Algorithms Books

Uniting information theory and inference in an interactive and entertaining way, this book has been a constant source of inspiration, intuition and insight for me. It is packed full of stuff - its contents appear to grow the more I look - but the layering of the material means the abundance of topics does not confuse.

This is _not_ just a book for the experts. However, you will need to think and interact when reading it. That is, after all, how you learn, and the book helps and guides you in this with many puzzles and problems.

I am a PhD student in computer science. Over the last year and a half this book has been invaluable (and parts of it a fun diversion).

For a course I help teach, the intoductions to probability theory and information theory save a lot of work. They are accessible to students with a variety of backgrounds (they understand them and can read them online). They also lead directly into interesting problems.

While I am not directly studying data compression or error correcting codes, I found these sections compelling. Incredibly clear exposition; exciting challenges. How can we ever be certain of our data after bouncing it across the world and storing it on error-prone media (things I do every day)? How can we do it without >60 hard-disks sitting in our computer? The mathematics uses very clear notation --- functions are sketched when introduced, theorems are presented alongside pictures and explanations of what's really going on.

I should note that a small number (roughly 4 or 5 out of 50) of the chapters on advanced topics are much more terse than the majority of the book. They might not be of interest to all readers, but if they are, they are probably more friendly than finding a journal paper on the same topic.

Most importantly for me, the book is a valuable reference for Bayesian methods, on which MacKay is an authority. Sections IV and V brought me up to speed with several advanced topics I need for my research.

This is fantastic book. Really takes an intuitive approach to the material. The explanation of occam's razor is worth the price of the whole book. Highly recommended.

I find it interesting that most of the people reviewing this book seem to be reviewing it as they would any other information theory textbook. Such a review, whether positive or critical, could not hope to give a complete picture of what this text actually is. There are many books on information theory, but what makes this book unique (and in my opinion what makes it so outstanding) is the way it integrates information theory with statistical inference. The book covers topics including coding theory, Bayesian inference, and neural networks, but it treats them all as different pieces of a unified puzzle, focusing more on the connections between these areas, and the philosophical implications of these connections, and less on delving into depth in one area or another.

This is a learning text, clearly meant to be read and understood. The presentation of topics is greatly expanded and includes much discussion, and although the book is dense, it is rarely concise. The exercises are absolutely essential to understanding the text. Although the author has made some effort to make certain chapters or topics independent, I think that this is one book for which it is best to more or less work straight through. For this reason and others, this book does not make a very good reference: occasionally nonstandard notation or terminology is used.

The biggest strength of this text, in my opinion, is on a philosophical level. It is my opinion, and in my opinion it is a great shame, that the vast majority of statistical theory and practice is highly arbitrary. This book will provide some tools to (at least in some cases) anchor your thinking to something less arbitrary. It's ironic that much of this is done within the Bayesian paradigm, something often viewed (and criticized) as being more arbitrary, not less so. But MacKay's way of thinking is highly compelling. This is a book that will not just teach you subjects and techniques, but will shape the way you think. It is one of the rare books that is able to teach how, why, and when certain techniques are applicable. It prepares one to "think outside the box".

I would recommend this book to anyone studying any of the topics covered by this book, including information theory, coding theory, statistical inference, or neural networks. This book is especially indispensable to a statistician, as there is no other book that I have found that covers information theory with an eye towards its application in statistical inference so well. This book is outstanding for self-study; it would also make a good textbook for a course, provided the course followed the development of the textbook very closely.

I have used this to get a good background in the topics covered, especially inference theory, and in general I found it to be great book which fills a market gap. The only sins I see are sins of omission. I personally would have enjoyed seeing a more task driven organization. I seem to need these methods periodically but I never seem to need the same method twice. Also, many of the techniques are heavily iterative, i.e., monte carlo, neural networks, etc. This is fine but much of what I do is in the context of simulations where 100,000 step iterative methods don't work so well because of resource constraints. Historically, that has been the problem with many of these methods. They are useful for relatively small domains but don't necessarily work that well for "real" problems. That is probably why more task oriented books are not available. Of course the author is following the outline of the current research into the subject manner which in turn is largely determined by "interesting" and "doable" problems. The real progess in this field will come when the problems are formulated more by what is needed in the nontraditional domains of application. A good example of a useful compression (and identification in some cases) technique that is not covered is Principal Component Analysis. Technically, it is in none of the technique domains covered in this book, but it would have been nice to see some of the methods in the book compared with PCA. The author does make the statement at one point that image recognition is an interesting problem for which the method being discussed at the time is used. Nevertheless, this is a great overview of the subject manner and is very entertaining. That in the long run probably explains the problem: it is a textbook.

Bioinformatics is probably the fastest growing field in both biology and computer science. The problems have come from the computer science department and the biology department having such fundamentally different goals. The computer scientists see the computer as an end in itself with no real thought on trying to do something useful with it. The biologists see the computer as just another tool in their laboratory. And the biological problems are huge, massive computers like the new Cray's and large Linux clusters are being devoted to biological applications.

This book is intended to fit into the chasm between biology and computer science. It discusses computer the algorithmic principles in terms of practical techniques that make sense to the undergraduate biologist. The book is well suited for a first class for the budding bioinformaticist.

Each main chapter in the book first introduces an algorithm, then it discusses the biologically relevant problems that this algorithm addresses, it includes a detailed problem and one or more solutions. Finally the chapter concludes with brief biographical sketches of leading figures in the field.

This is the first book of its type, and it's likely to remain a classic in the field through many editions and many years.

I knew most of the stuff before I opened the first page. It's basically teaching data structures 101 using a few watered down bioinformatic problems for motivation. The lack of applied problems involving real data was most disappointing. It does have a lot of the type questions that some nerd (me one day :P) might ask you on a job interview. The questions are also a good way to kill time if you have nothing better to do. I give the book credit for stressing dynamic programming. I believe that this is one of the most important concepts in problem solving.

3 stars because I think it is a fairly good introduction for fledgling computer scientists BUT not a good reference for comptuer scientists trying to apply their skills to solve bioinformatic problems.

This book gives a broad overview of algorithmic methods used in bioinformatics. It is well writen and the mathematics needed to understand is undergraduate level. Reading this book makes appetite to apply these methods to problems or to dig deeper in the corresponding method.

Overall, a very good book, and due to its introductory level, one can recommend to all people interested in bioinformatics from all disciplines.

This is the first book that I've read regarding bioinformatics, so Im updating this as my class moves along. You better have a grasp of basic data structures prior to beginning this book and background with a programming language as there is very little hand-holding in this text. A bio background makes it all more interesting but certainly is not critical. There are no sample code or sources printed with the book nor is there an included CD nor answers to exercises. There is an associated web site where some ideas may be had and errata found/reported, but its not very active that I have seen. The pseudo code in the book is very python-like so easy to make use of. I personally transfer the book's concepts to C/C++ (habit) without much problem, except sometimes my results differ from the book. Apparently these are book bugs, so be sure to check the web site out if unexpected things pop up.
Presently my class is in chapter 8 (of 12) and looking back I would like to caution that some data processing algorithms will drive a computer's CPU quite hard so be aware of battery-munching & heat. My only bones with this book so far are the alphabet soup of variables and lack of answers to exercises. It would be nice if variable definitions were refreshed at the beginning of pseudo code samples.
I like this book as an algorithms text over traditional texts because the applications are much more fascinating. Imagine searching for something and you don't know where that something is. On top of that add not even knowing exactly what it is you are looking for. And when you do find it, its not even in the data searched! This may sound unlikely or even impossible, but it is neither. Rather, its very cool.
4-stars

Este livro é excelente por várias razões. Entre elas posso citar o fato de estar totalmente voltado ao aprendizado por exemplos, sempre de forma a relacionar um problema computacional com um problema em bioinformática. É um livro muito abrangente, cobre muito bem os tópicos relacionados a alinhamentos e comparações de sequências. Seu capítulo sobre Algoritmos com Grafos é o meu preferido. O autor consegue passar as noções fundamentais com muita simplicidade, de forma que qualquer pessoa possa aprender num ritmo bem rápido.

The only way to go! This is a well written book that is structured like a text book, but stays true to the standard. Excellent overview of FDCT theory and Entropy Coding.

This book is a "must have" for anyone interested in understanding the JPEG standard. It has two distinct parts.
The second half is the JPEG standard itself. It contains all the technical details of how JPEG works, including pseudocode flow charts, and test data to verify JPEG compliance.
The first half is the author's (less formal) understanding of the JPEG standard, where he explains the details of the standard which might be unclear to the novice.
Everything from the aspects of the human visual system, to the mathematics of Discrete Cosine Transformation, to entropy coding, to JPEG file organization is explained.
I knew nothing about image processing before studying this book. After studying this book (for a long time) I was able to write a complete application and have total control over reading and writing JPG files.

I laughed, I cried, it was better than GIF

Created by the Joint Photographic Experts Group, the JPEG standard defines a toolkit of processes for lossy and lossless encoding and decoding of continuous-tone still images. This guide, which includes the the complete text of the ISO JPEG standards DIS 10918-1 and draft DIS 10918-2, offers detailed information on the JPEG modes of operation, signaling conventions, and structure of compressed data. It also provides a general review of image-coding techniques, so it does not assume the reader has expert status in data compression and coding techniques.

This is by far the most complete exposition of JPEG in existence. It's written by two people who know what they are talking about: both served on the ISO JPEG standards committee. If you want to know how JPEG works or why it works that way, this is the book to have. There are a number of errors that were in the first printing of this book that were all repaired in the second printing. The official specification of JPEG is not currently available on-line, and is not likely ever to be available for free because of ISO and ITU copyright restrictions, which makes it valuable to have in this book.

If you study this book in depth, you should be able to write programs that completely control the reading and writing of JPEG image files. This is different from most other books that contain scattered information on the JPEG standard and on image compression, but contain insufficient information for programmers who actually need to work with the standard on a pixel-by-pixel level. Also, considering this book was published by an "academic publisher", I was surprised at its accessible tone and numerous helpful diagrams. Note that if you are interested in JPEG2000, that this book does not contain information on that standard. The following is the table of contents:

Introduction.
Image Concepts and Vocabulary.
Aspects of the Human Visual Systems.
The Discrete Cosine Transform (DCT).
Image Compression Systems.
JPEG Modes of Operation.
JPEG Syntax and Data Organization.
Entropy Coding Concepts.
JPEG Binary Arithmetic Coding.
JPEG Coding Models.
JPEG Huffman Entropy Coding.
Arithmetic Coding Statistical.
More on Arithmetic Coding.
Probability Estimation.
Compression Performance.
JPEG Enhancements.
JPEG Applications and Vendors.
Overview of CCITT, ISO, and IEC.
History of JPEG.
Other Image Compression Standards.
Possible Future JPEG Directions.

This is a very advanced book with many technical details. A must have for everyone working in the field of image compression. For the beginner there is some text in the first part of the book, but I think that this book is primary for people working with actual implementations.

I am not writing a review about the book. I am writing a comment about the seller here on this site. I think it is really sad and quite ridiculous that they have chosen greed over helping the dieing. $98.89 for a book that normally sells for $20, is beyond ridiculous. Shame on you!!!

I hope your books rot on your bookshelves. I will be waiting for the newest editions that will be released in November.

Terrible, terrible, terrible!!! I never received this book. I am a hospice nurse whose patients depend upon me for the comfort that this book guides me to give them, but what kind of comfort can I give if I do not have this book in hand as a reference. Many, many weeks, if not months ago I ordered this book, but I have yet to receive it.
I will not accept the excuses that it was not available, because I have seen it advertised in my nursing journals from lots of book outlets. To keep me as the always-true-blue-absolutely-loyal-customer whose book purchasing monoply you have enjoyed for countless eons, and to keep me from purchasing this book, and taking my business to other outlets, I strongly suggest that you folks get off your collective duffs and ship this book immediately.
Perhaps I have not received this volume because I used your "orders >$25.00 get free shipping" option, and your free shipping is by way of BF Egypt, using slow-mo salamanders as carriers?
Well, you'd better whip up those pathetic little salamandor buggers into supersonic action and get that book to me ASAP!!!!!!!!!!!!! If you fail to deliver this book ASAP, I will curse you with the ultimate of all curses: May the flees of one-thousand camels invade your armpits, plus all your most sensitive body parts/orifices below.
You have exactly one week from today, 8 March 97, to respond, or get ready to scratch!
TTFN, Wendy

This is an excellent pocket reference for hospice and palliative care nursing. I am purchasing it for all my field staff!

I cannot imagine practicing without this book in my lab coat. Indispensible information. Allows you to solidly build a case before approaching the physician. Great reference tables and information for solid clinical decision making.

I've used my little handbook for a number of years now and recently purchased copies for my case managers. They have reported they refer to the handbook often and it is kept with them at all times while they are in the field. It is well worth the money.

I found this book easy to read and the codes are explain in details. I recomend this book for those who are learning data structures.

The only bad thing is that this book is a little outdated.

This book is a good introduction to the real world of computer programming and computer science---if you know C. However, time has moved on, and objects are now de rigeur. C++ is the new C, and a better choice for books would be one that teaches the same subject using C++ (or C#, or Java, or Python, or Ruby, or Perl, or...) Still, for some purposes, such as OS programming, device drivers, embedded application, and so on, C is still the standard, and for those applications, this book will suffice. I would add, it is a textbook that belongs in a classroom, with an instructor present to explain and give alternative examples to those found in the book. Independent-minded people might do better looking for a book written in the last five years.

The book covers the basic data structres, such as linked lists, stacks, queues, hashing, trees, tries, and heaps, but it lacks some of the insights gained since it was written. Red-black tress, skip lists, speed trees, and other similar innovatons are not covered.

Also, this book is not a good book for the study of algorithms. To do that subject justice, a book dedicated solely to the subject is needed. This book only gives those algorithms that are needed to use the data structures the book covers.

In short, this is a good book to read if you already know the subject, but are rusty on it. Those learning it anew are advised to look elsewhere.

Excellent book, full of code and best of all, full with good examples; very important in this topic that is often cover merely as ADT. Stacks, queues, list, trees, everything is covered here. The chapter about trees is specially detailed, and topics like sorting and searching are covered in a separated chapter. I lost mine some time ago, and I did not hesitate to buy it again.

..this book is very easy to read... and there are very very exercices to help in the learning, also the topics of structures data is sufficent vast!

good luck!

If you are interested in trees (Binary Search,Generic & Multiway ) & Graphs this book could be a very good reference. Common Data Structures are also dealt with very nicely.

This is a pretty good book, the ImageJ code is very helpful. Most of the material is straight forward and easy to understand. It does go into the mathematics of many things more than is probably necessary to understand the material.

This book focuses on introducing digital image processing using this programming language ImageJ. This distinguishes the book from basically all other text books in imaging I am aware of.
On the web site [...] additional information (source code, images) can be found.
The colour printing and the quality of the paper are excellent. This makes browsing the book a pleasant experience. It is a worthy addition to the textbooks I have, as it explains the basics of digital image processing without avoiding the essential mathematical background. It is an extremely useful textbook for (undergraduate) digital imaging courses based on Java / ImageJ. For people familiar with Java and interested in imaging, it is double worth the money as a reference book.

This book is just what I expected. It is a great reference to image processing especially with ImageJ

This book is very well organized, and the content is exactly what I have been looking for. It met my needs as far as helping me with my own image processing, as well as for training microscopists in a core facility in image processing. Really really fantastic - thorough content, clear and easy to follow, from basic processing concepts on to specific applications in Java (in particular ImageJ). GREAT!!! I really appreciate the authors translating this - and it is very well translated!

I have been waiting for this book a long time! A German colleague had the German edition and was always pulling out solutions from it. Now, I no longer need to rely on him. This book provides many great explanations and methods that really work, including code (which is also downloadable from the website that accompanies this book). As a medical doctor, scientist, and engineer, I appreciate the practicality of such a technical book that does not get lost in the mathematics. I deal with a variety of medical images all the time and have been looking for a book like this for myself and other colleagues in the field who want to get started in solving their own image processing problems. It has great examples of all the basic methods as well as some pretty advanced ones I am starting to get into. For instance, the color chapter has 70 pages and lots of details. It's also a very good value for a full color 500+ page, 3+ pound book and gives you alot of bang for your buck! I have purchased alot of image processing books in the past, but I am definitely keeping this one...

it is a very helpful book for those who want to get the outline of evolutionary computing. it will offer a solid foundation for further study.

The authors emphasise from the get-go that this book is meant as a practical introduction to the application of evolutionary computing. It is not a high brow, abstruse monograph. (Which indeed Springer texts often are.)

The level of discussion can be adequately understood by someone with a good background in computing and hopefully also in some science or engineering field. Certainly, there are important abstractions that must be mastered. Like how the evolutionary search can be seen as a path across a fitness landscape or potential energy surface. But there appears to be a careful explanation of the minimum necessary maths to convey an idea. And where a chapter's references might point to more specialised texts or journal papers that give a fuller math treatment.

It may well be, as another reviewer remarked, that there is insufficient detail in some passages of this book. But perhaps the text is not meant to be a low level "user's manual" type of discussion.

If you do find this book useful, consider a more advanced text, "Foundations of Genetic Programming" by Langdon and Poli, also published by Springer. It takes you deeper into the subject.

I taught our introduction to evolutionary computation class from this book. It is a well rounded introduction to the topic covering most of the introductorty material you would expect. There is an real dearth of good introductory books for EC. This is probably the best because of its breadth. Its weakness is its lack of detail. It would not hurt if they covered the same material in about 50% more pages. As soon as they start a topic its over and on to the next topic. But if you are new to the field they give plenty of references and touch on most topics in enough detail for students to implement. All in all a good solid job.

I have used evolutionary programming in my research in the past and have read several books on the topic. This is one of the most well written books available, that can easily be read by a beginner despite its depth. The conclusions that they draw are logical and supported by the appropriate references (I was not impressed with the theory and results in the field, but this has nothing to do with the quality of this book).

This is an excellent textbook which covers most aspects of the Evolutionary Computing. It's suitable for all levels. It's easy to follow, rich in content and has many references (439 to be precise) for further information. The table of contents from the book's web site is as follows:

1. Introduction
2. What is an Evolutionary Algorithm?
3. Genetic Algorithms
4. Evolution Strategies
5. Evolutionary Programming
6. Genetic Programming
7. Learning Classifier Systems
8. Parameter Control in Evolutionary Algorithms
9. Multi-Modal Problems and Spatial Distribution
10. Hybridisation with Other Techniques: Memetic Algorithms
11. Theory
12. Constraint Handling
13. Special Forms of Evolution
14. Working with Evolutionary Algorithms
15. Summary
16. Appendices
17. Index
18. References

Recommended to everyone interested in EC.

I strongly disagree with the first comment on this item, entitled "'Cutesy-poo' approach detracts from the content."

As a student in the Computer Science major at Yale University in spring 1994, I used this textbook in Professor Michael Fischer's course, "Computer Science 365b: Design and Analysis of Algorithms."

This book was a welcome breath of fresh air compared to every other title on algorithms that I had ever seen. It described how the study of algorithms need not be daunting, by explaining that every problem at some point did not have a solution, and described in great detail the exploratory process for finding solutions for designing and analyzing algorithms.

Further, it approached the topic in a fun and humorous manner, with numerous quotations and illustrations from works by Lewis Carroll.

It proved an ideal textbook for overcoming the formidable topic of designing and analyzing algorithms for students lacking self-confidence in this topic. This textbook proved to be a key tool for conquering this required course.

Benjamin L. Russell

Algorithms can be very dry and boring. Anyone who has had a boring tenured professor read directly from his textbook during class understands this.

Rawlins accurately captures the essence of solving theoretical problems, and presents it in a way accesible to a computer science major. (Relevant at the senior or 1st year graduate level) You'll come away with less depth than the Knuth title, but much more appreciation and understanding for the How and Why of problem solving.

This book is one of the very few that survived the trip from classroom to permanent bookshelf.

I disagree with the first comment. While I know more about the algorithms and analysys than author probably expected about reader, I found the book interesting and entertaining. It forced me to think again about the old known thinks, brought me new views and new analogies between them. This is very important.

Covers surprisingly wide range of ideas from various subjects. Contains analysis, complexity theory, logic, information theory, probability and more. Don't go to the depth but gives you clear idea what is the topic about.

And I like the style book is written. It force you to read it just because it is fun even in situations I would never read serious algorithm analysis textbook.

I am a senior in computer science & I'm taking my first course on Algorithm Analysis. Our instructor is using another book. Every year he picks a different book. I found this at our library & I've never read a computer book on this subject matter that is so useful. The theme of the book is it guides you thru "thinking about" the topic & all the other ramifications of doing things. You'll find yourself washing dishes differently after this book. Otherwise, if you looking for a better book "Introduction to Algorithms," Corman, Rivest, ..; is the definative text. And of course Knuth's monumental tome.

Reader from the Windy State

The analysis of algorithms is an important aspect of computer programming, and it's actually a pretty interesting study in its own right. That's why the "gee-whiz" tone or cutesy-poo approach Rawlings takes in this book is all the more discouraging -- not only is it a distraction, it was unnecessary. Of course, there are those who like the "cutesy" approach. Some people think, the more pyrotechnics, monkeyshines and theatrics in a classroom, the better. If that type of instruction pleases you, you might like this book. However, for those who aren't studying the analysis of algorithms for yuks, this book is off-putting. The content of this book is that of a competent work, suitable for a first-semester course in the analysis of algorithms. But it is too much work to separate the content from the lamely facetious, precious manner in which that content is expressed.

This is a decent read, teaches you some important topics like collections, strings, etc. But if you buy this book, supplement your reading with a complete VB book.

For some of us learning programming has been less of a structured approach and more of a journey, picking up what we could as we went along. This book is a significant aid in that it takes us back to the basics of how to write problem solving code and at the same time giving us an extended toolbox to apply to how we perform it. It has been a real help to me by reinforcing things to me that I thought I understood, and teaching me new ways to do things that I had no idea on how to approach.
Al Edlund

Michael McMillan's "Data Structures and Algorithms Using Visual Basic.Net" is an excellent resource for the serious programmer using Visual Basic.Net. With the advent of the .Net technology there have been some major changes to Visual Basic, one of them being the collection classes. Most vb reference books provide a weak introduction to the collections and how to use them; this is the focus of the book and really allows the reader to gain enough confidence to start using them effectively in their own programs.



I look forward to more books from this author.

McMillan does for Visual Basic in .NET what other languages like Java, C and C++ have long had. Fundamental data structures like hash tables or hash sets, arrays, linked lists. He shows how to use these in the VB context. They greatly simplify your programming tasks. These constructs are so widely used in any practical programming language.

Without them, you find yourself having to tediously reinvent basic functionality. Adding little value. And with the risk that your implementations might be buggy.

McMillan shows how to use the structures in several algorithms. Having said that, this book will not be of any surprise to someone already familiar with algorithms. It does not go into these to the depth of Knuth's 'Art of Computer Programming', or Sedgewick's texts.

I have selected this book to be the textbook of an introductory course in data structures and algorithms for my second year programming students. There were three major reasons behind this choice:

1- The book doesn't go into the mathematics found in other books. Of course, mathematics are essential for those who want to create algorithms, but it's not the case for the majority of programmers, at least in the business computing field, who just want to implement them. On the contrary, I think that teaching the mathematics underlying the construction of algorithms makes the learning process unnecessarily hard.

2- On the data structures field, the book takes advantage of structures already implemented in the major development platforms. As the author says it: "students of data structures and algorithms can now see how to use a data structure before learning how to implement it" (Preface, p. vii). From an educational standpoint, this is a far better approach than starting to discuss a concept abstractly.

3- The examples are in VB .NET, which I consider a good starting programming language. For those who rather prefer another language, the author has recently published the same book for C#: "Data Structures and Algorithms Using C#".

In a word, I consider this book an excellent practical approach to learn common data structures and algorithms without going into the complexity of mathematics.

I never really appreciated this book until many years after graduating from college. I had this in my library just sitting around. The data structures course I took in school was completely uninspiring. However, when it came to researching and implementing appropriate data structures for my own software, I didn't realize how comprehensive this book is. It covers more algorithms and in greater detail than the ever popular Cormen book. The pascal-like examples are very thorough. There are times when code is not provided, but it gives you enough detail to extrapolate from what is already there to get started.

This is one of the 2 texts we've had for our data structures course. I'm not sure what the other book is, because this one was so good. It covered the material in a clear and precise way. I even have a copy of this book today, for further reference!

I seriously like this book. It's explaining is close to crystal clear to me when I read it, and the algorithms listed (in pseudo-code) take it to a practical level.

One of the best ways to discover the strengths and weaknesses of a textbook is to teach a course using it. A few years ago I taught a senior-level course on Data Structures using this book. The book was a joy to teach from, and I would happily use it again -- I thought it was one of the nicest textbooks I've ever used.

(In case you haven't figured it out from the above paragraph, I believe that Paul Schreiber's review of this book is far too negative.)

In this book, Lewis and Denenberg attempt to explain data structures and associated algorithms. They rely too heavily on obscure proofs, have few, if any worked-out examples and many ambiguously worded questions. Their assesrrtion that a "high school" math background is needed is clearly false. The book also suffers from poor typesetting.