Review of "The boost graph library: user guide and reference manual by Jeremy G. Siek, Lie-Quan Lee, and Andrew Lumsdaine." Addison-Wesley 2002.

deeper into graph theory, thereby generating algorithms that are more challenging to the reader. Topics such as Depth-First Search, Hamiltonian Paths, Kruskal's Algorithm and Euclidean Networks are explored in detail. I have studied graph theory and therefore I was able to appreciate the examples and algorithms given in the text. However, I believe the author gives enough of an introduction in the beginning and explanations throughout the text so that a reader without any prior exposure to graph theory can still gain valuable experience in developing algorithms to solve complex problems. This book would be an excellent tool for a graph theory course (assuming the student is familiar with programming) or perhaps an advanced programming course dealing with algorithms or object oriented design methods. I found that the explanations of theorems and proofs in this text were excellent and helped me to further my knowledge and appreciation of graph theory. The object-oriented approach to implementing algorithms in C++ broadened my programming experience and helped to keep my interest in the topic. Occasionally the author assumes that the reader either has read the first volume, or has the text available for review. The first two volumes can be purchased as a bundle, and I suggest the reader consider obtaining both texts. However the programs from both volumes are available for download on the author's website, so it is not necessary to have both books if the reader is comfortable with programming topics such as queues. Overall, I enjoyed Algorithms in C++, and I plan to purchase the first and third volumes to compliment this text. I am certain that I will refer to all three in the future when I am in need of guidance, or perhaps even diversion. Pattern matching in strings is a basic problem in many areas of computer science, but particularly in applications that deal with text searching and genetic sequences. Information retrieval and computational biology are generating dramatic increases both in the size of texts to search and in the sophistication of the searches. The authors are two academics with bioinformatics industry experience. They use this book to make the case that information on pattern matching algorithms is not well understood except by experts in the area, and that for non-experts useful, practical implementations are nearly impossible to construct from available literature. Further , they claim that the only way to truly determine the fastest algorithm …