Dynamic stochastic optimization
著者
書誌事項
Dynamic stochastic optimization
(Lecture notes in economics and mathematical systems, 532)
Springer, c2004
大学図書館所蔵 全56件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references
内容説明・目次
内容説明
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.
目次
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.
「Nielsen BookData」 より