These 30 short chapters each deal with a particular programming problem drawn from sources as diverse as games and puzzles, intriguing combinatorial tasks, and more familiar areas such as data compression and string matching. Each pearl starts with the statement of the problem expressed using the functional programming language Haskell, a powerful yet succinct language for capturing algorithmic ideas clearly and simply. The novel aspect of the book is that each solution is calculated from an initial formulation of the problem in Haskell by appealing to the laws of functional programming. Pearls of Functional Algorithm Design will appeal to the aspiring functional programmer, students and teachers interested in the principles of algorithm design, and anyone seeking to master the techniques of reasoning about programs in an equational style.

    It was developed as a series of lecture notes for an undergraduate course I taught at GMU. The chapters are designed to be printable separately if necessary. As it's lecture notes, the topics are short and light on examples and theory. It's best when complementing other texts. With time, I might remedy this.What is a Metaheuristic? A common but unfortunate name for any stochastic optimization algorithm intended to be the last resort before giving up and using random or brute-force search. Such algorithms are used for problems where you don't know how to find a good solution, but if shown a candidate solution, you can give it a grade. The algorithmic family includes genetic algorithms, hill-climbing, simulated annealing, ant colony optimization, particle swarm optimization, and so on.

    AFI consists of 174 solved algorithm design problems. It covers core material, such as searching and sorting; general design principles, such as graph modeling and dynamic programming; advanced topics, such as strings, parallelism and intractability. It also covers system design, problem solving, and interviewing techniques. AFI's authors are practicing algorithmists, with extensive academic and industrial experience. They have collectively published over 100 articles on applied algorithms, applied their skills at Google, Microsoft, IBM, Qualcomm, and a number of smaller software startups, and conducted many job interviews for various computer science jobs.

    Social networks are organized as graphs, and the data on social networks takes on the form of massive streams, which are mined for a variety of purposes.Social Network Data Analytics covers an important niche in the social network analytics field. This edited volume, contributed by prominent researchers in this field, presents a wide selection of topics on social network data mining such as Structural Properties of Social Networks, Algorithms for Structural Discovery of Social Networks and Content Analysis in Social Networks. This book is also unique in focussing on the data analytical aspects of social networks in the internet scenario, rather than the traditional sociology-driven emphasis prevalent in the existing books, which do not focus on the unique data-intensive characteristics of online social networks. Emphasis is placed on simplifying the content so that students and practitioners benefit from this book.This book targets advanced level students and researchers concentrating on computer science as a secondary text or reference book. Data mining, database, information security, electronic commerce and machine learning professionals will find this book a valuable asset, as well as primary associations such as ACM, IEEE and Management Science.

    The material in this book is based on courses taught by the author, and key points are reinforced in sample problems and an accompanying instructor s manual. Graduate students and researchers in mathematics, cryptography, and security will benefit from this overview of hashing and the complicated mathematics that it requires.