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Advances in Randomized Parallel Computing
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Hardcover. Condizione: Very Good. No Jacket. Former library book; May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.
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Advances in Randomized Parallel Computing
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Advances in Randomized Parallel Computing (Combinatorial Optimization)
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Advances in Randomized Parallel Computing (Combinatorial Optimization)
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Advances in Randomized Parallel Computing (Combinatorial Optimization, 5)
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Advances in Randomized Parallel Computing
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Advances in Randomized Parallel Computing
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Advances in Randomized Parallel Computing (Combinatorial Optimization)
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Advances in Randomized Parallel Computing
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Advances in Randomized Parallel Computing
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Advances in Randomized Parallel Computing
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Advances in Randomized Parallel Computing
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Advances in Randomized Parallel Computing
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Advances in Randomized Parallel Computing
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Advances in Randomized Parallel Computing
Da: Books Puddle, New York, NY, U.S.A.
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Condizione: New. pp. 316.
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Advances in Randomized Parallel Computing
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
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EUR 194,62
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Advances in Randomized Parallel Computing
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
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Aggiungi al carrelloCondizione: New. 1999. 1999th Edition. hardcover. . . . . .
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Advances in Randomized Parallel Computing
Lingua: Inglese
Editore: Kluwer Academic Publishers, US, 1999
ISBN 10: 0792357140 ISBN 13: 9780792357148
Da: Rarewaves.com USA, London, LONDO, Regno Unito
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Aggiungi al carrelloHardback. Condizione: New. 1999 ed. 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 in practice. This book is a collection of articles written by renowned experts in the area of randomized parallel computing. A brief introduction to randomized algorithms In the aflalysis 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( n log n). The average case analysis is conducted with an assumption on the input space. The assumption made to arrive at the O( n log n) 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 p0:.sible inputs.
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Advances in Randomized Parallel Computing
Lingua: Inglese
Editore: Springer US, Springer New York, 2011
ISBN 10: 1461332842 ISBN 13: 9781461332848
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 in practice. This book is a collection of articles written by renowned experts in the area of randomized parallel computing. A brief introduction to randomized algorithms In the aflalysis 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( n log n). The average case analysis is conducted with an assumption on the input space. The assumption made to arrive at the O( n log n) 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 p0:.sible inputs.
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Advances in Randomized Parallel Computing (Combinatorial Optimization)
Da: Revaluation Books, Exeter, Regno Unito
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EUR 230,17
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Aggiungi al carrelloPaperback. Condizione: Brand New. reprint edition. 352 pages. 9.45x6.30x0.72 inches. In Stock.
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Advances in Randomized Parallel Computing
Da: Kennys Bookstore, Olney, MD, U.S.A.
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Condizione: New. 2011. Paperback. . . . . . Books ship from the US and Ireland.
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Advances in Randomized Parallel Computing
Da: Kennys Bookstore, Olney, MD, U.S.A.
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Condizione: New. 1999. 1999th Edition. hardcover. . . . . . Books ship from the US and Ireland.
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Advances in Randomized Parallel Computing
Lingua: Inglese
Editore: Kluwer Academic Publishers, US, 1999
ISBN 10: 0792357140 ISBN 13: 9780792357148
Da: Rarewaves.com UK, London, Regno Unito
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Aggiungi al carrelloHardback. Condizione: New. 1999 ed. 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 in practice. This book is a collection of articles written by renowned experts in the area of randomized parallel computing. A brief introduction to randomized algorithms In the aflalysis 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( n log n). The average case analysis is conducted with an assumption on the input space. The assumption made to arrive at the O( n log n) 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 p0:.sible inputs.
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Aggiungi al carrelloBuch. Condizione: Neu. Neuware - 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 in practice. This book is a collection of articles written by renowned experts in the area of randomized parallel computing. A brief introduction to randomized algorithms In the aflalysis 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( n log n). The average case analysis is conducted with an assumption on the input space. The assumption made to arrive at the O( n log n) 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 p0:.sible inputs.
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Advances in Randomized Parallel Computing
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 in practice. This book is a collection of articles written by renowned experts in the area of randomized parallel computing. A brief introduction to randomized algorithms In the aflalysis 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( n log n). The average case analysis is conducted with an assumption on the input space. The assumption made to arrive at the O( n log n) 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 p0:.sible inputs. 316 pp. Englisch.
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Advances in Randomized Parallel Computing
Da: Majestic Books, Hounslow, Regno Unito
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Advances in Randomized Parallel Computing
Da: Majestic Books, Hounslow, Regno Unito
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Aggiungi al carrelloCondizione: New. Print on Demand pp. 316 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam.
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Advances in Randomized Parallel Computing
Lingua: Inglese
Editore: Springer US, Springer New York Okt 2011, 2011
ISBN 10: 1461332842 ISBN 13: 9781461332848
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 in practice. This book is a collection of articles written by renowned experts in the area of randomized parallel computing. A brief introduction to randomized algorithms In the aflalysis 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( n log n). The average case analysis is conducted with an assumption on the input space. The assumption made to arrive at the O( n log n) 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 p0:.sible inputs.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 316 pp. Englisch.
