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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.

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.

정보제공 :

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 Spot-Volatilities: the EpiVolatility Model.- Structured Products for Pension Funds.- III. Optimal Control Under Stochastic Uncertainty.- Real-time 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 Chance-constrained 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.

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