Dynamic Stochastic Optimization - Lecture Notes in Economics and Mathematical Systems Softcover reprint of the original 1st ed. 2004 edition

Marc Notes: Includes bibl. ref.; Conf. papers ... 2002. Table of 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 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."Publisher Marketing: 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." Contributor Bio:  Marti, Kurt Univ. Prof. Kurt Marti studierte Mathematik und Physik an der UniversitAt ZA1/4rich. Nach dem Diplom wechselte er an die UniversitAt Mannheim, wo er zum Dr.sc.math. promovierte. AnschlieAend war er als Wissenschaftlicher Mitarbeiter und Dozent an der UniversitAt ZA1/4rich tAtig. Es folgte ein Aufenthalt als Visiting Associate Professor an der University of Kentucky in Lexington, Ky, USA. Er wurde dann auf die Professur fA1/4r HAhere Mathematik und Operations Research an der FakultAt fA1/4r Luft- und Raumfahrttechnik der UniversitAt der Bundeswehr MA1/4nchen berufen. SpAter erfolgte der Ruf auf die Professur fA1/4r Ingenieurmathematik an derselben UniversitAt. Sein Forschungsgebiet ist die Stochastische Optimierung, wobei approximative LAsungsverfahren und insbesondere Anwendungen im Ingenieurwesen im Vordergrund stehen. Eine Aoebersicht A1/4ber die Publikationen von Prof. Marti und seiner Arbeitsgruppe auf verschiedenen Gebieten der Stochastischen Optimierung und ihren Anwendungen in der Strukturoptimierung sowie in der Robotik findet man auf der Webseite: http: //www.stoch.net. Prof. Marti ist Chairman des GAMM-Fachausschusses "Optimierung und Angewandte Stochastik" und Chairman der IFIP Working Group WG7.7 "Stochastic Optimization."