Algorithms Books

A network here mean a graph with with weighted, undirected edges. This short (113 page) book is written in a clear style, and could be used as a textbook, athough it does not include exercises. Emphasis is placed on proving the efficiency of the algorithms presented. No knowlege of graph theory or algorithms is assumed, but knowlege of basic programming and college-entry level math is required.

The first half of the book covers the data structures used in solving the network problems that are presented in the second half. These data structures including disjoint sets, heaps, and search trees. Highlights of this half of the book are Tarjan's proof of the amoritized cost of union find, and explaination of self-adjusting binary trees.

The second half of the book covers four classical network problems: minimum spanning tree, shortest paths, network flows (e.g. min-cut), and matchings.

I found the first half of the book more interesting, because more of it was new to me, and because it seemed more likely to be of practical value to me in my work. I have seen presentations of the network problems before, but not with the analysis of efficiency, and comparison of different approaches.

Robert Tarjan (the author) has written many papers on efficient graph algorithms, and appears to be a pioneer in applying proofs of efficiency to graph algorithm. He is often cited in reference to the efficient graph algorithms he has discovered.

This book was published in 1983, but does not seem to be dated.

This is a superb book. I taught a graduate level course based on it at Lehigh University in 1984 or 1985. It was awarded the prestigious annual Lanchester prize for book of the year Operations Research Society of America about that time. Robert Tarjan was awarded the ACM's Turing award, computer sciences closest equivalent to the Nobel Prize for his contibutions to the theory of algorithms. This book is an excellent introduction to his work. The algorithms in this book were state of the art when it was published, but I don't know how close they are to today's best.

Most of the optimal algorithms in the book grew out of Tarjan's pioneering work on algorithms that minimizes total complexity by allowing individual chunks of work to consume large amounts of computing resources if they build up "credits" that make subsequent steps more efficient. Until Tarjan used this approach to develop superior algorithms for a number of classical problems, the state of the art had been to limit the resources consumed by each step and bound total complexity by multipying the number of steps by the worst case resource consumption per step.

Tarjan's exposition illustrates the power of abstraction. He uses abstract data types throughout, carefully defining them in terms of their fundamental operations. This approach will be very natural for anyone familiar with object oriented programming.

There is a huge amount of information in very few pages, but it is organized very well. Often Tarjan's carefully chosen words say a lot more than is apparent to casual reader's. I spent one 75 minute period explaining his 12 line proof of one of his algorithms. Then the class demanded that I illustrate how the algorithm actually worked on a real problem, so we spent another 1.5 classes applying the algorithm to a small problem I contrived to exercise all of its boundary conditions.

Other faculty advised me that this book was much too hard for course intended for advanced undergraduate and beginning graduate students, but the students disagreed. More than one commented that the material was hard after first reading, but that after hearing my lectures and rereading their assignments, they realized that it was really pretty easy and that the book presented it well. Most would have appreciated worked out examples to observe the dynamic behavior of the algorithms. One student animated some of the algorithms and went on to write his masters thesis on algorithm animation.

A book which will show you how to create datastructure and give you a broad understanding of how to create software. I'm a computer science student at Empire State College and the book seems to cover lots of important details on how programming developts from top to bottom overview. Although the book is good, the authors seems to forget to illustrated some of the 'not very easy to understand point' but overall the main ideas are illustrated in the most understandable manner. 5 starts is not enough for this book. If you are a computer science mayor and taking Datastructures and algorithms, this is a most have book.

This book proved to be a valuable tool to propel me to the next level of understanding of computer science. I gave it four stars because the author skips out on a lot of explanative detail, I felt should of been included, but does an excellent overview/summary of ideas and concepts. I only give it four stars because of it's lack of information. This book is not for the beginner. If you're serious, and want to learn something, then read this one.

This book does live up to the expectations I had. The first three chapters are a very solid introduction to genetic algorithms. It also goes into variations that lets one overcome some of the 'stagnation' that can occur in GAs.

The rest of the book does dive into applying GAs to EM applications. They have a very good introduction of the motivation, problems encountered and how it was resolved. No code is available, but many have already written GAs in many programming languages.

Book assumes some knowledge in math (~calculus) and electrical engineering (my background is in physics, but it didn't handicap me). A must for anyone interested in EM design while integrating GAs or for anyone interested in applying GAs to their engineering problems.

This book is well worth the price I paid for it.

It is a good book for engineers looking for practical information on genetic algorithms. The book assumes familiarity with electromagnetic theory, practical problems, and current simulation techniques.

The first three introductory chapters are solid, and universally applicable. They are a beginning overview of genetic algorithms, a more serious treatment describing pitfalls and variant techniques, and finally some rules of thumb for using the techniques.

The following chapters are detailed treatments of the application of genetic algorithms to real problems. Seven chapters are devoted to various types of antennas and antenna arrays. Additional chapters cover specific devices, such as electromagnetic filters, diffraction gratings, backscattering problems and magnetostatic devices.

This book will not teach you how to write genetic algorithm codes line by line. This book will not go into extreme depths about the theoretical limits and performance of genetic algorithms (where given, these details are not derived, simply given and explained.) There are any number of computer science and programming books which do that.

This is an engineering book: it will give many implementation details on practical problems, along with the results.

The text covers quite a broad range of topics. It assumes that the reader has a fairly good grasp of statistics and mathematics (senior undergraduate level). Introductory financial material reads ok. The algorithmic approach is useful and so are the answers to select problems. However, some could find the material too tightly packed.

The term financial engineering is appearing increasingly in the title of various works. What it actually means remains nebulous, for even natural sciences and engineering remain irresolvable at their respective cores. This is evidently addressing another boon of the digital age, where equations and approximations can be done both automatically, and with extreme rapidity. It is only natural for the advantages of this to trickle into research of finance and economics, only now it is becoming a steady stream, and, with the inclusion of this work, a most sound one at that.

It is inferred that the author views this phrase, "financial engineering," as the level of control over precision of computation, and then the resulting accuracy of projected results (with an occasional forecast of unanticipated outcome). His credentials validate this as well.

The tools utilized include the complete discipline of algorithmics, and numerous branches of mathematics, along with tools assisting with and automating graphics and formatting, such as Latex and Mathematica, all channelled into this most profitable and competitive field of finance.

The approach for most sections begins with a brief discussion of motivation, typically condensed to one or a few paragraphs, followed by an equation representing an historical approach to the problem. This is followed by one or more expanded sections building algorithms, expressed in mathematics and pseudocode, as well as plots of typical results. The section then concludes with a broader discussion of how computation and finance become intertwined through this particular application. The author is extremely well versed in both. There are numerous exercises as well.

The book has the look and feel of an adept computer scientist, applying his honed skills to the financial realm. The typesetting is extremely well done, and even for sections initially unfamiliar, the reader feels confident and motivated to become fluent in time. Many of the exercises have solutions provided in an appendix.

At the time of its original publish date the book was unique in the field due to its approach and concise depth of mathematics, all available from a single resource. The author clearly exerted an extraordinary amount of time and energy to producing this work, and each section attests to this meticulous attention detail.

This work is highly-recommended as a reference, for a plethora of well-constructed algorithms in pseudocode are provided; Java examples are also provided via a website. Some considerable level of sophistication in topics typically relegated to computer science and mathematics are required, for which the intrepid reader can find additional resources. When time and motivation are sufficient, there is a wealth of mathematically sound information, providing depth of understanding and a mature foundation to build upon just what financial engineering means.

Don't let its small size (~200 pages) fool you. There's a lot of good material here, and in very usable form. In fact, that's the whole focus of the book: practical, efficient implementations.

The main topics, chapter by chapter, are simple matching of one desired word to a string, matching of multiple words, two levels of complexity in wildcards and regular expressions, and approximate matching. A number of important and historical algorithms are discussed in each chapter, in great detail. There's pseudo-code for the most important algorithms. Quite a few also have examples worked in detail. The mechanics are tedious and somewhat bulky, but anyone actually trying to implement these techniques will appreciate the examples.

What's really interesting is what's not in this book. You won't find a lot of theory, and you won't find some of the most famous algorithms in string matching. The authors make it clear that this is about practical algorithms with efficient implementations. Lots of the algorithms beloved by theoreticians are impractically complex or just plain slow. Those may be mentioned in passing or as the base for more practical algorithms, but are not welcome on these pages.

It's not an easy read, but it's not a book for people with easy problems. It discusses tradeoffs, like when one technique works well for short strings but another works better on long strings. It addresses the different needs of English-language processing and bioinformatics - just the different numbers of letters in each alphabet make a difference, in some cases.

This is a good one for anyone who takes string processing seriously. There's no cut&paste code here, but plenty for a knowledgable programmer to use. Even better, it offers references to the literature and to working code, and pointers to some books on related topics. I expect to get a lot of use out of this one.

//wiredweird

This is an excellent book for someone with a strong foundation in computer science wanting to learn about regular expression and string matching. It is not for the feint-hearted, and would not be recommended to computer science novices. Written by one of the world experts in the field, the relatively thin book is packed with theory and comments on application. The algorithms are presented in a very concise form which often requires a fair amount of thought when trying to implement. However once understood, the book makes an excellent reference for the various algorithms.

For nearly 30 years, Padberg has been a leader in computational integer programming and in combinatorial optimization theory.

In practice, Padberg has helped to design and implement "branch-and-cut" methods for finding exact optimal solutions to large traveling salesman problems, and this approach is a method of choice for finding approximately optimal solutions to tough industrial problems. The book provides the mathematical and computational background for understanding branch-and-cut; the established mathematical texts by Nemhauser and Wolsey and by Schrijver are less detailed and more condensed, and omit numerical issues. The treatment of modern simplex algorithms for linear programming---updating LU factorizations and using column- and constraint-generation and -purging---is excellent, and a large bibliography contains recent references. Besides industrial and Berlin-airlift scheduling problems, the book contains TSP examples of circuit-board wiring, U.S. state capitals, and Odysseus!

Three more highlights: The double description algorithm receives a complete description, and this is useful for combinatorial geometers. The discussion of integer-arithmetic and complexity theory is very readable, and these technical topics are slighted by interior-point books (besides Wright's quickie), despite their importance in integer programming and combinatorial optimization. The discussion of interior-point algorithms emphasizes projective geometry, a beautiful theory that has inspired so much of optimization theory---besides Karmarkar's interior-point algorithm, Dantzig's simplex algorithm, Fenchel duality, Davidon's conic algorithm for nonlinear optimization, etc.).

The book is not a comprehensive survey of linear programming,
and lacks a treatment of Nesterov's theory of self-concordant barrier-functions. Also, no treatment is given of pivoting algorithms besides Dantzig's (e.g., Terlaky's criss-cross method, Todd's oriented matroid algorithm).

This book is certainly a very good reference for theoretical topics of linear programming. It covers the Simplex method and the Ellipsoid algorithms. It also covers the geometry of linear programming (polyhedra and polytopes, etc). It certainly covers more topics than most other linear programming texts. As expected, a book writen for theoretical topics is certainly not easy to read, especially for people with no training in doing rigorous mathematical proofs. Also, not many examples or illustrations are given in this book, and this might be a problem for some readers.

I was accidentally pulled in to the world of REA. I was evaluating the redesign of our major financial application. After deeply thinking the details of the business application dynamics, I started to group them under some basic overly simplified models and entities.

From there, I started to think that there should be somebody out there who faced the same situation and solved the same set of problems with a similar approach and hopefully more elegantly.

Then, I stopped evolving my model and started searching the literature and the Internet. I came across Fowler's book and I think it was great and I liked it so much, especially modelling the account and the relaed entries. But that was about it as far as the simplicity goes. It started to get a bit more complex as I started to get more patterns.

I started to do some more searches till I got to the REA, Resources- Events-Agents and that was it. I was blown away.

The model is so simple but powerful in capturing the most fundamental concepts in the accounting and business domain.

Unfortunately, I did not find enough resources (at this time) that examines the REA and its applications in detail till I found this wonderful book.

I really thank the author for his work.

So I think, REA model will change the business information modelling arena in the same way object oriented programming changed the programming world, and like design patterns impacted the design world.

I also predict that this book will be for the business application architecture community as the GoF book to the software designers community at large.

Design patterns, as general solutions to commonly occurring problems, were introduced in the software field by the landmark book by the so-called "Gang of Four" (Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides: "Design Patterns: Elements of reusable object-oriented software," Addison-Wesley, 1994. ISBN 0201633612 ). Even though design patterns were originally targeted at the design of object-oriented software systems, their use quickly spread to the analysis phase.

Pattern catalogs for analysts have been relatively successful because they provide extremely useful information for software analysts, novices and seasoned alike. Martin Fowler and David C. Hay were the first to produce pattern catalogs from the analyst's point of view. Both Fowler's analysis patterns, whose notation predates the now ubiquitous UML standard, and Hay's data model patterns, from a more database-oriented perspective, delved into different application domains and provided us with a rich repertoire of invaluable models for the analysis phase of software development projects. More recently, David Hay has updated and complemented his original work and the OMG-sponsored Model-Driven Architecture has also led to similar catalogues using the UML notation (see Arlow and Neudstat's "Enterprise Patterns and MDA").

Even though the relatively typical dull prose of pattern catalogs is in this case exacerbated by some typos (showing the lack of proper copy editing work I hope will be fixed in future printings of this book), this book is still noteworthy for two reasons:

- Plenty of modeling diagrams (almost one per page) cover almost every situation you can find when developing business applications,

- and they do so by relying on a simple conceptual model, the REA model, which is in itself an interesting approach for the design of business applications [...]



PS: Here are the complete references to other pattern catalogs you might find of interest...

- Jim Arlow and Ila Neustadt: "Enterprise Patterns and MDA: Building better software with archetype patterns and UML," Pearson Education, 2003. ISBN 032111230X

- Martin Fowler: "Analysis Patterns: Reusable object models," Addison-Wesley, 1996. ISBN 0201895420

- David C. Hay: "Data Model Patterns: Conventions of Thought," Dorset House, 1995. ISBN 0932633293

- David C. Hay: "Data Model Patterns: A Metadata Map," Morgan Kaufmann, 2006. ISBN 0120887983

This boook is not an introductory one although it could be read by almost everyone. It cover many facets of cryptography as randomness, zero knowledge proofs and interactive proofs. It also gives a good background of this ideas.

The book can be read by everyone who is interested on cryptography, secure intrnet and alike. But needs a background on CS.
The author hass a webpage with some of the material that lead to the book. It is a good idea to read them first and them decide on the book.

The book contains an broad overview of the 3 fields in the title (probabilistic proofs stands for both Interactive Proofs, Zero-Knowledge & Probabilisticly Checkable Proofs).

While definitions are accurate (therefore technical), most proof are only given in sketches which enables the reader to understand the basic ideas behind them with out getting lost in the details.

I (as a graduate student in theoretical CS) felt it is non-technical enough as to be readable and yet improved both my knowledge of the field and its techniques, and my "maturity" regarding the concepts involved.

I am a PhD student in Operations Management and this is one of the best books in Location Management. Very clear and very precise, I did not need a class to understand the material. Its definitely a must for any body who wants a background on Location Models

This is the 2nd best book after Ahuja et.al. book (Network Flows : Theory, Algorithms, and Applications, 1993). As I said, it's the 2nd best book -so don't be surprised if you found Ahuja's book more useful. However, Daskin did good job to compile both location and network analysis in a single text book. We can see many issues in these areas in this book. I'd love to see more integration both math model and algorithm in this book though. If you don't have Ahuja's book, you may wanna grasp one (it's out-of-stock) before getting this book. However, if you are new to do research in location and network, grasp Daskin's book. You won't be disappointed with this book!

It have a whole section about graphs, what's not normal. It have an excelent and didatic way of explaining things, with lots of examples, exercises and graphics.

It's undoubtely the best book on ANN I've ever seen.

this book is for the advanced user. It covers in-depth the concepts of neural networks, Artificial intelligence, and intelligent systems. A must have for all those artificial intelligent buffs...