Описание: For linear optimization models that can be formulated as linear programs with the block-angular structure, i.e. It can also be used as supplemental material in a second course in linear programming, computational mathematical programming, or large-scale systems.
Автор: Julia L. Higle; S. Sen Название: Stochastic Decomposition ISBN: 1461368456 ISBN-13(EAN): 9781461368458 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Motivation Stochastic Linear Programming with recourse represents one of the more widely applicable models for incorporating uncertainty within in which the SLP optimization models.
Автор: Michel Bercovier; Martin Gander; Ralf Kornhuber; O Название: Domain Decomposition Methods in Science and Engineering XVIII ISBN: 364226025X ISBN-13(EAN): 9783642260254 Издательство: Springer Рейтинг: Цена: 25155.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: th This volume contains a selection of 41 refereed papers presented at the 18 International Conference of Domain Decomposition Methods hosted by the School of ComputerScience and Engineering(CSE) of the Hebrew Universityof Jerusalem, Israel, January 12-17, 2008.
Автор: Bank Randolph Название: Domain Decomposition Methods in Science and Engineering XX ISBN: 364235274X ISBN-13(EAN): 9783642352744 Издательство: Springer Рейтинг: Цена: 23058.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Domain decomposition methods are iterative methods for solving the often very large linearor nonlinear systems of algebraic equations that arise when various problems in continuum mechanics are discretized using finite elements.
Автор: Francisco Chinesta; Roland Keunings; Adrien Leygue Название: The Proper Generalized Decomposition for Advanced Numerical Simulations ISBN: 3319028642 ISBN-13(EAN): 9783319028644 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In the PGD framework, this enriched model is solved only once to yield a parametric solution that includes all particular solutions for specific values of the parameters.The PGD has now attracted the attention of a large number of research groups worldwide.
Автор: Dmytro Iatsenko Название: Nonlinear Mode Decomposition ISBN: 3319200151 ISBN-13(EAN): 9783319200156 Издательство: Springer Рейтинг: Цена: 14365.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This work introduces a new method for analysing measured signals: nonlinear mode decomposition, or NMD. Scientists often need to be able to analyse time series data that include a complex combination of oscillatory modes of differing origin, usually contaminated by random fluctuations or noise.
Автор: Jocelyne Erhel; Martin J. Gander; Laurence Halpern Название: Domain Decomposition Methods in Science and Engineering XXI ISBN: 331905788X ISBN-13(EAN): 9783319057880 Издательство: Springer Рейтинг: Цена: 23757.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume contains a selection of papers presented at the 21st international conference on domain decomposition methods in science and engineering held in Rennes, France, June 25-29, 2012.
Автор: Irfan M. Ovacik; Reha Uzsoy Название: Decomposition Methods for Complex Factory Scheduling Problems ISBN: 1461379067 ISBN-13(EAN): 9781461379065 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The factory scheduling problem, that of allocating machines to competing jobs in manufacturing facilities to optimize or at least improve system performance, is encountered in many different manufacturing environments.
Andreas B?rmann develops novel approaches for the solution of network design problems as they arise in various contexts of applied optimization. At the example of an optimal expansion of the German railway network until 2030, the author derives a tailor-made decomposition technique for multi-period network design problems. Next, he develops a general framework for the solution of network design problems via aggregation of the underlying graph structure. This approach is shown to save much computation time as compared to standard techniques. Finally, the author devises a modelling framework for the approximation of the robust counterpart under ellipsoidal uncertainty, an often-studied case in the literature. Each of these three approaches opens up a fascinating branch of research which promises a better theoretical understanding of the problem and an increasing range of solvable application settings at the same time.
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