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Dynamic stochastic optimization

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 ris...

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Detalles Bibliográficos
Autores principales: Marti, Kurt, Ermoliev, Yuri, Pflug, Georg
Lenguaje:eng
Publicado: Springer 2004
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-642-55884-9
http://cds.cern.ch/record/2146590
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author Marti, Kurt
Ermoliev, Yuri
Pflug, Georg
author_facet Marti, Kurt
Ermoliev, Yuri
Pflug, Georg
author_sort Marti, Kurt
collection CERN
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.
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spelling cern-21465902021-04-21T19:43:21Zdoi:10.1007/978-3-642-55884-9http://cds.cern.ch/record/2146590engMarti, KurtErmoliev, YuriPflug, GeorgDynamic stochastic optimizationMathematical Physics and MathematicsUncertainties 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.Springeroai:cds.cern.ch:21465902004
spellingShingle Mathematical Physics and Mathematics
Marti, Kurt
Ermoliev, Yuri
Pflug, Georg
Dynamic stochastic optimization
title Dynamic stochastic optimization
title_full Dynamic stochastic optimization
title_fullStr Dynamic stochastic optimization
title_full_unstemmed Dynamic stochastic optimization
title_short Dynamic stochastic optimization
title_sort dynamic stochastic optimization
topic Mathematical Physics and Mathematics
url https://dx.doi.org/10.1007/978-3-642-55884-9
http://cds.cern.ch/record/2146590
work_keys_str_mv AT martikurt dynamicstochasticoptimization
AT ermolievyuri dynamicstochasticoptimization
AT pfluggeorg dynamicstochasticoptimization