Algorithms Books

The book content is quite fascinating; the only real readability difficuly is some rather obscure and undefined notation.

The physical quality of the book is inexcusably bad. The brand new book started falling apart as soon as it was opened. Springer Verlag, the publisher, refused to correct the situation.

I shan't again buy anything publishd by Springer-Verlag!

While recently published, this book is shaping to become the standard reference on the theory that surrounds prime numbers in a computational setting, drawing from all branches of number theory, as well as abstract algebra, analysis, combinatorics, statistics, complexity theory and elliptic curves. Surely a multidisciplinary treatise if there ever was one.

The authors' writing style, while not conversational, never gets in the way, and allows reading at many levels (from light reading to deep research). Theorems are proved only when it makes sense to do so, i.e. when the proof adds insight into the matter. The exercises are interesting and challenging, and closing each chapter are avenues of further research, referencing open problems in the literature and the authors' own opinion on interesting subjects for research.

The first chapter is an overview of theoretical and computational developments, with anything from Euclid's proof of the infinitude of primes, Riemann's study of the zeta function, down to the latest huge computation of the twin prime constant and zeros of the zeta function in the critical line. Some famous open problems are displayed as well.

The necessary number theory background is covered on Chapter 2, though the interested reader should seek a more complete treatise on the subject.

Trial division, sieving and pseudo-primality tests are fully covered in Chapter 3. There is really nothing to complain about this chapter of the book.

Chapter 4 concerns proving the primality of integers. Many results are presented from the classical (meaning not involving elliptic curves) primality tests, and again there is nothing to complain.

Many people, such as myself, are drawn to the book for the integer factoring algorithms, and they're not going to be disappointed. Unfortunately, modern factoing algorithms deserve a book on its own, and it's impossible to cover all the ground in the space alloted to them in this book. The authors do a pretty good job of introducing them, even if the explanation is unclear and a bit shallow at times, and they always reference other works on the field for further information they were unable to cover.

Chapter 7, ``Elliptic Curve Arithmetic,'' is a great starting point for elliptic curve studies, with a no-nonsense introduction to the subject that is certainly enough for the algorithms that follow. These include Lenstra's Elliptic Curve Method of factorization; Shanks-Mestre's, Schoof's and Atkin-Morain's algorithms for assessing curve order; and Goldwasser-Kilian's and Atkin-Morain's primality proving algorithms.

Almost as valuable as the rest of the book itself (at least for implementers) is the ninth and last chapter, ``Fast algorithms for large-integer arithmetic.'' Many of these can be carried over without effort to floating point, so the scope of the material is even broader than the authors claim. Having read parts of Knuth's ``The Art of Computer Programming: Seminumerical Algorithms,'' I can attest to the superb exposition of Crandall and Pomerance being a breath of fresh air in this field. This book belongs on the shelf of every programmer implementing multiprecision arithmetic for this chapter alone.

This book has all the recent (2001) developments in factoring algorithms and related number theory. It has chapters on algorithms for large numbers. While graduate-level, much of it should be accessible by an undergraduate. It has exercises and research questions after each chapter.

To find all the info in it, you would have to scour a research library for all the papers that have been published on factoring and primality testing -- they are scattered thru many math journals. It also covers things like quantum computing and cryptography.

It's a good reference - no need to read the whole thing. It would also make an excellent graduate-level textbook.

This is an advanced treatment of prime numbers. But it is not all abstract number theory. The recurrent theme is how to compute these and how to use primes in other computationally intensive tasks.

The book summarises centuries of effort. Notably with Goldbach's Conjecture about every even number>2 being the sum of two primes. But intriguing issues like the density of primes along the number line are gone into. Along with the Mersenne primes and prime producing formulae.

An entire chapter discusses cryptography and related matters. Primes are at the heart of PKI and its RSA implementation. There is even a section briefly covering quantum computing and a quantum Turing Machine. Rather sparse detail because, well, the experimental results are still very new. Only baby steps have been forthcoming. The phase coherence difficulties are formidable. But it is a potentially vast area of future work.

My background is from 20 years of experience in high performance computing conducted in the academic, national lab, and comercial realms. I can say that this is a very enjoyable and entertaining book.

There once was a time when a programmer could look at several algorithms, and decide the ones they want to use based on an analysis of the operations that were performed, those simple times are over and have been over for many years. Now, detailed attention also has to be paid to the overall system architecture, particularily the cache locality of the chosen approach. This book introduces a mental tool set to determine how to best make these tradeoffs for your application and system. The book does a good job in hammering home the point that O(n) analysis by itself no longer cuts it. You have to know how big your cache lines are and make sure you use them. If I had my way, every new hire out of college would be forced to read this book before they ever allowed to utter the words "Stassen algorithm".

I didn't give it a five because as another reviewer pointed out it is a bit rocky in parts, and the underlying analytic processes which the authors were following have to be tweaked out by the reader. None the less, procuring and reading this book was time and money well spent.

I don't wish to offend the authors of this book, who I am sure are experts in this field. However, I found this book to be unnecessarily difficult to read.

The the book presents inherently complex material, which testifies to the proficiency of the authors. However, it was often not clear what point the authors were trying to make, especially regarding graphs which lacked clear explanations. Often I had to to re-read an entry several times before I understood what the authors were trying to express.

I am sure the book contains a wealth of valuable information (which is why I ordered it), but I personally was unwilling to invest the time and energy necessary to fathom the authors intent.

I am a systems engineer for an international telecom company.

I am a physics graduate student and taking a graduate course on programming parallel machines. This course is offered by an electrical and computer engineering professor at our university.

This course covers computer architectures (SMP, NUMA, et al.), theory on parallelism, OpenMP, MPI, Pthreads, and various research tools. I found this book by Drs. Wadleigh and Crawford very helpful for me to go through the entire semester.

This book follows three important core issues on high performance computing. Part I includes hardware overview and basic parallel programming methodologies. I found this part help me a lot to catch the backgrounds that I don't previously have. Part II deals with several issues on software techniques. Part II lists the tools, algorithms, and applications such as LAPACK, and fast Fourier transform.

I would highly recommend this book to scientists and engineers in the areas of computational science and engineering applications. I am so glad that our physics library has ordered and placed this book on the new bookshelf.

Written by sjtu from computational neutrino physics and geometric probability research group.

Wadleigh and Crawford have collected, organized, and presented a great deal of useful information for anyone who wants to obtain high-end performance on modern, high-end computer systems. I appreciate how the book explains and compares the approaches of various computer vendors in high-end systems, providing some historical context along the way. Along with explanations, the authors have included numerous, relevant examples (high-level & assembly source, tables of test results) to illustrate the key factors that contribute to application performance.

I think this book could easily be made into, or used with, a short course/overview on high performance computing.

I NEED TO KNOW WHERE BOTH MY ITEMS ARE. I HAVE ONLY RECEIVED
ONE. PLEASE EXPEDIATE AND ADVISE.

This is a great book! I have been doing industrial problem solving for nearly ten years and I can tell you that you won't find strategies and tools like this in a Six Sigma seminar.

I reviewed this book very favorably for the Journal of Quality Technology in the October 2005 issue. This review is freely available to the public.

I am not allowed to post the URL, but a quick search on ASQ Journal of Quality Technology should get you there.

Bill Woodall

This is NOT a typical book on statistical tools. It is a strategy book on how to search for cost-effective changes to reduce variation using empirical means (i.e. observation and experiment). The uniqueness of this book:
(1) Summarizes the seven ways to reduce variation so we know the goal of the data gathering and analysis
(2) Present analysis results using graphs instead of P-value.
(3) It integrates Taguchi, Shainin methods and classical statistical approach.

It is a must read for those who are in the business of reducing variation using data, in particular for the Six Sigma Black Belts and Master Black Belts. Don't forget to read the solutions to exercises and supplementary materials to each chapter on the enclosed CD-ROM.

Top-level robotics research involves many disciplines: kinematics, dynamics, control, and programming. A growing number of researchers see the key to unifying these diverse fields in robot algorithms -mathematical descriptions linking perception and motion that can operate in the real world to achieve goals. This collection features articles by top-level scientists in theoretical computer science, computational geometry, robotics, perception, artificial intelligence, and mechanical engineering. They present robotics as a field of computation applied to the real world much more than just controlling robot arms! A very technical collection valuable to both researchers and students.

Top-level robotics research involves many disciplines: kinematics, dynamics, control, and programming. A growing number of researchers see the key to unifying these diverse fields in robot algorithms -mathematical descriptions linking perception and motion that can operate in the real world to achieve goals. This collection features articles by top-level scientists in theoretical computer science, computational geometry, robotics, perception, artificial intelligence, and mechanical engineering. They present robotics as a field of computation applied to the real world much more than just controlling robot arms! A very technical collection valuable to both researchers and students.

Top-level robotics research involves many disciplines: kinematics, dynamics, control, and programming. A growing number of researchers see the key to unifying these diverse fields in robot algorithms -mathematical descriptions linking perception and motion that can operate in the real world to achieve goals. This collection features articles by top-level scientists in theoretical computer science, computational geometry, robotics, perception, artificial intelligence, and mechanical engineering. They present robotics as a field of computation applied to the real world much more than just controlling robot arms! A very technical collection valuable to both researchers and students.

Ed Moyle and Diana Kelley's CRYPTOGRAPHIC LIBRARIES FOR DEVELOPERS is a winning guide for developers seeking a tutorial on how to write and deploy applications requiring cryptography. The five common cryptography libraries are surveyed in a title which teaches which ones are best for different projects based on platform, language and regulatory requirements, offering software writers plenty of examples and explanations.

When we think of cryptography we normally think back to the codes broken during World War II, of Enigma, Magic and Purple. Today when I bought an item from e-Bay I thought nothing about putting in my credit card information because I had faith that the card number wouldn't been seen by any of the bad guys. ==We use words like 'secure transaction' and 'SSL' to tell people that sending such information is at little or no risk to them. We rarely stop to think that the reason sending this information is safe is cryptography.

At the user level, cryptography has become invisible to the ordinary operation of the web. At the serious end of cryptography, it is still an arcane world of advanced math and carefully designed and tested algorythms. This book is aimed at the application developer level, where you and I might be wanting to ensure the safe and private transmission of data, but where we don't want to get down into the heavy math.

Enter standard libraries of cryptographic routines to enable the use of very advanced techniques in standard applications code. That's the focus of this book. It investigates and describes the various libraries that are available for the safe transmission of data. These libraries make what would be an all but impossible task easily manageable.

This book is neither a security book nor is it a cryptography book. Instead it is a book for programmers who want to use available APIs to add security and cryptography to the code they are writing without having to actually learn cryptography. Thus, it is assumed that the reader is proficient in C, C++, or Java programming, depending upon which cryptography API you are planning to employ. The authors spend the first part of the book going over what I consider to be a very bare outline of cryptography techniques and development goals for cryptographic code. They spend the second half of the book talking about specific cryptographic API's and how to program cryptographic solutions using each of these API's. The API's covered are OpenSSL, CryptoAPI (CAPI), JCA/JCE (Java Cryptography API), and BSAFE. Each API has its own chapter that contains an API overview and code examples. The book's grand finale is a chapter on applications that shows how to build a multilibrary cryptographic interface in C++, and also how to perform file content protection in C++.
The authors say that their goal is to shave development time for working programmers, and in that goal they succeed. However, it is very hard to succeed at a task by rote learning, and this book on cryptographic programming without any of the pesky but interesting mathematical details just left me wanting more. If you want a couple of good books on cryptography and network security themselves might I recommend "Cryptography and Network Security (4th Edition)" by Stallings, and also "Applied Cryptography" by Schneier. Stallings' book was only recently published, is very easy to read, and covers both subjects comprehensively in an accessible style. Schneier's book is starting to age, yet is still the definitive text for programmers who are interested in cryptographic algorithms.
I notice that Amazon does not show the table of contents for this book, so I do that here:
1. Introduction
2. Security Concepts- Developer Overview
3. Cryptographic Engineering
4. Basic Techniques
5. Implementation Choices
6. Application Development Strategies
7. Developing with OpenSSL
8. Developing with CAPI
9. Developing with JCA/JCE
10. Developing with BSAFE
11. Example Applications

The book was purchased as a classroom text. The subject matter is incredibly difficult, but the authors do their best to make it comprehensible.

I found the material and explanations in this book to be generally followable. I would definitely recommend it for learning the various concepts.

The problem is, as a reference for how to implement certain algorithms, it falls completely flat. Simply because they wrote all their algorithms with the idea that arrays start at '1'. This is icomprehensible inasmuch as no programming language has arrays starting at 1, no budding programmer would think of an array as starting at 1, the authors would have had to test all these algorithms in languages with arrays starting at 0 and then spent way too long converting them since a good many of the algorithms in the book are completely broken when rewritten under the assumption that arrays start at 0 (the 'norm').

In summary, it seems like they went out their way to create extra work for both themselves and their readers. This one little thing is honestly a huge pain.

The authors take a more informal approach to describing algorithms, than other texts, by Knuth, Sedgewick and Aho. This may appeal to some of you, who might have been put off by the level of mathematical background required by the other books. Or by the rate of exposition of ideas within those books.

The presence of problems with each chapter helps your learning. And there certainly seems to be plenty of examples. To me, it seemed like they were belabouring the point, in places. But to you, it might feel like a comfortable dialog.

The usage of Java pseudocode is neither here nor there. If you are sufficiently skilled in a programming language, and it need not be Java, then you should have no problem writing out actual code to test the algorithms. In other words, don't worry if you do not know Java. Just imagine it as generic pseudocode.

The book covers most of the fundamental aspects of logic synthesis and verification algorithms commonly implemented in the popular CAD tools. However, the book is poorly written and full of typos. One may constantly wonder what is going on until he reads the material like the 2nd or 3rd times and guess his way through. Sadly, there is rarely a better book in the CAD field, so even though the book is not very well written the material presented is still worth reading especially for any serious readers working in the field of logic synthesis.

I had the oppurtunity to study this book in my graduate study. I think it is a well-written book, which has a substantial coverage of the field of logic synthesis and verification. I would highly recommend this book to advanced readers and those who are mathematically inclined. The expertise of the authors is reflected in the content of the book, and this can be fully appreciated by the readers having the qualifications mentioned above.

This book is good. It covers all the fundamentals needed to learn two level and multi level logic synthesis and verification.

It's a good stepping stone for those readers wanting to delve more into the IEEE publications area later on in their careers when implementing new algorithms for logic synthesis.

If you need to learn and apply machine vision techniques this is a good place to start.

This is a good book on algorithms in machine vision, not computer vision. It is important that you know the difference before you buy this book. Machine vision is used in manufacturing plants for detecting the parts according to your needs. On the other hand, computer vision is integrated into AI where you can create vision input for your robotics or AI system.

That's why this book is good for those who wants to create Photoshop-like effects or implement systems for part analysis. If you are looking for something related with robotics, look for elsewhere.

There are better books on Java imaging and better books on machine vision. However, where this book stands out is in its catalogue of machine vision algorithms. The book is really a vehicle for the author's machine vision application, complete with user interface, entitled "NeatVision". You can download it off the web complete with a developer's guide on how to extend the application. There's really nothing unique about the built-in transforms. If you are already familiar with computer vision- and you should be if you buy this book- you will most likely already have heard of most of them. What it does enable you to do is to chain transforms together to perform larger more complex machine vision tasks.
As for the book itself, the first chapter is a forgettable overview of machine vision, and the second chapter is an equally forgettable chapter on Java and imaging in Java. Chapters three through six comprise the meat of the book. These chapters talk about the specific algorithms for machine vision transformations divided into the categories of general machine vision techniques, morphology, texture analysis, and color analysis. Chapter seven switches gears and talks about the author's full visual application, Neatvision. Details are given on how to use and and to extend the application. The appendices are about NeatVision's graphic file formats, API specification, and visual interface components respectively. My advice to the potential buyer of this book is to visit the application's website, and if you find NeatVision very interesting you will find the book valuable. I would type the address into this review myself, but Amazon usually kicks out reviews with web addresses in them. The search engine keyword is just "NeatVision".

If there's one topic this books is not about, it's the numerical geometry of images
despite its title.

This book should have been titled "Introduction to Geometry of Curves on Surfaces."

The closest the author gets to anything resembling the study of the numerical
geometry of images is the Taylor series expansion of derivatives taught in most
undergraduate calculus courses - and two simple MATLAB program of questionable
value at the end the book.

There are a handful of simple algorithms in the chapter 7 but they only address
fast marching methods applied to two silly boundary value problems. The author
completely ignores the corresponding initial value problem.

I'm giving it 2 stars since the title of the book was completely misleading.
And after reading it, I was left wondering how would someone apply the information
presented in this book to a simple 2 dimensional image since there are absolutely
no examples of any practical value.

A very well-written, interesting and useful book covering a wide range of topics in image processing and computer vision and beyond. A good balance between theory and implementation issues that make the things work. A 100% recommendation to students and specialists in the field.

The book is an extended version of my lecture notes. Including
introduction to variational methods, differential geometry,
level sets and fast marching numerical methods, and geometric active
contours for segmentation with Matlab pseudo code, 3D face recognition,
texture mapping and more applications.

Thanks to the many who bought the book.

If Mr. Bizon's work is truly an example of what is the best that NASA can offer, than the National Aeronautics and Space Administration is in big trouble. I am rather surprised that Mr. O'Keefe debases himself with such a overexuberant review of Mr. Bizon's work. The report itself is a standard technical report written by a mostly competent digital design engineer. It is dry and unexciting, albeit somewhat informative. One can tell that it was written by an engineer rather than a professional writer.

Mr Bizon provides an example of the best NASA has to offer. This paper is extremely well-written and demonstrates that complex algorithms can be implemented efficiently (and, dare I say, even beautifully) in digital hardware. I can hardly wait to read more of Mr Bizon's mesmerizing writing.

I hope you have as much fun reading it as I did building it.