The Design of Approximation Algorithms
by D. P. Williamson, D. B. Shmoys
Publisher: Cambridge University Press 2010
Number of pages: 496
This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions. The book is organized around central algorithmic techniques for designing approximation algorithms, including greedy and local search algorithms, dynamic programming, linear and semidefinite programming, and randomization.
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by Herbert Edelsbrunner - Duke University
The main topics to be covered in this course are: Design Techniques; Searching; Prioritizing; Graph Algorithms; Topological Algorithms; Geometric Algorithms; NP-completeness. The emphasis will be on algorithm design and on algorithm analysis.
by Anil K. Jain, Richard C. Dubes - Prentice Hall
The book is useful for scientists who gather data and seek tools for analyzing and interpreting data. It will be a reference for scientists in a variety of disciplines and can serve as a textbook for a graduate course in exploratory data analysis.
by Chris Okasaki - Carnegie Mellon University
This book describes data structures from the point of view of functional languages. The author includes both classical data structures, such as red-black trees, and a host of new data structures developed exclusively for functional languages.
by Andrew Tridgell - samba.org
This thesis presents efficient algorithms for parallel sorting and remote data update. The sorting algorithms approach the problem by concentrating first on efficient but incorrect algorithms followed by a cleanup phase that completes the sort.