Riassunto
Foundations of Algorithms, Fifth Edition offers a well-balanced presentation of algorithm design, complexity analysis of algorithms, and computational complexity. Ideal for any computer science students with a background in college algebra and discrete structures, the text presents mathematical concepts using standard English and simple notation to maximize accessibility and user-friendliness. Concrete examples, appendices reviewing essential mathematical concepts, and a student-focused approach reinforce theoretical explanations and promote learning and retention. C++ and Java pseudocode help students better understand complex algorithms. A chapter on numerical algorithms includes a review of basic number theory, Euclid's Algorithm for finding the greatest common divisor, a review of modular arithmetic, an algorithm for solving modular linear equations, an algorithm for computing modular powers, and the new polynomial-time algorithm for determining whether a number is prime.
The revised and updated Fifth Edition features an all-new chapter on genetic algorithms and genetic programming, including approximate solutions to the traveling salesperson problem, an algorithm for an artificial ant that navigates along a trail of food, and an application to financial trading. With fully updated exercises and examples throughout and improved instructor resources including complete solutions, an Instructor’s Manual and PowerPoint lecture outlines, Foundations of Algorithms is an essential text for undergraduate and graduate courses in the design and analysis of algorithms.
Key features include:
•The only text of its kind with a chapter on genetic algorithms
•Use of C++ and Java pseudocode to help students better understand complex algorithms
•No calculus background required
•Numerous clear and student-friendly examples throughout the text
•Fully updated exercises and examples throughout
•Improved instructor resources, including complete solutions, an Instructor’s Manual, and PowerPoint lecture outlines
Informazioni sull?autore
ichard Neapolitan, PhD-Northwestern University, Illinois Richard Neapolitan is a professor in the Division of Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine. His research interests include probability and statistics, artificial intelligence, cognitive science, and applications of probabilistic modeling to fields such as medicine, biology, and finance. Dr. Neapolitan has given talks and conducted seminars throughout the world, including Australia and Hungary. His online tutorial concerning causal learning has been viewed over 10,000 times and has a 5-star rating (see http://videolectures.net/kdd/). Dr. Neapolitan is a prolific author and has published in the most prestigious widely used broad area of reasoning under uncertainty. He has written six books, including the seminal 1989 Bayesian network text, Probabilistic Reasoning in Expert Systems; this textbook, Foundations of Algorithms (1996, 1998, 2003, 2011, 2013), which has been translated into several languages and is one of the most widely-used algorithms texts worldwide; Learning Bayesian Networks (2004); Probabilistic Methods for Financial and Marketing Informatics (2007); Probabilistic Methods for Bioinformatics (2009); and Contemporary Artificial Intelligence (2012). His approach to textbook writing is innovative; his books have the reputation of making difficult concepts easy to understand while still remaining rigorous and thought-provoking.
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