Reviews (23)

A very practical reference in designing algorithm
Nice to have this book handy if you need to design efficient algorithms for your programs. This is more of a reference than to teach you what algorithms are out there, so you better know them before using this book. That said, this book is quite unique since it differs from a typical algorithm book on the market. Skiena has done a great job in presenting the material. I find this book really handy, definately recommanded.

Covers an Important Middle Ground
This book fills a nice niche -- it is practical enough to be useful and accesible to professional programmers (rather than algorithms researchers or academics) but is build on solid theory as well.

Aside from this, the book has several features to recommend it:
(1) There are "war stories" scattered throughout the text.These are special sections that describe the author's experience working with algorithmic problems "in the field".These are particularly interesting because the present false starts and failures along theway to the final solution.This is a nice change from the standard model of simply presenting algorithms and proving them correct.

(2) Unlike others, such as the popular Cormen, Lieserson, Rivest and Stein text -- which is a bit advanced for beginning study -- this really is an introduction to algorithms. It is quite suitable for, say, undergradutes who have taken a couple of basic CS courses.

(3) Chapter 8 is a 250-page "Catalog of Algorithmic Problems".Here, you will find descriptions of hundreds of standard formulations for algorithmic problems along with the basic solution approaches, and -- this is what really sets it apart -- pointers to implementations of these algorithms.This is part of the overall emphasis of the book: that of understanding standard algorithms in order to avoid "reinventing the wheel".

Overall, I recommend this book for people with some programming experience that would like to take their work to the next level.Hardcore computer scientists (especially those with a more theoretical bent) may also benefit from this book, but should consider it a supplement to one of the more advanced texts, such as the above-mentioned CLRS.

Interesting but sloppily written
This is a good attempt at an algorithm design manual, but from my perspective (as a professor of mathematics working in combinatorics and combinatorial optimization) the writing is simply not precise enough. I'm not saying the book is too "chatty" or "informal". A technical book can be either or both while imparting its content in a clear and unambiguous fashion. I compliment the author on the idea for this book, which I think fills a gap and is at about the right level. However, if a second edition is printed (and I hope it will be), the author should read every (technical) sentence of his text and ask the question "Am I saying exactly what I mean to say?". Unfortunately, despite knowing more often than not what the author _intends_ to say, often I have trouble convincing myself that this is what is actually said.

That having been said: this is a good reference and provides a good "meta-overview" of the basics of algorithms. The Catalog of Algorithmic Problems is a particularly useful tool for quickly locating problems that might be similar to one under consideration and determining (as of 5 years ago, anyway) the approximate state of the art.

Reviews (1)

Editorial Review

This book's introduction features a humorous story of a man with a line of people behind him, who explains to his boss, "I can't find an efficient algorithm, but neither can all these famous people." This man illustrates an important quality of a class of problems, namely, the NP-complete problems: if you can prove that a problem is in this class, then it has no known polynomial-time solution that is guaranteed to work in general. This quality implies that the problem is difficult to deal with in practice.

Features

• Unabridged

Reviews (8)

A masterpiece
This book is a classic. Besides, it's a bargain. I read it more than 10 years ago as an undergrad and, even then, could grasp all the concepts. So, it's really accessible.

It is the Globally Optimal Solution!
I can't say more than what already been said by other reviewers. To recap,

F = {all excellent combinatorial optimization books}
cost(this_book) <= cost(y) for all y in F.

;)

Inexpensive, excellently written, and quite interesting!
I had this book on my shelf for two years before taking a serious look at it, and only wish I had read it much earlier in life. Christos Papadimitriou has written quite a gem! On one hand this book serves as a good introduction to combinatorial optimization algorithms, in that it provides a flawless introduction to the simplex algorithm, linear and integer programming, and search techniques such as Branch-and-Bound and dynamic programming. On another, it serves as a good reference for many graph-theoretic algorithms. But most importantly Papadimitriou and Steiglitz seem to be on a quest to understand why some problems, such as Minimum Path or Matching, have efficient solutions, while others, such as Traveling Salesman, do not. And in doing so they end up providing the reader with a big picture behind algorithms and complexity, and the connection between optimization problems and complexity.

Reviews (2)

Another classic book
I read Introduction to Algorithms (1990, MIT) and this book is recommended for "further studies" on networks flow and Matroids. However, can anyone tell me is Eugene S Lawler the same person as Eugene L Lawler (a UCB professor)? Or are they two persons?

A good overview of combinatorial optimisation
Combinatorial Optimisation : Networks and Matroids by Eugene Lawler examines shortest paths, network flows, bipartite matching, non bipartite matching. More importantly there is an excellent introduction to matroid theory including matroids and the greedy algorithm, matroid intersections and matroid parity problems, some of these Lawler's own results.

However there is not much on NP completeness, since this book was published in 1976. For a more to date version of events in combinatorial optimisation one might want to look at Papadimitriou and Steglitz's book on combinatorial optimisation (quite old too, considering this was published in 1982), Ahuja, Magnanti and Orlin's book on Network algorithms, Hochbaum's book on approximation algorithms and Cook, Cunnigham,Pulleyblank and Schrijver's book on combinatorial optimisation (listed in the order they were published).

Reviews (3)

Confirm: Excellent Resource
Just bought the book and it has been useful already. If you are serious enough about computer science to want to know what is in the professional literature this is full of wonderful summaries.

Excellent Computer Science Resource
This book is an incredible resource for all people interested in software engineering and AI.I've used numerous ideas in from this book in current research projects, and I'm sure that my copy will become well worn withtime! -Ben Peterson, Computer Science student

Reviews (2)

Just what you expect from a handbook
This is probably the best or may be the only handbook on theeretical computer science.If one wants to get a good idea on any topic in this field, just open the book and you do not have to run around looking into different books.

On the other hand, this is a huge field, and no handbook can put all of this in one book.I found that many topics that I was looking in were not in good detail at all.For instance I was looking in Fortune's voronoi construction, the terse way of explaining things does not give the reader enough insight.

Isbn: 0262720159
Sales Rank: 988606
Subjects:  1. Computer Bks - General Information    2. Computer Books: General    3. Computer Science    4. Computers    5. Computers / Computer Science   

Editorial Review

This magnificent tour de force presents a comprehensive overview of a wide variety of algorithms and the analysis of them. Now in its third edition, The Art of Computer Programming, Volume I: Fundamental Algorithms contains substantial revisions by the author and includes numerous new exercises.

Although this book was conceived several decades ago, it is still a timeless classic. One of the book's greatest strengths is the wonderful collection of problems that accompany each chapter. The author has chosen problems carefully and indexed them according to difficulty. Solving a substantial number of these problems will help you gain a solid understanding of the issues surrounding the given topic. Furthermore, the exercises feature a variety of classic problems.

Fundamental Algorithms begins with mathematical preliminaries. The first section offers a good grounding in a variety of useful mathematical tools: proof techniques, combinatorics, and elementary number theory. Knuth then details the MIX processor, a virtual machine architecture that serves as the programming target for subsequent discussions. This wonderful section comprehensively covers the principles of simple machine architecture, beginning with a register-level discussion of the instruction set. A later discussion of a simulator for this machine includes an excellent description of the principles underlying the implementation of subroutines and co-routines. Implementing such a simulator is an excellent introduction to computer design.

In the second section, Knuth covers data structures--stacks, queues, lists, arrays, and trees--and presents implementations (in MIX assembly) along with techniques for manipulating these structures. Knuth follows many of the algorithms with careful time and space analysis. In the section on tree structures, the discussion includes a series of interesting problems concerning the combinatorics of trees (counting distinct trees of a particular form, for example) and some particularly interesting applications. Also featured is a discussion of Huffmann encoding and, in the section on lists, an excellent introduction to garbage collection algorithms and the difficult challenges associated with such a task. The book closes with a discussion of dynamic allocation algorithms.

Editorial Review

Written for readers with at least some Perl programming experience, Mastering Algorithms in Perl delivers a solid library of algorithms written in Perl for business and mathematical computing. From data structures to cryptography and more advanced mathematical algorithms, this book provides a worthwhile guide to extending Perl's coding capabilities.

The best thing about Mastering Algorithms in Perl is the scope at which it covers the universe of algorithms while refraining from getting bogged down in academic detail. Besides basic data structures--a lynchpin of books on algorithms--the authors provide dozens and dozens of algorithms for sorting, searching, and doing mathematical computations of all kinds. While they discuss "Big-O" notation and assume a general familiarity with math, they don't overwhelm the reader. (You can even borrow the code without needing a math degree to understand it.) The focus is on efficient, reusable Perl subroutines written and compiled by three Perl experts.

Standout chapters include extending Perl's already powerful string processing abilities, game programming, and cryptography. Generally, the authors achieve a good mix of more advanced (and less well-known) algorithms, along with the basics. Chances are you won't need to use all the dozen or so sorting algorithms presented here, but the authors include them all, just in case. As a reference and tutorial, readers can pick and choose what they need for real-world Perl development.

There hasn't been a book dedicated exclusively to Perl algorithms prior to the publication of this one. In all, Mastering Algorithms in Perl fills a useful niche by compiling a powerful library of Perl algorithms that will be useful for anyone who works with this programming language, whether in business or academic computing. --Richard Dragan

Reviews (12)

Repetitive and disappointing - Failed to deliver
"Practical Algorithms for Programmers" fails to deliver what is mentioned by authors as "The purpose of this book is providing a practical compendium of algorithms for use in applications" and "Most algorithm books today are either academic text books or rehash of the same tired set of algorithms". This book goes over that tired set of algorithms over again (B-Trees, bubble sort, shell sort etc) and is filled with long code listings with little comments and faux paus code practices (if column > 61).

Almost all of the algorithms discussed in the book have already been very well described in various books of the same genre, notably "Introduction to Algorithms, Second Edition by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein" which has better explanation and examples. I see that "Practical Algorithms for Programmers" has good description of algorithmic efficiency, B-trees, AVL trees, phonetic comparisons, soundEx and metaphone along with excercises, but as an overall study for a CS grad and/or skilled developer, it's repetitive and meaningless. If authors' idea was to provide a cookbook for algorithm implementation, due to lack of component oriented thinking, this book lack this prospect as well. As mentioned, it's not a text book and therefore I don't see a reason of having about hundred pages of printed source code in the book? If the intended audience are software developers, why not highlight the important code segments and let the rest available via CD/FTP and use remaining pages for practical industry implementation discussions like the title suggests.

As mentioned by other reviewers, this book might be a short & quick review or refresher course but I believe that it doesn't add any valuable reference to existing set of books available in this niche. Especially in the current development era when underlying software architectures and programming languages provides the built-in complex datatypes and memory management, algorithm world now belongs to exploration of binomial option pricing, naïve Bayes filtering and normal distribution approximation style studies.

The most complete collection of implemented algorithms.
This book has the singular benefit of doing complete implementations of algorithms, not just code snippets norpseudo-code. (The code is in C.)

The book also covers lots of the algorithms other books forget: searching for multiple strings at once, searching for regular expressions, complete calendar routines, arbitrary-precision arithmetic, phonetic searches, most data-compression algorithms (including the one used by Microsoft in its product distribution), etc. Plus all the traditional sorts and data structures that appear in other books. As to data structures, it's one of the few books that implements an ISAM routine using your choice of red-black or B-trees.

(By the way, although there is a coupon at the back of the book for a source code disk, the code is made available here for free download: http://www.pacificdataworks.com/bookdownload.html The authors update the codebase intermittently.)

A good book, which has no direct counterpart.

Reviews (1)

Reviews (2)

A great sequel to Garey and Johnson
This book is a great sequel to Garey and Johnson. The appendix of this book gives a list of all NP optimisation problems together with their current approximability (or inapproximability results) in a Garey Johnson fashion.

Developing approximation algorithms for NP hard problems is now a very active field in Mathematical Programming and Theoretical Computer Science. There have been a number of exciting developments like semidefinite programming , the Goemans Williamson algorithm for max cut et al.

On the other hand, from a theoretical computer science point of view, we now have a proof that many of these problems cannot have polynomial approximation algorithms unless P=NP.

This book provides an excellent introduction to both areas. A worthy supplement to Garey and Johnson,Papadimitriou's books on combinatorial optimisation and computational complexity, Hochbaum's book on approximation algorithms, Alon and Spencer's book on the probabilistic method and finally Motwani and Raghavan's book on randomised algorithms.

A great sequel to Garey and Johnson
This book is a great sequel to Garey and Johnson. The appendix of this book gives a list of all NP optimisation problems together with their current approximability (or inapproximability results) in a Garey Johnson fashion.

Developing approximation algorithms for NP hard problems is now a very active field in Mathematical Programming and Theoretical Computer Science. There have been a number of exciting developments like semidefinite programming , the Goemans Williamson algorithm for max cut et al.

On the other hand, from a theoretical computer science point of view, we now have a proof that many of these problems cannot have polynomial approximation algorithms unless P=NP.

Reviews (1)

Reviews (2)

Deja vu?
In 1958, Cornell psychologist Frank Rosenblatt proposed the 'perceptron', one of the first neural networks to become widely known. A retina sensory layer projected to an association layer made up of threshold logic units which in turn connected to the third layer, the response layer. If two groups of patterns are linearly separable then the perceptron network works well in learning to classify them in separate classes. In this reference, Minsky and Papert show that assuming a diameter-limited sensory retina, a perceptron network could not always compute connectedness, ie, determining if a line figure is one connected line or two separate lines. Extrapolating the conclusions of this reference to other sorts of neural networks was a big setback to the field at the time of this reference. However, it was subsequently shown that having an additional 'hidden' layer in the neural network overcame many of the limitations. This reference figures so prominently in the field of neural networks, and is often referred to in modern works. But of even greater significance, the history of the perceptron demonstrates the complexity of analyzing neural networks. Before this reference, artificial neural networks were considered terrific, after this reference limited, and then in the 1980s terrific again. But at the time of this writing, it is realized that despite physiological plausibility, artificial neural networks do not scale well to large or complex problems that brains can easily handle, and artificial neural networks as we know them may actually be not so terrific.

Seminal AI book
This is a seminal work in the field of Artificial Intelligence.Following an initial period of enthusiasm, the field encountered a period of frustration and disrepute.Minksy and Papert's 1969 book summed up this general feeling of frustration among researchers by demonstrating therepresentational limitations of Perceptrons (used in neural networks). Their arguments were very influential in the field and accepted by mostwithout further analysis.

Reviews (1)

A great graduate book to complement the textbooks!
If you've read computational geometry textbooks (like the Preparata-Shamos, or the recent and great "Dutch" book by de Berg et al.), and liked the material, here's your chance to delve into the moremathematical and analytical aspect of the topic. This book is not for anundergraduate class, but its thorough yet readable treatment ofarrangements or Voronoi diagrams will bring you up to speed for readingtechnical articles or more elaborate references (like Edelsbrunner'smonograph). The scope is wider than Edelsbrunner's, even though fewer orless technical results appear. For instance, there is an analysis of thecomplexity of the union of triangles in space (based on recent researchresults).

The book takes the viewpoint of randomized algorithms, whichare very simple to describe and implement, and provides general tools fortheir analysis (on-line or off-line dynamic algorithm). It is based in fiveparts around geometric structures (polytopes, arrangements, Voronoidiagrams) with two introductory parts (algorithms, randomized analysis --general theorems).

Reviews (11)

Good Introduction but look elsewhere for detailed reference
Pro:
(1) Each chapter begins with a practical example. For example, the chapter computing intersections of lines starts with a discussion of a map-making application that goes into enough detail to see how the algorithms they present would be useful. This is a considerable step up from the common practice in algorithms literature of motivation by way of vaguely mentioning some related field (i.e. "These string matching algorithms are useful in computational biology"). This book does a much better job of motivating the material it presents, but if you're primarily interested in the abstract problem, these sections can be skipped.

(2) Each chapter is relatively self-contained. Feel free to skip ahead to subjects that interest you.

(3) Surprisingly readable. Unlike most technical material, one can read an entire chapter in a single sitting without missing much. Generally, each chapter will develop a single algorithm for a single kind of problem.

(4) It's very up to date. This second edition is less than two years old, it includes some new results in the field.

Con:
(1) Algorithms are only given in pseudocode. The emphasis is on describing algorithms and data structures clearly and completely. If you're looking for a "cookbook" with code to copy and paste into an application, perhaps O'Rourke's "Computational Geometry in C" would be a better choice.

(2) There are many important advanced results that are not discussed in the main text. An obvious example is the first chapter, which describes a well-known convex hull algorithm that takes O(n log n) time but algorithms that are faster for most inputs are mentioned only in the "Notes and Comments" at the end of the chapter. Someone interested in lots of gory details would be well-served to combine this book with Boissonnat and Yvinec's more detailed and mathematical "Algorithmic Geometry".

Extremely well written
Algorithm books are often quite hard to understand, but this is not the case with this book. The information is very compact so it is a slow read but due to the high quality of the text this is only an advantage. You are never left wondering what the authors might have meant with a certain statement.

The book focuses solely on theory, so it presents no real source code (only pseudo-code) which I think is good thing since that would otherwise have polluted the clarity of the explanations.

Many of the topics it covers has been a help to me as a programmer. Can be recommended for anyone interested in computation geometry - but it requires some computer science maturity so I don't recommend it unless you have a bachelor's degree in C.S. or something similar.

Jacob Marner, M.Sc.

Interesting read, excellent theory, no code
This book serves as a survey of computational geometry algorithms.The explanations are very readable.The authors have taken special care to prove algorithm correctness and time complexity bounds.

Although I have yet to actually implement one of the algorithms in the book directly, I was exposed to a number of general techniques which I have used, such as randomized techniques to eliminate pathological worst-case performance problems, and various space partitioning techniques.

The algorithms are all presented in pseudocode, unfortunately, which is the reason for only 4 out of 5 stars.Also, some important details are omitted which make a few of their algorithms practically useless (although they are interesting theoritically).For example, there is an algorithm for pathfinding and collision avoidance for a translating (but not ROTATING!) robot.

Reviews (15)

Lots of data
This book presents more than 25 case studies in framework development. The approaches are varied. Some are very nuts&bolts, others operate at a much more philosophical level. Some of the more interesting topics involve component-based systems, hardware design, and several applications of design patterns.

The whole thing, including 50 pages of end matter, weighs in over 700 pages. The writing is dense and packed with data. It's a real goldmine of information.

The problem is that a goldmine, these days, typically yields one gram of gold per tonne of ore. Extracting this book's information, the real usable content, from its data is about like extracting that gram. Maybe that's appropriate, though. Framework development is not as well understood as lots of other kinds of software, and the first step in any science is to collect specimens. I've found my nuggets in this book, as other readers have found theirs, but different people find different nuggets according to their needs and interests.

If you have an active interest in framework development, and if you have time and energy to distill over 650 pages of raw data on your own, this book may be for you. It really is not for the beginner or the casual reader, though.

Shame on you!
Ok. Almost all the reviews have arab names (that link them to Mohamed E. Fayad) or are hidden behind expressions like "A reader". Almost all the reviews says that the book is excellent, perfect and they don't find any defects.

It's easy to get a good rating for your book if you are without scruples and don't even have the smallest ethics.

Reviews (3)

excellent reference
I find this book to be a valuable reference for teaching my own college C++ programming class.Many object-oriented topics which are largely ignored by other authors are nicely covered in this text, making it a super first reference for any future C++ programmer.The real world treatment of object-oriented design and analysis is appreciated, from the educator's perspective.

Good as introduction
I'm half way through this book. Based on what I have read, this book is good for someone new to OO design and possibly not familiar with C++ either. But if you have read Bjarne Stroustrup's "The C++ Programming Language" and the Gamma et al's "Design Patterns" book, you won't gain much new from it. Moreover, it is sometimes not very acurate. For example, when talking about the difference between malloc() and new, the authors failed to point out that new calls object constructors while malloc() does not, which is perhaps the most important difference. Overall, this book is good for beginners for getting a general view of programming methodologies, OO and C++.But if you think you have known the basics, this book is not for you.

Reviews (9)

Not worth your time if you are a younger programmer
This book was recommended to improve my algorithm skills. However, any younger person (< 30) should not buy this book. It is not an easy read. Might be because it was written in the 70's, but the author constanly uses large and complitcated words for no reason. Also, anyone who's code base is C/C++ will find it hard and annoying to read the books ancient language. I found myself reading each sentense 2 or 3 times just to understand the point. This book might only be $5, but its not worth the savings. Get a book that was printed in the same decade.

A must have book
This book should be on the shelves of every computer science enthusiast and it does not hurt to read it once in a while to reestablish the foundations.