Stochastic Programming: Modeling Decision Problems Under Uncertainty, Klein Haneveld Willem K., Van Der Vlerk Maarten H., Romeijnders Ward
Автор: Shi-Yu Huang; Jaques Teghem Название: Stochastic Versus Fuzzy Approaches to Multiobjective Mathematical Programming under Uncertainty ISBN: 0792308875 ISBN-13(EAN): 9780792308874 Издательство: Springer Рейтинг: Цена: 277650.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Lastly the constraints, expressed by equalities or inequalities between linear expressions, are often softer in reality that what their mathematical expression might let us believe, and infeasibility as detected by the linear programming techniques can often been coped with by making trade-offs with the real world.
Автор: Dror Moshe, L`Ecuyer Pierre, Szidarovszky Ferenc Название: Modeling Uncertainty: An Examination of Stochastic Theory, Methods, and Applications ISBN: 1475783698 ISBN-13(EAN): 9781475783698 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The papers cover a great variety of topics in probability, statistics, economics, stochastic optimization, control theory, regression analysis, simulation, stochastic programming, Markov decision process, application in the HIV context, and others.
Автор: Marti Kurt Название: Optimization Under Stochastic Uncertainty: Methods, Control and Random Search Methods ISBN: 3030556611 ISBN-13(EAN): 9783030556617 Издательство: Springer Цена: 74530.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 1. Optimal Control under Stochastic Uncertainty.- 2. Stochastic Optimization of Regulators.- 3. Optimal Open-Loop Control of Dynamic Systems under Stochastic Uncertainty.- 4. Construction of feedback control by means of homotopy methods.- 5. Constructions of Limit State Functions.- 6. Random Search Procedures for Global Optimization.- 7. Controlled Random Search under Uncertainty.- 8. Controlled Random Search Procedures for Global Optimization.- 9. Mathematical Model of Random Search Methods and Elementary Properties.- 10. Special Random Search Methods.- 11. Accessibility Theorems.- 12. Convergence Theorems.- 13. Convergence of Stationary Random Search Methods for Positive Success Probability.- 14. Random Search Methods of convergence order U(n-").- 15. Random Search Methods with a Linear Rate of Convergence.- 16. Success/Failure-driven Random Direction Procedures.- 17. Hybrid Methods.- 18. Solving optimization problems under stochastic uncertainty by Random Search Methods(RSM).
Автор: Claude Greengard; Andrzej Ruszczynski Название: Decision Making Under Uncertainty ISBN: 1441930140 ISBN-13(EAN): 9781441930149 Издательство: Springer Рейтинг: Цена: 144410.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In the ideal world, major decisions would be made based on complete and reliable information available to the decision maker. The volume contains articles on model problems of decision making process in the energy and power industry when the available information is noisy and/or incomplete.
Автор: Vincent A. W. J. Marchau; Warren E. Walker; Pieter Название: Decision Making under Deep Uncertainty ISBN: 3030052516 ISBN-13(EAN): 9783030052515 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This open access book focuses on both the theory and practice associated with the tools and approaches for decisionmaking in the face of deep uncertainty. It explores approaches and tools supporting the design of strategic plans under deep uncertainty, and their testing in the real world, including barriers and enablers for their use in practice. The book broadens traditional approaches and tools to include the analysis of actors and networks related to the problem at hand. It also shows how lessons learned in the application process can be used to improve the approaches and tools used in the design process. The book offers guidance in identifying and applying appropriate approaches and tools to design plans, as well as advice on implementing these plans in the real world. For decisionmakers and practitioners, the book includes realistic examples and practical guidelines that should help them understand what decisionmaking under deep uncertainty is and how it may be of assistance to them.
Decision Making under Deep Uncertainty: From Theory to Practice is divided into four parts. Part I presents five approaches for designing strategic plans under deep uncertainty: Robust Decision Making, Dynamic Adaptive Planning, Dynamic Adaptive Policy Pathways, Info-Gap Decision Theory, and Engineering Options Analysis. Each approach is worked out in terms of its theoretical foundations, methodological steps to follow when using the approach, latest methodological insights, and challenges for improvement. In Part II, applications of each of these approaches are presented. Based on recent case studies, the practical implications of applying each approach are discussed in depth. Part III focuses on using the approaches and tools in real-world contexts, based on insights from real-world cases. Part IV contains conclusions and a synthesis of the lessons that can be drawn for designing, applying, and implementing strategic plans under deep uncertainty, as well as recommendations for future work.
The publication of this book has been funded by the Radboud University, the RAND Corporation, Delft University of Technology, and Deltares.
Автор: Martin L. Puterman Название: Markov Decision Processes: Discrete Stochastic Dynamic Programming ISBN: 0471727822 ISBN-13(EAN): 9780471727828 Издательство: Wiley Рейтинг: Цена: 137230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book is an up-to-date, unified and rigorous treatment of theoretical, computational and applied research on Markov decision process models. The concentration of the book is on infinite-horizon discrete-time models, and it also discusses arbitrary state spaces, finite-horizon and continuous-time discrete-state models.
Автор: Shi-Yu Huang; Jaques Teghem Название: Stochastic Versus Fuzzy Approaches to Multiobjective Mathematical Programming under Uncertainty ISBN: 9401074496 ISBN-13(EAN): 9789401074490 Издательство: Springer Рейтинг: Цена: 277650.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Lastly the constraints, expressed by equalities or inequalities between linear expressions, are often softer in reality that what their mathematical expression might let us believe, and infeasibility as detected by the linear programming techniques can often been coped with by making trade-offs with the real world.
Автор: Harald Held Название: Shape Optimization under Uncertainty from a Stochastic Programming Point of View ISBN: 3834809098 ISBN-13(EAN): 9783834809094 Издательство: Springer Рейтинг: Цена: 97820.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Optimization problems whose constraints involve partial differential equations (PDEs) are relevant in many areas of technical, industrial, and economic app- cations. At the same time, they pose challenging mathematical research problems in numerical analysis and optimization. The present text is among the ?rst in the research literature addressing stochastic uncertainty in the context of PDE constrained optimization. The focus is on shape optimization for elastic bodies under stochastic loading. Analogies to ?nite dim- sional two-stage stochastic programming drive the treatment, with shapes taking the role of nonanticipative decisions.The main results concern level set-based s- chastic shape optimization with gradient methods involving shape and topological derivatives. The special structure of the elasticity PDE enables the numerical - lution of stochastic shape optimization problems with an arbitrary number of s- narios without increasing the computational effort signi?cantly. Both risk neutral and risk averse models are investigated. This monograph is based on a doctoral dissertation prepared during 2004-2008 at the Chair of Discrete Mathematics and Optimization in the Department of Ma- ematics of the University of Duisburg-Essen. The work was supported by the Deutsche Forschungsgemeinschaft (DFG) within the Priority Program "Optimi- tion with Partial Differential Equations." Rudiger Schultz Acknowledgments I owe a great deal to my supervisors, colleagues, and friends who have always supported, encouraged, andenlightenedmethroughtheirownresearch, comments, and questions.
Автор: Uwe Gotzes Название: Decision Making with Dominance Constraints in Two-Stage Stochastic Integer Programming ISBN: 3834808431 ISBN-13(EAN): 9783834808431 Издательство: Springer Рейтинг: Цена: 97820.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Uwe Gotzes analyzes an approach to account for risk aversion in two-stage models based upon partial orders on the set of real random variables. He illustrates the superiority of the proposed decomposition method over standard solvers for example with numerical experiments with instances from energy investment.
Автор: Urmila Diwekar; Amy David Название: BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems ISBN: 1493922815 ISBN-13(EAN): 9781493922819 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents the details of the BONUS algorithm and its real world applications in areas like sensor placement in large scale drinking water networks, sensor placement in advanced power systems, water management in power systems, and capacity expansion of energy systems.
Автор: Shige Peng Название: Nonlinear Expectations and Stochastic Calculus under Uncertainty ISBN: 3662599023 ISBN-13(EAN): 9783662599020 Издательство: Springer Рейтинг: Цена: 79190.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book is focused on the recent developments on problems of probability model uncertainty by using the notion of nonlinear expectations and, in particular, sublinear expectations. It provides a gentle coverage of the theory of nonlinear expectations and related stochastic analysis. Many notions and results, for example, G-normal distribution, G-Brownian motion, G-Martingale representation theorem, and related stochastic calculus are first introduced or obtained by the author.This book is based on Shige Peng’s lecture notes for a series of lectures given at summer schools and universities worldwide. It starts with basic definitions of nonlinear expectations and their relation to coherent measures of risk, law of large numbers and central limit theorems under nonlinear expectations, and develops into stochastic integral and stochastic calculus under G-expectations. It ends with recent research topic on G-Martingale representation theorem and G-stochastic integral for locally integrable processes.With exercises to practice at the end of each chapter, this book can be used as a graduate textbook for students in probability theory and mathematical finance. Each chapter also concludes with a section Notes and Comments, which gives history and further references on the material covered in that chapter.Researchers and graduate students interested in probability theory and mathematical finance will find this book very useful.
Автор: Peng Shige Название: Nonlinear Expectations and Stochastic Calculus Under Uncertainty: With Robust Clt and G-Brownian Motion ISBN: 3662599058 ISBN-13(EAN): 9783662599051 Издательство: Springer Рейтинг: Цена: 111790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book is focused on the recent developments on problems of probability model uncertainty by using the notion of nonlinear expectations and, in particular, sublinear expectations.
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