Publicado por LAP LAMBERT Academic Publishing Jun 2010, 2010
ISBN 10: 3838377435 ISBN 13: 9783838377438
Idioma: Inglés
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
EUR 68,00
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Añadir al carritoTaschenbuch. Condición: Neu. Neuware -This book forms the much needed strong interface between algorithmic complexity and computer experiments using a careful blending of traditional ideas in algorithms with untraditional research in computer experiments (esp. fitting stochastic models to non-random data). While establishing the aforesaid interface, the important role of statistical bounds and their empirical estimates obtained over a finite range (called empirical O) is discovered as a bonus. While these bounds are very valuable for the average case, our research suggests in addition that there is no need to be over-conservative in the worst case just as the statistical bounds safeguard against making tall optimistic claims for the best cases. In short the statistical bounds have a sense of 'calculated guarantee' that is neither too risky nor too conservative. In parallel computing, with every change of the processor, it can be argued that it is the weight of the operation that changes. Hence, if the bound is itself based on weights, it should be deemed as the ideal one.Books on Demand GmbH, Überseering 33, 22297 Hamburg 192 pp. Englisch.
Publicado por LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838377435 ISBN 13: 9783838377438
Idioma: Inglés
Librería: Mispah books, Redhill, SURRE, Reino Unido
EUR 134,59
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Añadir al carritoPaperback. Condición: Like New. Like New. book.
Publicado por LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838377435 ISBN 13: 9783838377438
Idioma: Inglés
Librería: moluna, Greven, Alemania
EUR 55,21
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Añadir al carritoCondición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Chakraborty SoubhikDr. Soubhik Chakraborty is a Reader, Deptt. of Applied Mathematics, BIT Mesra, Ranchi, India with 50 international papers, an ACM and IEEE Reviewer (Comp. Rev./Trans. Comp.). Dr. Suman Kumar Sourabh is a Sr. Facu.
Publicado por LAP LAMBERT Academic Publishing Jun 2010, 2010
ISBN 10: 3838377435 ISBN 13: 9783838377438
Idioma: Inglés
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
EUR 68,00
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book forms the much needed strong interface between algorithmic complexity and computer experiments using a careful blending of traditional ideas in algorithms with untraditional research in computer experiments (esp. fitting stochastic models to non-random data). While establishing the aforesaid interface, the important role of statistical bounds and their empirical estimates obtained over a finite range (called empirical O) is discovered as a bonus. While these bounds are very valuable for the average case, our research suggests in addition that there is no need to be over-conservative in the worst case just as the statistical bounds safeguard against making tall optimistic claims for the best cases. In short the statistical bounds have a sense of 'calculated guarantee' that is neither too risky nor too conservative. In parallel computing, with every change of the processor, it can be argued that it is the weight of the operation that changes. Hence, if the bound is itself based on weights, it should be deemed as the ideal one. 192 pp. Englisch.
Publicado por LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838377435 ISBN 13: 9783838377438
Idioma: Inglés
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 68,00
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book forms the much needed strong interface between algorithmic complexity and computer experiments using a careful blending of traditional ideas in algorithms with untraditional research in computer experiments (esp. fitting stochastic models to non-random data). While establishing the aforesaid interface, the important role of statistical bounds and their empirical estimates obtained over a finite range (called empirical O) is discovered as a bonus. While these bounds are very valuable for the average case, our research suggests in addition that there is no need to be over-conservative in the worst case just as the statistical bounds safeguard against making tall optimistic claims for the best cases. In short the statistical bounds have a sense of 'calculated guarantee' that is neither too risky nor too conservative. In parallel computing, with every change of the processor, it can be argued that it is the weight of the operation that changes. Hence, if the bound is itself based on weights, it should be deemed as the ideal one.