Dynamic Stochastic Optimization (Lecture Notes in Economics and Mathematical Systems) pdf epub mobi txt 电子书 下载 2025
- 数理经济学
- Optimization
- Stochastic Optimization
- Dynamic Programming
- Mathematical Economics
- Mathematical Systems
- Optimization Theory
- Control Theory
- Stochastic Control
- Economic Modeling
- Lecture Notes
- Applied Mathematics
具体描述
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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目录信息
Front Matter....Pages 1-1
Reflections on Output Analysis for Multistage Stochastic Linear Programs....Pages 3-20
Modeling Support for Multistage Recourse Problems....Pages 21-41
Optimal Solutions for Undiscounted Variance Penalized Markov Decision Chains....Pages 43-66
Approximation and Optimization for Stochastic Networks....Pages 67-79
Front Matter....Pages 81-81
Optimal Stopping Problem and Investment Models....Pages 83-98
Estimating LIBOR/Swaps Spot-Volatilities: the EpiVolatility Model....Pages 99-114
Structured Products for Pension Funds....Pages 115-130
Front Matter....Pages 131-131
Real-time Robust Optimal Trajectory Planning of Industrial Robots....Pages 133-154
Adaptive Optimal Stochastic Trajectory Planning and Control (AOSTPC) for Robots....Pages 155-206
Front Matter....Pages 207-207
Solving Stochastic Programming Problems by Successive Regression Approximations — Numerical Results....Pages 209-224
Stochastic Optimization of Risk Functions via Parametric Smoothing....Pages 225-247
Optimization under Uncertainty using Momentum....Pages 249-256
Perturbation Analysis of Chance-constrained Programs under Variation of all Constraint Data....Pages 257-274
The Value of Perfect Information as a Risk Measure....Pages 275-291
New Bounds and Approximations for the Probability Distribution of the Length of the Critical Path....Pages 293-320
Simplification of Recourse Models by Modification of Recourse Data....Pages 321-336
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