Stochastic optimization methods applications von marti kurt (29 Ergebnisse)

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Optimization problems arising in practice usually contain several random parameters. Hence, in order to obtain optimal solutions being robust with respect to random parameter variations, the mostly available statistical information about the random parameters should be considered already at the planning phase. The original problem with random parameters must be replaced by an appropriate deterministic substitute problem, and efficient numerical solution or approximation techniques have to be developed for those problems. This proceedings volume contains a selection of papers on modelling techniques, approximation methods, numerical solution procedures for stochastic optimization problems and applications to the reliability-based optimization of concrete technical or economic systems.…

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This volume includes a selection of papers presented at the GAMM/ IFIP-Workshop on IIStochastic Optimization: Numerical Methods and ll Technical Applications , held at the Federal Armed Forces Univer sity Munich, May 29-31, 1990. The objective of this meeting was to bring together scientists from Stochastic Programming and from those Engineering areas, where Mathematical Programming models are common tools, as e.g. Optimal structural Design, Power Dispatch, Acid Rain Abatement etc . Hence, the aim was to discuss the effects of taking into account the in herent randomness of some data of these problems, i.e. considering Stochastic Programming instead of Mathematical Programming models in order to get solutions being more reliable, but not more expen sive. An international programme committe2 was formed which included H.A. Eschenauer (Germany) P. Kall (Switzerland) K. Marti (Germany, Chairman) J. Mayer (Hungary) G.I. Schueller (Austria) Although the number of participants had to be small for technical reasons, the area covered by the lectures during the workshop was rather broad. It contains theoretical insight into stochastic pro gramming problems, new computational approaches, analyses of known solution methods, and applications in such very different technical fields as ecology, energy demands, and optimal reliability of me chanical structures. In particular, the applied presentation also pointed to several open methodological problems.…

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Zustand: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | This volume includes a selection of papers presented at the GAMM/ IFIP-Workshop on IIStochastic Optimization: Numerical Methods and ll Technical Applications , held at the Federal Armed Forces Univer­ sity Munich, May 29-31, 1990. The objective of this meeting was to bring together scientists from Stochastic Programming and from those Engineering areas, where Mathematical Programming models are common tools, as e.g. Optimal structural Design, Power Dispatch, Acid Rain Abatement etc . Hence, the aim was to discuss the effects of taking into account the in­ herent randomness of some data of these problems, i.e. considering Stochastic Programming instead of Mathematical Programming models in order to get solutions being more reliable, but not more expen­ sive. An international programme committe2 was formed which included H.A. Eschenauer (Germany) P. Kall (Switzerland) K. Marti (Germany, Chairman) J. Mayer (Hungary) G.I. Schueller (Austria) Although the number of participants had to be small for technical reasons, the area covered by the lectures during the workshop was rather broad. It contains theoretical insight into stochastic pro­ gramming problems, new computational approaches, analyses of known solution methods, and applications in such very different technical fields as ecology, energy demands, and optimal reliability of me­ chanical structures. In particular, the applied presentation also pointed to several open methodological problems.…

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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book examines optimization problems that in practice involve random model parameters. It outlines the computation of robust optimal solutions, i.e., optimal solutions that are insensitive to random parameter variations, where appropriate deterministic substitute problems are needed. Based on the probability distribution of the random data and using decision theoretical concepts, optimization problems under stochastic uncertainty are converted into corresponding deterministic problems.Due to the probabilities and expectations involved, the book also shows how to apply approximative solution techniques. Several deterministic and stochastic approximation methods are provided: Taylor expansion methods, regression and response surface methods (RSM), probability inequalities, multiple linearization of survival/failure domains, discretization methods, convex approximation/deterministic descent directions/efficient points, stochastic approximation and gradient procedures, and differentiation formulas for probabilities and expectations.The fourth edition of this classic text has been carefully and thoroughly revised. It includes new chapters on the solution of stochastic linear programs by discretization of the underlying probability distribution, and on solving deterministic optimization problems by means of controlled random search methods and multiple random search procedures. It also presents a new application of stochastic optimization methods to machine learning problems with different loss functions. For the computation of optimal feedback controls under stochasticuncertainty, besides the open-loop feedback procedures, a new method based on Taylor expansions with respect to the gain parameters is presented.The book is intended for researchers and graduate students who are interested in stochastics, stochastic optimization, and control. It will also benefit professionals and practitioners whose work involves technical, economicand/or operations research problems under stochastic uncertainty. …

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Zustand: gut. 2001. Stochastic Optimization Techniques: Numerical Methods and Technical Applications (Lecture Notes in Economics and Mathematical Systems) In deutscher Sprache. pages.

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Taschenbuch. Zustand: Neu. Stochastic Optimization | Numerical Methods and Technical Applications | Kurt Marti | Taschenbuch | Lecture Notes in Economics and Mathematical Systems | viii | Englisch | 1992 | Springer | EAN 9783540552253 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. …

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book examines optimization problems that in practice involve random model parameters. It outlines the computation of robust optimal solutions, i.e., optimal solutions that are insensitive to random parameter variations, where appropriate deterministic substitute problems are needed. Based on the probability distribution of the random data and using decision theoretical concepts, optimization problems under stochastic uncertainty are converted into corresponding deterministic problems.Due to the probabilities and expectations involved, the book also shows how to apply approximative solution techniques. Several deterministic and stochastic approximation methods are provided: Taylor expansion methods, regression and response surface methods (RSM), probability inequalities, multiple linearization of survival/failure domains, discretization methods, convex approximation/deterministic descent directions/efficient points, stochastic approximation and gradient procedures, and differentiation formulas for probabilities and expectations.The fourth edition of this classic text has been carefully and thoroughly revised. It includes new chapters on the solution of stochastic linear programs by discretization of the underlying probability distribution, and on solving deterministic optimization problems by means of controlled random search methods and multiple random search procedures. It also presents a new application of stochastic optimization methods to machine learning problems with different loss functions. For the computation of optimal feedback controls under stochasticuncertainty, besides the open-loop feedback procedures, a new method based on Taylor expansions with respect to the gain parameters is presented.The book is intended for researchers and graduate students who are interested in stochastics, stochastic optimization, and control. It will also benefit professionals and practitioners whose work involves technical, economicand/or operations research problems under stochastic uncertainty.…

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