9781461348863-1461348862-Handbook of Randomized Computing: Volume I/II (Combinatorial Optimization, 9)

Handbook of Randomized Computing: Volume I/II (Combinatorial Optimization, 9)

ISBN-13: 9781461348863
ISBN-10: 1461348862
Edition: Softcover reprint of the original 1st ed. 2001
Author: Panos M. Pardalos, José Rolim, Sanguthevar Rajasekaran, J.H. Reif
Publication date: 2013
Publisher: Springer
Format: Paperback 1046 pages
FREE US shipping

Book details

ISBN-13: 9781461348863
ISBN-10: 1461348862
Edition: Softcover reprint of the original 1st ed. 2001
Author: Panos M. Pardalos, José Rolim, Sanguthevar Rajasekaran, J.H. Reif
Publication date: 2013
Publisher: Springer
Format: Paperback 1046 pages

Summary

Handbook of Randomized Computing: Volume I/II (Combinatorial Optimization, 9) (ISBN-13: 9781461348863 and ISBN-10: 1461348862), written by authors Panos M. Pardalos, José Rolim, Sanguthevar Rajasekaran, J.H. Reif, was published by Springer in 2013. With an overall rating of 3.6 stars, it's a notable title among other books. You can easily purchase or rent Handbook of Randomized Computing: Volume I/II (Combinatorial Optimization, 9) (Paperback) from BooksRun, along with many other new and used books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.3.

Description

The technique of randomization has been employed to solve numerous prob lems of computing both sequentially and in parallel. Examples of randomized algorithms that are asymptotically better than their deterministic counterparts in solving various fundamental problems abound. Randomized algorithms have the advantages of simplicity and better performance both in theory and often is a collection of articles written by renowned experts in practice. This book in the area of randomized parallel computing. A brief introduction to randomized algorithms In the analysis of algorithms, at least three different measures of performance can be used: the best case, the worst case, and the average case. Often, the average case run time of an algorithm is much smaller than the worst case. 2 For instance, the worst case run time of Hoare's quicksort is O(n ), whereas its average case run time is only O(nlogn). The average case analysis is conducted with an assumption on the input space. The assumption made to arrive at the O(n logn) average run time for quicksort is that each input permutation is equally likely. Clearly, any average case analysis is only as good as how valid the assumption made on the input space is. Randomized algorithms achieve superior performances without making any assumptions on the inputs by making coin flips within the algorithm. Any analysis done of randomized algorithms will be valid for all possible inputs.

Rate this book Rate this book

We would LOVE it if you could help us and other readers by reviewing the book