network so that the ANNs could evolve.

The fifth chapter discusses when to employ a GA for maximum success. I appreciate the clearly thought out discussion of when to choose a GA for a problem. Sometimes authors of these types of books mimic the man with a hammer that thinks everything looks like a nail.

This is a great place to start to learn about genetic algorithms. The writing is clear and not bogged down by jargon. The book is not overly technical; it is written for the layman and has a casual conversational style that is a pleasure to read.

About half of the book is devoted to presenting examples of studies that have used genetic algorithms. These examples are interesting in themselves and also serve to illustrate the variety of genetic approaches that are available. The book also presents conflicting points of view of experts about which algorithms work best and why. This is helpful in combatting the impression that a beginner sometimes gets that everything is simple and all the answers are known.

This is a very accessible introduction to data structures and algorithms in Perl. It doesn't go into a lot of theory, it isn't going to answer your computer science homework, but it does give a good feel for the various applications of algorithm research.

Plus, the code is all in Perl, which is not as unreadable as received wisdom asserts. It's certainly more accessible for the interested Perl-savvy amateur than the pseudocode in Introduction to Algorithms.

Obviously, you're going to have to move onto the likes of Cormen et al, if you're really serious about this stuff. And practically speaking, yes, most of this can be found in CPAN without you having to worry your pretty little head about the mechanics. If just getting something done is your main concern, then this is not the book for you.

Plus, it must be admitted that the level of detail varies across the chapters, and some of the explanations can be opaque, even for the simple stuff. I felt I had to work unnecessarily hard to comprehend some of the material: the discussion of the A* algorithm, some of the tree-related algorithms and the section on compression all suffered from this to varying degrees. This is the sort of book which requires concentration (plus copious scrap paper for scribbling down arrows and boxes) to get anything from.

But to complain that Perl doesn't need you to write these data structures from scratch, and it isn't a suitable language for this sort of thing anyway, is to miss the point of at least part of the book. It's about communicating the intellectual pleasure of wrapping your head around these fundamental bits of computer science, and in that respect it succeeds admirably. If you're looking for an introduction to the area, this is definitely worth getting hold of.

If you have ever studied algorithms and data structures, then balanced trees and O(N) notation may still be a painful memory. Also, part of Perl's strength is in its built in parsing and sorting features so why would you need to know other ways of doing the same job? If your work does not challenge Perl's features then you probably do not need this book. However, if you have no computer science background and Perl is your language of choice then this book requires serious consideration.

The book is concise and the advice given in concepts like choosing an appropriate data structure or in benchmarking your program is actually quite sound. It covers a wide number of topics such as sorting, searching, sets and matrices together with material you may not find in a data structures book like geometry, cryptography and statistics.

Your choice depends on the task at hand. If you're looking for a Perl book where you can find routines to encrypt a string or find the maximum distance between two points then this book will not disappoint. Indeed, I believe that anyone serious about programming would benefit greatly from some of the Computer Science subjects discussed and implemented in Perl that are offered in this book.

This book is a great book, not only on the subject of algorithms, but also on how to implement them in Perl. A huge number of topics are covered, from Data Structures, to Searching/sorting, to cryptography, and much more. And what I found to be among the most useful additions was that for every subject, they give you a full implementation on the subject. I've read a few books on algorithms, but this is one of the most easy to read, and definitely one of the most practically useful. Recommended for any Perl programmer, regardless of skill.

Klowledge of algorithms and data structures is vital for effective programming, and Perl is one of the most popular programming languages around, so this book fills a long-needed niche.

If you've ever looked at "Introduction to algorithms " by Cormen et al (CLR), this book will look familiar. It covers many of the topics covered in CLR, though not in such theoretic depth. It does, however, have mountains of Perl code implementing those algorithms.

This book can seemingly have two purposes - one is to learn algorithms (as the title suggests), and the other is to understand the implementation of algorithms in Perl.

IMHO, the authors fulfilled the second part quite well. For the first part, CLR is a excellent book and is hard to better. I don't think "Mastering algorithms" explained the topics in a clear enough way to compete with CLR, but it can indeed be a terrific companion to CLR (get the first edition, used copies cost pennies). Read about the algorith m in CLR, understand it from the pseudo-code and diagrams, then take "Mastering algorithms with Perl" and learn the Perl implementation of the algorithm.

As a guy ( engineer not computer expert ) who uses computers everyday to help his research, I would steer you away from using Perl for any task involving mathematical concepts more complicated than addition/subtraction/multiplication and addition.

I heard this same advice before buying this book and ignored it, I really wish I had listened back then.

While MAP has some nice pictures which broadly describe the essential concepts, it will give you no idea as to how to actually implement those ideas. Further, all the code is available in CPAN ( If you don't know CPAN, check it out before going any further - at the very least install a module ) and much ( at least what I attempted to use ) appeared to be broken.

Authors of computer books are usually good about answering e-mail but these authors did not deign to respond to mine.

If you are out there, struggling to learn algorithms, I would suggest taking a good computer course on the subject. I'm 99% certain the course will be taught in C/C++ or similar language -these languages have tremendous advantages over Perl when it comes to data structures and, believe me, even as a novice I've come to appreciate them...

If you really know algorithms and wish to write a few in Perl, you can do without this book. Pick up Deitel & Deitel's 'Perl: How to Program' instead or O'Reilly's basic book ( which is good, but I prefer Deitel and Deitel ) ....besides D&D answer their e-mail.

This book is excellent. However, you need strong training in the kind of reasoning used in math and CS theory before you can read it. The subject gets very abstract, and may be hard to follow (and that's not Papadimitriou's fault).

I would recommend it for people who have already read Sipser's book (working on the exercises), for example.

This is a good introductory book of computational theory for students in computer science, good juniors, seniors and first year graduates. The book is well presented, fit for self studies, and covered most contents of computability and complexity. The book is slightly old, some of the latest result are not included, e.g., a P-algorithm of solving "prime problem" was found in 2001. This book is not good for advanced researchers in theoretical computer science, it is way to shallow. Compared with Martin Davis's book, this is easier to understand, equally well presented. Be sure not to get the $8-9 version, that is not the book, although under the same title.

I am a research in theoretical algorithms.

If your purpose is to learn something. This book is really bad at teaching you.

The author assumes many things. He has no idea of building things in a gradient. He leaves out the details of how something was arrived at.

If his purpose is to show off, then he has achieved. If his purpose is to create a text that is readable and understandable. He has failed.

A well-written book that teaches you how to think about complexity theory instead of just a flat summary of results. Something like Lewis and Papadimitriou's _Elements of the Theory of Computation_ would be more than enough preparation for this (note that the style of these books is quite different- this one is more informal and descriptive). Covers all the material you need in a first text. Has a good little introduction to mathematical logic in it, including a nice succinct version of Godels Incompleteness Theorem. Lots of interesting exercises.

I agree with the review by Arthur Fischer. Papadimitriou might
be an excellent researcher, but his communication skills are
hopeless and horrible. The typos make learning even harder.

Perhaps someone like Michael Sipser should take up the task of
rewriting this book.