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Handbook of Randomized Computing von Sanguthevar (Hrsg.) Rajasekaran

Volume I/II
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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.


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.


AutorRajasekaran, Sanguthevar (Hrsg.) / Rolim, José (Hrsg.) / Reif, J. H. (Hrsg.) / Pardalos, Panos M. (Hrsg.)
EinbandKartonierter Einband (Kt)
Erscheinungsjahr2013
Seitenangabe1052 S.
LieferstatusFolgt in ca. 10 Arbeitstagen
AusgabekennzeichenEnglisch
AbbildungenPaperback
MasseH23.5 cm x B15.5 cm x D5.7 cm 1'576 g
AuflageSoftcover reprint of the original 1st ed. 2001
ReiheCombinatorial Optimization
VerlagSpringer Us

Alle Bände der Reihe "Combinatorial Optimization"

Über den Autor Sanguthevar (Hrsg.) Rajasekaran

Sanguthevar Rajasekaran is the UTC Chair Professor of Computer Science and Engineering and director of the Booth Engineering Center for Advanced Technologies at the University of Connecticut. He received a Ph.D. in computer science from Harvard University. He is a fellow of the IEEE and the AAAS and an elected member of the Connecticut Academy of Science and Engineering. His research interests include bioinformatics, parallel algorithms, data mining, randomized computing, computer simulations, and combinatorial optimization.Lance Fiondella is an assistant professor in the Department of Electrical and Computer Engineering at the University of Massachusetts Dartmouth. He received a Ph.D. in computer science and engineering from the University of Connecticut. His research interests include algorithms, reliability engineering, and risk analysis. Mohamed Ahmed is a program manager at Microsoft Windows Azure Mobile. He received a PhD in computer science and engineering from the University of Connecticut. His research interests include multi/many-cores technologies, high-performance computing, parallel programming, cloud computing, and GPU programming.Reda A. Ammar is a professor and the head of the Department of Computer Science and Engineering at the University of Connecticut. He received a PhD in computer science from the University of Connecticut. He is the president of the International Society of Computers and Their Applications and editor-in-chief of the International Journal on Computers and Their Applications. His primary research interests encompass distributed and high-performance computing and real-time systems.

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