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The Resource Dynamic Stochastic Optimization, edited by Kurt Marti, Yuri Ermoliev, Georg Ch. Pflug, (electronic resource)
Dynamic Stochastic Optimization, edited by Kurt Marti, Yuri Ermoliev, Georg Ch. Pflug, (electronic resource)
Resource Information
The item Dynamic Stochastic Optimization, edited by Kurt Marti, Yuri Ermoliev, Georg Ch. Pflug, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Oklahoma Libraries.This item is available to borrow from all library branches.
Resource Information
The item Dynamic Stochastic Optimization, edited by Kurt Marti, Yuri Ermoliev, Georg Ch. Pflug, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Oklahoma Libraries.
This item is available to borrow from all library branches.
 Summary
 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) closedform 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 "hitormiss" 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
 Language

 eng
 eng
 Edition
 1st ed. 2004.
 Extent
 1 online resource (VIII, 336 p.)
 Note
 Bibliographic Level Mode of Issuance: Monograph
 Contents

 I. Dynamic Decision Problems under Uncertainty: Modeling Aspects
 Reflections on Output Analysis for Multistage Stochastic Linear Programs
 Modeling Support for Multistage Recourse Problems
 Optimal Solutions for Undiscounted Variance Penalized Markov Decision Chains
 Approximation and Optimization for Stochastic Networks
 II. Dynamic Stochastic Optimization in Finance
 Optimal Stopping Problem and Investment Models
 Estimating LIBOR/Swaps SpotVolatilities: the EpiVolatility Model
 Structured Products for Pension Funds
 III. Optimal Control Under Stochastic Uncertainty
 Realtime Robust Optimal Trajectory Planning of Industrial Robots
 Adaptive Optimal Stochastic Trajectory Planning and Control (AOSTPC) for Robots
 IV. Tools for Dynamic Stochastic Optimization
 Solving Stochastic Programming Problems by Successive Regression Approximations — Numerical Results
 Stochastic Optimization of Risk Functions via Parametric Smoothing
 Optimization under Uncertainty using Momentum
 Perturbation Analysis of Chanceconstrained Programs under Variation of all Constraint Data
 The Value of Perfect Information as a Risk Measure
 New Bounds and Approximations for the Probability Distribution of the Length of the Critical Path
 Simplification of Recourse Models by Modification of Recourse Data
 Isbn
 9783642558849
 Label
 Dynamic Stochastic Optimization
 Title
 Dynamic Stochastic Optimization
 Statement of responsibility
 edited by Kurt Marti, Yuri Ermoliev, Georg Ch. Pflug
 Language

 eng
 eng
 Summary
 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) closedform 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 "hitormiss" 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
 Dewey number
 003
 http://bibfra.me/vocab/relation/httpidlocgovvocabularyrelatorsedt

 9upI8S452lU
 F_Rq0H4wPec
 ut838hvk8iA
 Image bit depth
 0
 Language note
 English
 LC call number

 Q295
 QA402.3402.37
 Literary form
 non fiction
 Nature of contents
 dictionaries
 http://library.link/vocab/relatedWorkOrContributorName

 Marti, Kurt.
 Ermoliev, Yuri.
 Pflug, Georg Ch.
 Series statement

 Lecture notes in economics and mathematical systems
 Lecture Notes in Economics and Mathematical Systems,
 Series volume

 532
 532
 http://library.link/vocab/subjectName

 Systems theory
 Operations research
 Mathematical optimization
 Systems Theory, Control
 Operations Research/Decision Theory
 Calculus of Variations and Optimal Control; Optimization
 Label
 Dynamic Stochastic Optimization, edited by Kurt Marti, Yuri Ermoliev, Georg Ch. Pflug, (electronic resource)
 Note
 Bibliographic Level Mode of Issuance: Monograph
 Antecedent source
 mixed
 Bibliography note
 Includes bibliographical references
 Carrier category
 online resource
 Carrier category code
 cr
 Color
 not applicable
 Content category
 text
 Content type code
 txt
 Contents
 I. Dynamic Decision Problems under Uncertainty: Modeling Aspects  Reflections on Output Analysis for Multistage Stochastic Linear Programs  Modeling Support for Multistage Recourse Problems  Optimal Solutions for Undiscounted Variance Penalized Markov Decision Chains  Approximation and Optimization for Stochastic Networks  II. Dynamic Stochastic Optimization in Finance  Optimal Stopping Problem and Investment Models  Estimating LIBOR/Swaps SpotVolatilities: the EpiVolatility Model  Structured Products for Pension Funds  III. Optimal Control Under Stochastic Uncertainty  Realtime Robust Optimal Trajectory Planning of Industrial Robots  Adaptive Optimal Stochastic Trajectory Planning and Control (AOSTPC) for Robots  IV. Tools for Dynamic Stochastic Optimization  Solving Stochastic Programming Problems by Successive Regression Approximations — Numerical Results  Stochastic Optimization of Risk Functions via Parametric Smoothing  Optimization under Uncertainty using Momentum  Perturbation Analysis of Chanceconstrained Programs under Variation of all Constraint Data  The Value of Perfect Information as a Risk Measure  New Bounds and Approximations for the Probability Distribution of the Length of the Critical Path  Simplification of Recourse Models by Modification of Recourse Data
 Dimensions
 unknown
 Edition
 1st ed. 2004.
 Extent
 1 online resource (VIII, 336 p.)
 File format
 multiple file formats
 Form of item
 online
 Isbn
 9783642558849
 Level of compression
 uncompressed
 Media category
 computer
 Media type code
 c
 Other control number
 10.1007/9783642558849
 Quality assurance targets
 absent
 Reformatting quality
 access
 Specific material designation
 remote
 System control number

 (CKB)3400000000103849
 (SSID)ssj0000805929
 (PQKBManifestationID)11496374
 (PQKBTitleCode)TC0000805929
 (PQKBWorkID)10741525
 (PQKB)11063659
 (DEHe213)9783642558849
 (MiAaPQ)EBC3071196
 (EXLCZ)993400000000103849
 Label
 Dynamic Stochastic Optimization, edited by Kurt Marti, Yuri Ermoliev, Georg Ch. Pflug, (electronic resource)
 Note
 Bibliographic Level Mode of Issuance: Monograph
 Antecedent source
 mixed
 Bibliography note
 Includes bibliographical references
 Carrier category
 online resource
 Carrier category code
 cr
 Color
 not applicable
 Content category
 text
 Content type code
 txt
 Contents
 I. Dynamic Decision Problems under Uncertainty: Modeling Aspects  Reflections on Output Analysis for Multistage Stochastic Linear Programs  Modeling Support for Multistage Recourse Problems  Optimal Solutions for Undiscounted Variance Penalized Markov Decision Chains  Approximation and Optimization for Stochastic Networks  II. Dynamic Stochastic Optimization in Finance  Optimal Stopping Problem and Investment Models  Estimating LIBOR/Swaps SpotVolatilities: the EpiVolatility Model  Structured Products for Pension Funds  III. Optimal Control Under Stochastic Uncertainty  Realtime Robust Optimal Trajectory Planning of Industrial Robots  Adaptive Optimal Stochastic Trajectory Planning and Control (AOSTPC) for Robots  IV. Tools for Dynamic Stochastic Optimization  Solving Stochastic Programming Problems by Successive Regression Approximations — Numerical Results  Stochastic Optimization of Risk Functions via Parametric Smoothing  Optimization under Uncertainty using Momentum  Perturbation Analysis of Chanceconstrained Programs under Variation of all Constraint Data  The Value of Perfect Information as a Risk Measure  New Bounds and Approximations for the Probability Distribution of the Length of the Critical Path  Simplification of Recourse Models by Modification of Recourse Data
 Dimensions
 unknown
 Edition
 1st ed. 2004.
 Extent
 1 online resource (VIII, 336 p.)
 File format
 multiple file formats
 Form of item
 online
 Isbn
 9783642558849
 Level of compression
 uncompressed
 Media category
 computer
 Media type code
 c
 Other control number
 10.1007/9783642558849
 Quality assurance targets
 absent
 Reformatting quality
 access
 Specific material designation
 remote
 System control number

 (CKB)3400000000103849
 (SSID)ssj0000805929
 (PQKBManifestationID)11496374
 (PQKBTitleCode)TC0000805929
 (PQKBWorkID)10741525
 (PQKB)11063659
 (DEHe213)9783642558849
 (MiAaPQ)EBC3071196
 (EXLCZ)993400000000103849
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<div class="citation" vocab="http://schema.org/"><i class="fa faexternallinksquare fafw"></i> Data from <span resource="http://link.libraries.ou.edu/portal/DynamicStochasticOptimizationeditedbyKurt/z4MojWdVS1M/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.libraries.ou.edu/portal/DynamicStochasticOptimizationeditedbyKurt/z4MojWdVS1M/">Dynamic Stochastic Optimization, edited by Kurt Marti, Yuri Ermoliev, Georg Ch. Pflug, (electronic resource)</a></span>  <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.libraries.ou.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.libraries.ou.edu/">University of Oklahoma Libraries</a></span></span></span></span></div>