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.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Preface. Contributing Authors. Volume I. 1. Random Sampling: Sorting and Selection; D. Krizanc, S. Rajasekaran. 2. Simplified Analysis of Randomized Algorithms for Searching, Sorting, and Selection; M.T. Goodrich, R. Tamassia. 3. Concentration of Measure for Randomized Algorithms: Techniques and Analysis; D. Dubhashi, S. Sen. 4. Randomization in Graph Optimization Problems: A Survey; D.R. Karger. 5. The Delay Sequence Argument; A. Ranade. 6. Randomized Algorithms for Geometric Optimization Problems; P.K. Agarwal, S. Sen. 7. Randomized Geometry Algorithms for Coarse Grained Parallel Computers; Xiaotie Deng. 8. A Randomized Approach to Robot Path Planning Based on Lazy Evaluation; R. Bohlin, L.E. Kavraki. 9. The Power of Two Random Choices: A Survey of Techniques and Results; M. Mitzenmacher, A.W. Richa, R. Sitaraman. 10. Randomized Techniques for Modelling Faults and Achieving Robust Computing; S.E. Nikoletseas, P.G. Spirakis. 11. Randomized Communication in Radio Networks; B.S. Chlebus. Index. Preface. Contributing Authors. Volume II. 12. A Guide to Concentration Bounds; J. Díaz, J. Petit, M. Serna. 13. Bounded Error Probabilistic Finite State Automata; A. Condon. 14. Communication Protocols - An Exemplary Study of the Power of Randomness; J. Hromkovic. 15. Property Testing; D. Ron. 16. The Random Projection Method; S. Vempala. 17. Error Estimates for Indirect Measurements: Randomized vs. Deterministic Algorithms for `Black-Box' Programs; V.Kreinovich, R. Trejo. 18. Derandomization in Combinatorial Optimization; A. Srivastav. 19. Derandomizing Complexity Classes; P.B. Miltersen. Index.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
EUR 17,38 per la spedizione da Regno Unito a Italia
Destinazione, tempi e costiEUR 9,70 per la spedizione da Germania a Italia
Destinazione, tempi e costiDa: moluna, Greven, Germania
Condizione: New. Codice articolo 447070941
Quantità: Più di 20 disponibili
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
Buch. 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 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. 1052 pp. Englisch. Codice articolo 9780792369592
Quantità: 2 disponibili
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
Buch. 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 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. 1052 pp. Englisch. Codice articolo 9780792369592
Quantità: 2 disponibili
Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: New. Codice articolo 1841535-n
Quantità: Più di 20 disponibili
Da: Ria Christie Collections, Uxbridge, Regno Unito
Condizione: New. In. Codice articolo ria9780792369592_new
Quantità: Più di 20 disponibili
Da: AHA-BUCH GmbH, Einbeck, Germania
Buch. 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 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. Codice articolo 9780792369592
Quantità: 1 disponibili
Da: GreatBookPricesUK, Woodford Green, Regno Unito
Condizione: New. Codice articolo 1841535-n
Quantità: Più di 20 disponibili
Da: Revaluation Books, Exeter, Regno Unito
Hardcover. Condizione: Brand New. 1st edition. 1048 pages. 9.75x6.50x2.50 inches. In Stock. Codice articolo x-0792369599
Quantità: 2 disponibili
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
Condizione: New. Codice articolo ABLIING23Feb2416190184123
Quantità: Più di 20 disponibili
Da: GreatBookPricesUK, Woodford Green, Regno Unito
Condizione: As New. Unread book in perfect condition. Codice articolo 1841535
Quantità: Più di 20 disponibili