DECOMP: an Implementation of Dantzig-Wolfe Decomposition for Linear Programming, James K. Ho; Rangaraja P. Sundarraj
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: 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: 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.
Автор: Antonio J. Conejo; Enrique Castillo; Roberto Mingu Название: Decomposition Techniques in Mathematical Programming ISBN: 3642066070 ISBN-13(EAN): 9783642066078 Издательство: Springer Рейтинг: Цена: 22201.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Optimization plainly dominates the design, planning, operation, and c- trol of engineering systems. This is a book on optimization that considers particular cases of optimization problems, those with a decomposable str- ture that can be advantageously exploited. Those decomposable optimization problems are ubiquitous in engineering and science applications. The book considers problems with both complicating constraints and complicating va- ables, and analyzes linear and nonlinear problems, with and without in- ger variables. The decomposition techniques analyzed include Dantzig-Wolfe, Benders, Lagrangian relaxation, Augmented Lagrangian decomposition, and others. Heuristic techniques are also considered. Additionally, a comprehensive sensitivity analysis for characterizing the solution of optimization problems is carried out. This material is particularly novel and of high practical interest. This book is built based on many clarifying, illustrative, and compu- tional examples, which facilitate the learning procedure. For the sake of cl- ity, theoretical concepts and computational algorithms are assembled based on these examples. The results are simplicity, clarity, and easy-learning. We feel that this book is needed by the engineering community that has to tackle complex optimization problems, particularly by practitioners and researchersinEngineering, OperationsResearch, andAppliedEconomics.The descriptions of most decomposition techniques are available only in complex and specialized mathematical journals, di?cult to understand by engineers. A book describing a wide range of decomposition techniques, emphasizing problem-solving, and appropriately blending theory and application, was not previously available.
Автор: George B. Dantzig; Mukund N. Thapa Название: Linear Programming 2 ISBN: 1441931406 ISBN-13(EAN): 9781441931405 Издательство: Springer Рейтинг: Цена: 11313.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: George Dantzig is widely regarded as the founder of this subject with his invention of the simplex algorithm in the 1940`s. In this second volume, the theory of the items discussed in the first volume is expanded to include such additional advanced topics as variants of the simplex method;
Автор: George B. Dantzig; Mukund N. Thapa Название: Linear Programming 1 ISBN: 1475781121 ISBN-13(EAN): 9781475781120 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Encompassing all the major topics students will encounter in courses on the subject, the authors teach both the underlying mathematical foundations and how these ideas are implemented in practice.
Автор: Andrea Toselli; Olof Widlund Название: Domain Decomposition Methods - Algorithms and Theory ISBN: 3642058485 ISBN-13(EAN): 9783642058486 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book offers a comprehensive presentation of some of the most successful and popular domain decomposition preconditioners for finite and spectral element approximations of partial differential equations. It covers in detail important methods such as FETI and balancing Neumann-Neumann methods and algorithms for spectral element methods.
Автор: Ralf Kornhuber; Ronald W. Hoppe; Jacques Periaux; Название: Domain Decomposition Methods in Science and Engineering ISBN: 3540225234 ISBN-13(EAN): 9783540225232 Издательство: Springer Рейтинг: Цена: 24456.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Domain decomposition is an active, interdisciplinary research area that is devoted to the development, analysis and implementation of coupling and decoupling strategies in mathematics, computational science, engineering and industry.
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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