Dimensionality Reduction in Dynamic Optimization Under Uncertainty PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Dimensionality Reduction in Dynamic Optimization Under Uncertainty PDF full book. Access full book title Dimensionality Reduction in Dynamic Optimization Under Uncertainty by Napat Rujeerapaiboon. Download full books in PDF and EPUB format.
Author: Napat Rujeerapaiboon
Publisher:
ISBN:
Category :
Languages : en
Pages : 177
Get Book Here
Book Description
Mots-clés de l'auteur: convex optimization ; conic programming ; distributionally robust optimization ; stochastic programming ; linear decision rules ; portfolio optimization ; growth-optimal portfolio ; value-at-risk ; Chebyshev inequality ; electricity market.
Author: Napat Rujeerapaiboon
Publisher:
ISBN:
Category :
Languages : en
Pages : 177
Get Book Here
Book Description
Mots-clés de l'auteur: convex optimization ; conic programming ; distributionally robust optimization ; stochastic programming ; linear decision rules ; portfolio optimization ; growth-optimal portfolio ; value-at-risk ; Chebyshev inequality ; electricity market.
Author: Kurt Marti
Publisher: Springer Science & Business Media
ISBN: 3642558844
Category : Science
Languages : en
Pages : 337
Get Book Here
Book Description
Uncertainties and changes are pervasive characteristics of modern systems involving interactions between humans, economics, nature and technology. These systems are often too complex to allow for precise evaluations and, as a result, the lack of proper management (control) may create significant risks. In order to develop robust strategies we need approaches which explic itly deal with uncertainties, risks and changing conditions. One rather general approach is to characterize (explicitly or implicitly) uncertainties by objec tive or subjective probabilities (measures of confidence or belief). This leads us to stochastic optimization problems which can rarely be solved by using the standard deterministic optimization and optimal control methods. In the stochastic optimization the accent is on problems with a large number of deci sion and random variables, and consequently the focus ofattention is directed to efficient solution procedures rather than to (analytical) closed-form solu tions. Objective and constraint functions of dynamic stochastic optimization problems have the form of multidimensional integrals of rather involved in that may have a nonsmooth and even discontinuous character - the tegrands typical situation for "hit-or-miss" type of decision making problems involving irreversibility ofdecisions or/and abrupt changes ofthe system. In general, the exact evaluation of such functions (as is assumed in the standard optimization and control theory) is practically impossible. Also, the problem does not often possess the separability properties that allow to derive the standard in control theory recursive (Bellman) equations.
Author: Tilahun, Surafel Luleseged
Publisher: IGI Global
ISBN: 1522550925
Category : Computers
Languages : en
Pages : 327
Get Book Here
Book Description
When it comes to optimization techniques, in some cases, the available information from real models may not be enough to construct either a probability distribution or a membership function for problem solving. In such cases, there are various theories that can be used to quantify the uncertain aspects. Optimization Techniques for Problem Solving in Uncertainty is a scholarly reference resource that looks at uncertain aspects involved in different disciplines and applications. Featuring coverage on a wide range of topics including uncertain preference, fuzzy multilevel programming, and metaheuristic applications, this book is geared towards engineers, managers, researchers, and post-graduate students seeking emerging research in the field of optimization.
Author: Peter Jason Kalman
Publisher:
ISBN:
Category : Economic life of fixed assets
Languages : en
Pages : 44
Get Book Here
Book Description
Author: Peter Jason Kalman
Publisher:
ISBN:
Category : Replacement of industrial equipment
Languages : en
Pages : 30
Get Book Here
Book Description
Author: Jonas Ekblom
Publisher: Linköping University Electronic Press
ISBN: 9176852024
Category :
Languages : en
Pages : 36
Get Book Here
Book Description
This thesis addresses the topic of decision making under uncertainty, with particular focus on financial markets. The aim of this research is to support improved decisions in practice, and related to this, to advance our understanding of financial markets. Stochastic optimization provides the tools to determine optimal decisions in uncertain environments, and the optimality conditions of these models produce insights into how financial markets work. To be more concrete, a great deal of financial theory is based on optimality conditions derived from stochastic optimization models. Therefore, an important part of the development of financial theory is to study stochastic optimization models that step-by-step better capture the essence of reality. This is the motivation behind the focus of this thesis, which is to study methods that in relation to prevailing models that underlie financial theory allow additional real-world complexities to be properly modeled. The overall purpose of this thesis is to develop and evaluate stochastic optimization models that support improved decisions under uncertainty on financial markets. The research into stochastic optimization in financial literature has traditionally focused on problem formulations that allow closed-form or `exact' numerical solutions; typically through the application of dynamic programming or optimal control. The focus in this thesis is on two other optimization methods, namely stochastic programming and approximate dynamic programming, which open up opportunities to study new classes of financial problems. More specifically, these optimization methods allow additional and important aspects of many real-world problems to be captured. This thesis contributes with several insights that are relevant for both financial and stochastic optimization literature. First, we show that the modeling of several real-world aspects traditionally not considered in the literature are important components in a model which supports corporate hedging decisions. Specifically, we document the importance of modeling term premia, a rich asset universe and transaction costs. Secondly, we provide two methodological contributions to the stochastic programming literature by: (i) highlighting the challenges of realizing improved decisions through more stages in stochastic programming models; and (ii) developing an importance sampling method that can be used to produce high solution quality with few scenarios. Finally, we design an approximate dynamic programming model that gives close to optimal solutions to the classic, and thus far unsolved, portfolio choice problem with constant relative risk aversion preferences and transaction costs, given many risky assets and a large number of time periods.
Author: Phebe Theofano Vayanos
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Dynamic decision problems affected by uncertain data are notoriously hard to solve due to the presence of adaptive decision variables which must be modeled as functions or decision rules of some (or all) of the uncertain parameters. All exact solution techniques suffer from the curse of dimensionality while most solution schemes assume that the decision-maker cannot influence the sequence in which the uncertain parameters are revealed. The main objective of this thesis is to devise tractable approximation schemes for dynamic decision-making under uncertainty. For this purpose, we develop new decision rule approximations whereby the adaptive decisions are approximated by finite linear combinations of prescribed basis functions. In the first part of this thesis, we develop a tractable unifying framework for solving convex multi-stage robust optimization problems with general nonlinear dependence on the uncertain parameters. This is achieved by combining decision rule and constraint sampling approximations. The synthesis of these two methodologies provides us with a versatile data-driven framework, which circumvents the need for estimating the distribution of the uncertain parameters and offers almost complete freedom in the choice of basis functions. We obtain a-priori probabilistic guarantees on the feasibility properties of the optimal decision rule and demonstrate asymptotic consistency of the approximation. We then investigate the problem of hedging and pricing path-dependent electricity derivatives such as swing options, which play a crucial risk management role in today's deregulated energy markets. Most of the literature on the topic assumes that a swing option can be assigned a unique fair price. This assumption nevertheless fails to hold in real-world energy markets, where the option admits a whole interval of prices consistent with those of traded instruments. We formulate two large-scale robust optimization problems whose optimal values yield the endpoints of this interval. We analyze and exploit the structure of the optimal decision rule to formulate approximate problems that can be solved efficiently with the decision rule approach discussed in the first part of the thesis. Most of the literature on stochastic and robust optimization assumes that the sequence in which the uncertain parameters unfold is independent of the decision-maker's actions. Nevertheless, in numerous real-world decision problems, the time of information discovery can be influenced by the decision-maker. In the last part of this thesis, we propose a decision rule-based approximation scheme for multi-stage problems with decision-dependent information discovery. We assess our approach on a problem of infrastructure and production planning in offshore oil fields.
Author: Yuanguo Zhu
Publisher: Springer
ISBN: 9811321345
Category : Technology & Engineering
Languages : en
Pages : 211
Get Book Here
Book Description
This book introduces the theory and applications of uncertain optimal control, and establishes two types of models including expected value uncertain optimal control and optimistic value uncertain optimal control. These models, which have continuous-time forms and discrete-time forms, make use of dynamic programming. The uncertain optimal control theory relates to equations of optimality, uncertain bang-bang optimal control, optimal control with switched uncertain system, and optimal control for uncertain system with time-delay. Uncertain optimal control has applications in portfolio selection, engineering, and games. The book is a useful resource for researchers, engineers, and students in the fields of mathematics, cybernetics, operations research, industrial engineering, artificial intelligence, economics, and management science.
Author: Gerhard Hambusch
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Author: Massimiliano Vasile
Publisher: Springer Nature
ISBN: 3030601668
Category : Science
Languages : en
Pages : 573
Get Book Here
Book Description
In an expanding world with limited resources, optimization and uncertainty quantification have become a necessity when handling complex systems and processes. This book provides the foundational material necessary for those who wish to embark on advanced research at the limits of computability, collecting together lecture material from leading experts across the topics of optimization, uncertainty quantification and aerospace engineering. The aerospace sector in particular has stringent performance requirements on highly complex systems, for which solutions are expected to be optimal and reliable at the same time. The text covers a wide range of techniques and methods, from polynomial chaos expansions for uncertainty quantification to Bayesian and Imprecise Probability theories, and from Markov chains to surrogate models based on Gaussian processes. The book will serve as a valuable tool for practitioners, researchers and PhD students.
