Editorial Review

Aimed at any serious programmer or computer science student,the new second edition of Introduction to Algorithms builds onthe tradition of the original with a truly magisterial guide to theworld of algorithms. Clearly presented, mathematically rigorous, andyet approachable even for the math-averse, this title sets a highstandard for a textbook and reference to the best algorithms forsolving a wide range of computing problems.

With sample problems andmathematical proofs demonstrating the correctness of each algorithm,this book is ideal as a textbook for classroom study, but its reachdoesn't end there. The authors do a fine job of explaining eachalgorithm. (Reference sections on basic mathematical notation will helpreaders bridge the gap, but it will help to have some math backgroundto appreciate the full achievement of this handsome hardcover volume.)Every algorithm is presented in pseudo-code, which can be implementedin any computer language, including C/C++ and Java. This ecumenicalapproach is one of the book's strengths. When it comes to sorting andcommon data structures, from basic linked lists to trees (includingbinary trees, red-black, and B-trees), this title really shines, withclear diagrams that show algorithms in operation. Even if you justglance over the mathematical notation here, you can definitely benefitfrom this text in other ways.

The book moves forward with moreadvanced algorithms that implement strategies for solving morecomplicated problems (including dynamic programming techniques, greedyalgorithms, and amortized analysis). Algorithms for graphing problems(used in such real-world business problems as optimizing flightschedules or flow through pipelines) come next. In each case, theauthors provide the best from current research in each topic, alongwith sample solutions.

This text closes with a grab bag of usefulalgorithms including matrix operations and linear programming,evaluating polynomials, and the well-known Fast Fourier Transformation(FFT) (useful in signal processing and engineering). Final sections on"NP-complete" problems, like the well-known traveling salesman problem,show off that while not all problems have a demonstrably final and bestanswer, algorithms that generate acceptable approximate solutions canstill be used to generate useful, real-world answers.

Throughout thistext, the authors anchor their discussion of algorithms with currentexamples drawn from molecular biology (like the Human Genome Project),business, and engineering. Each section ends with short discussions ofrelated historical material, often discussing original research in eacharea of algorithms. On the whole, they argue successfully thatalgorithms are a "technology" just like hardware and software that canbe used to write better software that does more, with betterperformance. Along with classic books on algorithms (like DonaldKnuth's three-volume set, The Art of ComputerProgramming), this title sets a new standard for compiling thebest research in algorithms. For any experienced developer, regardlessof their chosen language, this text deserves a close look for extendingthe range and performance of real-world software. --RichardDragan

Editorial Review

Reviews (13)

Cover and Thomas ain't no Robert Gallager
This book is a predictable dissapointment.It is just yet another of the best examples of how modern textbooks have diverged from traditional textbooks of the 1950 - 1970's.Although this book doesn't focus too heavily on the "practical" aspects as far as implementation of information theory, it is clear that todays books are not nearly as mathematically rigorous as the books of yesteryear....

Not an introductory text
My big complaint with this book is that it is written like the math texts of the 1950's. This book has the words "Lemma" and "Corollary" about 5 times per page.

This book is a pure mathematical approach. I prefer a more pedagogical style, where the equations are explained in plain terms, with examples. This text gives the equations and presumes that is adequate.

I would say that this book should only be used by advanced upper graduate level courses, and only after a strong introduction to information theory. As well your probability, stochastic (markov chains), and infinite series skills need to be very strong.

I must stress that this book is written very mathematical. What I mean by this is that all equations are written in the (very hard to understand) shorthand notation. I am sure math majors enjoy that style, but for engineers it can be bothersome having to look up what this and that symbol mean.

While this book is worthy enough to stay in my collection, it is by far the last book I reach for when I look stuff up.

Reviews (9)

What it says, it says best.
If you haven't read this book, you don't know biological string matching. The book's focus is clearly on string algorithms, but the author gives good biological significance to the problems that each technique solves. I came away from this book understanding the algorithms, but also knowing why the algorithms were valuable.

No, there isn't any real source code here. That should not be a problem - this book aims above the cut&paste programmer. The book in meant for readers who can not only understand the algorithms, but apply them to unique solutions in unique ways.

String matching is far too broad a topic for any one book to cover. The study can include formal language theory, Gibbs sampling and other non-deterministic optimizations, and probability-based techniques like Markov models. The author chose a well bounded region of that huge territory, and covers the region expertly. The reader will soon realize, though, that algorithms from this book work well as pieces of larger computations. The book's chosen limits certainly do not limit its applicability.

By the way, don't let the biological orientation put you off. DNA analysis is just one place where string-matching problems occur. The author motivates algorithms with problems in biology, but the techniques are applicable by anyone that analyzes strings.

Definitive String Algorithms Text
If you like definition-theorem-proof-example and exercise books, Gusfield's book is the definitive text for string algorithms.The algorithms are abstracted from their biological applications, and the book would make sense without reading a single page of the biological motivations. Gusfield aims his book at readers who are fluent in basic algorithms and data structures (at the level of Cormen, Leisersohn and Rivest's excellent text).The exercises are wonderfully illustrative, being neither trivial nor impossible.

All of the major exact string algorithms are covered, including Knuth-Morris-Pratt, Boyer-Moore, Aho-Corasick and the focus of the book, suffix trees for the much harder probem of finding all repeated substrings of a given string in linear time. In addition to exact string matching, there are extensive discussions of inexact matching.Even the discussions of widely known topics like dynamic programming for edit distance are insightful; for instance, we find how to easily cut space requirements from quadratic to linear.There is also a short chapter on semi-numerical matching methods, which are also of use in information retrieval applications. Inexact matching is extended to the threshold all-against-all problem, which finds all substrings of a string that match up to a given edit distance threshold. The theoretical development concludes with the much more difficult problem of aligning multiple sequences with ultrametric trees, with applications to phylogenetic alignment for evolutionary trees (an approach that has also been applied to the evolution of natural languages).

Note that there is no discussion of statistical string matching.For that, Durbin, Eddy, Krogh and Mitchison's "Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acides" is a good choice, or for those more interested in language than biology, Manning and Schuetze's "Statistical Natural Language Processing".There is also no information on more structured string matching models such as context-free grammars, as are commonly used to analyze RNA folding or natural language syntax.Luckily, Durbin et al. and Manning and Schuetze also provide excellent coverage of these higher-order models in their books.

This book is not about efficient implementation.If you need to build these algorithms, you'll also need to know how to write efficient code and tune it for your needs.This is an algorithms book, pure and simple.

As a computer scientist, I found the discussions of computational biology to be more enlightening than in other textbooks on similar topics such as Durbin et al., because Gusfield does not assume the reader has any background in cellular biology. Instead, he provides his own clear and gentle introductions illustrated with algorithms, applications, open problems and extensive references.Like most Cambridge University Press books, this one is beautifully typeset and edited.

All about suffix trees
Excellent book on String Algorithms. A lot of material. This is not an easy read, though, relatively not difficult for an algorithms and data-structures book.

This is the most complete resource i could find about suffix trees, how to implement them, usages, and algorithms. Actually, when I took this book, I was interested in suffix arrays. Well - this book explains those better than the original paper do.

Many applications to suffix trees are listed, along with comparisons to other algorithms applied to those problems.

If you need to get into string algorithms from computer science perspective - this is a good book to start. If you want to "feel" of the biologists side of the story, than this is not a good choice.

I use this book as a textbook on the subject, and I'm sure I'll be using it as a reference later on.

Reviews (6)

Thorough Overview of Stats and Algorithms for Speech Rec
This book provides a comprehensive introduction to the statistical models and algorithms used for speech recognition.Jelinek sets up the speech recognition problem in the traditional way as the decoding half of Shannon's noisy channel model. While Jelinek glosses over signal processing, he provides an excellent overview of the symbolic stages of processing involved in speech recognition.

After a quick introduction, Jelinek digs into the statistics behind Hidden Markov Models (HMMs), the foundation of almost all of today's speech recognizers.This is followed by chapters devoted to acoustic modeling (probability of acoustics given words) and language modeling (probability of a given sequence of words), and the algorithmic search induced by this model.There are also advanced chapters on fast match (widely used heuristics for pruning search), the Expectation-Maximization (EM) algorithm for training, and the use of decision trees, maximum entropy and backoff for language models.He covers several auxiliary topics including information theory and perplexity, the spelling to phoneme mapping, and the use of triphones for cross-phoneme modeling.Each chapter is a worthy introduction to an important topic.

This book does not presuppose much in the way of mathematical, computational, or linguistic background.A simple intro to probability and some experience with search problems would be of help, but isn't necessary -- you'll learn a lot about these topics reading the book.

All in all, this is the best thorough introduction to speech recognition that you can find.Read it along with Manning and Schuetze's "Foundations of Statistical Natural Language Processing" from the same series; there's a little overlap in language modeling, but not much.You might want to start with the gentler book by Jurafsky and Martin, "Speech and Language Processing", before tackling either Jelinek or Manning and Schuetze.

Excellent synposis of statistical theory
This book provides an excellent overview of speech recognition technology using Hidden Markov Models.Although Jelinek is clearly speaking with respect to his experience at IBM - he might as well be describing any other commercial speech recognition framework in the world.As a researcher and programmer in the area of speech recognition I regard this book as an excellent reference.It is concise, and I would say that anyone with a reasonable grasp of mathematics should have no trouble understanding most of the topics.In some of the more advanced areas some readers might need to refer to one of reference papers described in the book.I agree with the first reader - destined to be a classic!

Excellent,Unique Book - Destined to be a Classic
This book is possibly the first of its kind - exclusively devoted to Statistical Speech Recognition. The author is a pioneer in the area - one of the 'fathers' of the field,as it were. Thus one expects the text to be authoritative, and it is. The 'information density' is very high - it's a small book, but absolutely packed with information. You'll learn a lot about Hidden Markov Models and their use in Speech Recognition, but it also addresses many other issues, like language modelling and grammar, making it much more than a mere 'speech maths' book.

However, this is definitely not meant for absolute newcomers to the field of speech processing, and it does assume some background in advaced mathematics as well, especially in probability.

If you're looking for other aspects of Speech Recognition or code, you've come to the wrong place - but please don't spoil the rating of an excellent book by complaining that it doesn't have what it never promised to :-) - if you want a solid introduction to the field as a whole, i'd suggest 'Fundamentals of Speech Recognition' by Rabiner & Juang, and if it's code that you're looking for, there's lots of excellent open source stuff available on the net, notably from CMU and Cambridge, and there are some recent books in the market exclusively devoted to implementation of speech recognition systems.

Reviews (9)

very definitive, really a must read
this is an import pre-req to any research/inquiry into this field.

Very technical
Only buy this book if you want a very technical book about this subject. I bought this book because I was generally interested in this research field... and I never read it. If you are a researcher or a student studying this field, then this might be a good book. Otherwise, there are books that you will probably enjoy more.

Reviews (12)

A terrible book
I have to say this is a terrible book. When reading the book, I have a feeling that this book is just a note for the authors themselves. The book is not written from the readers' viewpoint. Some definitions are incomplete; proofs are missing; particularly, notations are confused. I have to continue to guess what the authors truly mean. A good thing about the book is that it covers lots of examples of using HMM for sequence analysis.

Brief and clear
I keep coming back to this book for its readable, applicable summaries of basic algorithms.

One chapter covers the basics of dynamic programming for string matching: a staple of bioinformatics computing. The authors come back to it a number of times as they introduce new variations on the string-matching theme. They give about the clearest description of the Needleman-Wunsch and basic variants (including Smith-Waterman) of any book I know.

The bulk of the book is devoted to Hidden Markov Models (HMMs), as one might have guessed in a book with Eddy as co-author. It covers the basics of model construction, motif finding, and various uses for decoding. Again, it covers all the basics so clearly you'll want to start coding as soon as you read it.

The later sections of the book cover phylogeny and tree building, along with the relationships to multiple alignment. Good, solid, clear writing prepares the reader for texts that may be more specialized, but possibly less transparent.

The next-to-last chapter, on RNA folding, is weaker than the ones before, in my opinion. It ties to the other chapters reasonably well in terms of algorithms, but I don't think it does justice to the thermodynamic models of RNA folding. If there is any weakness in this chapter, though, it does not detract from the strengths elsewhere.

The final chapter, the "background on probability", is the one that I think needs the most support. If you don't already understand its topics, I doubt that this will help very much. (If you do understand them, you won 't need the help.) There's nothing inherently tricky about probability, but individual distributions carry many assumptions, and I did not see those spelled out well.

This shouldn't be the only book in your bioinformatics library. If you really want algorithms, though, it's a good book to have in the collection and one you'll keep coming back to.

Reviews (29)

The best biology book ever.
This book is science writing at its best.The writing is clear and unpretentious.The authors lead the field of molecular biology.And they are superb teachers.

The Best Book You'll Ever Buy
This is a great book.I've used it for all of my undergrad and graduate level classes.It is well written, with complete explinations and in an easy to understand language.It is well organized and has excellent diagrams and images to help illustrate important concepts.This book is expensive but well worth the money.....the only thing I don't like...it's heavy.

Wow!
Taking an online course in Molecular Cell Biology. This is the text.

This is an incredible book. Well organized, very up to date. It is not a trivial book by any means. I recomend that during the first pass on reading an assignment - that you do NOT have a pen at your side, just read it. It is an incredible read. The hard part is the shear amount of information in 1300 pages.