Algorithms Books

This is an excellent book on simulation of n-body systems. The
author gives a very good description of several techniques and
algorithms together with some theoretical background. This is
the book to have on your desk if you don't want to have a pile
of pappers and other books.

I am using the book now for class work. I find it easy to read and comprehensible.

This book is an edited collection of chapters written by knowledgeable researchers in the field of evolutionary computation. The collection is very well organized, and it covers in adequate depth many topics of evolutionary algorithms. This is an expensive book, but it is worth the price if you are really interested in a good reference book of this area. I recommend it highly.

Now and then a book comes along to open your eyes. I've been developing embedded computing solutions for 20 years and thought I knew a thing or two about this important computing discipline. Wayne Wolf's book put structure and analysis around what I knew (and thought was just improvised) and taught me a lot more about industrial-strength solutions. Wish to use the correct model of computation for your real-time application? Start with this book and follow the leads. Want to minimize memory? Practical algorithms are here. Need to balance throughput and latency? Go no further. Have to develop software and hardware concurrently? Get the right picture in the last chapter. Above all, the book gives excellent coverage of critical issues for migrating uniprocessor code to multiprocessors.

I only wish that the solution manual could be available to those of us who are not instructors but work in the industry, and I feel the Index ought to be expanded. Otherwise a perfect book for embedded computing professionals and students.

A very nice book that focuses on algorithms in applied mathematics through the work of their creators. The readers might need a bit of a background (say a year or two of university level math) but not more because the presentation and symbolism are very clear.

The style is simple, most sections start with an intro, followed by a short extract written by an original author (say Newton or Gauss), to finish with explanation or highlights refering to the text. This is what makes the book so good, throughout the book is a great feeling of discovery in its context. When you read a section on Gauss it is right after the section on Fermat and the flow of ideas is really well presented.

So if you like mathematics and want to get a nice overview of algorithms in applied mathematics this is the book to buy.

I stumbled over this (lovely) book a little by accident. As I kept reading, my enthusiasm for the book gradually increased. While the book is addressed perhaps more to students in computation and in CS, it is very attractive also as a text to be used in mainstream mathematics, and in probability theory.

It begins with a new look at the classical Kolmogorov construction of measures on infinite product spaces, and asks for explicit ways of labeling them with a class of certain concrete numerical functions. Then it moves onto noiseless coding theory (from communications science), but it stays rooted firmly in classical ideas from Shannon-Kolmogorov communication and information theory.

It is indeed pleasing to see that God still plays dice, not only in quantum theory, but also in such classical areas of math as in number theory.
From the foreword: "...putting Shannon's information theory and Turing's computability theory into a cocktail shaker, and shaking vigorously..."

The book is a second edition 2002, with a number of attractive additions to the first edition from 1994. It will likely work equally well in a course, as for self-study.
The main portion in the book focuses on classical and modern topics in computability, and its connections to randomness; covering concrete halting problems, chaos, cellular automata, algorithms, and their complexity.
Palle Jorgensen, October 2004.

This book is about the philosophy of mathematics and computation. It uses mathematics to analyse it self. I one need to point the most basic tree of research, with no doubt this must be it.

This book is mandatory reading for anyone interested in the fascinating and complex world of complexity theory. Circuits represent an interesting approach to complexity since they allow the computer scientist to gain a better mathematical foothold when attempting to investigate the intrinsic complexity of a problem. The book seems concise, well-written, very accessible to advanced students, and covers most of the important circuit complexity results of the last 20 years. A good supplement to this book, however, is Ingo Wegener's famous "blue book" (which is now downloadable from his website) which provides a more historical and classical treatment of the subject. If P not = NP is proved within my lifetime, I'm estimating that the proof will involve an analysis of circuits. Good luck!

Kearns is an impressive researcher, precise and succinct. The material on this book follows a tradition of careful proofs of fundamental issues in learning. I wouldn't think this is material of practical use; for that kind of material I'd recommend the new edition of Duda. Rather, Kearns is one of a team of researchers pushing the frontier of proving what is learnable and what is not, why some representations are good for learning and which are not, the dimensionality of the target problem (related to overfitting) working with prinpled definitions of what it is meant to learn borrowed from computational complexity theory.

This book used to be used for the introductory computing course at Georgia Tech. It provides an excellent starting point for those who wish to enter into the field of computing. Be forewarned, however, that this book will not teach you any programming languages. It will, however, teach you the concepts behind programming in general.