Overview

This volume considers optimal stochastic decision processes from the viewpoint of stochastic programming. It focuses on theoretical properties and on approximate or numerical solution techniques for time-dependent optimization problems with random parameters (multistage stochastic programs, optimal stochastic decision processes). Methods for finding approximate solutions of probabilistic and expected cost based deterministic substitute problems are presented. Besides theoretical and numerical considerations, the proceedings volume contains selected refereed papers on many practical applications to economics and engineering: risk, risk management, portfolio management, finance, insurance-matters and control of robots.

Full Product Details

Author:   Kurt Marti ,  Yuri Ermoliev ,  Georg Ch. Pflug
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   Softcover reprint of the original 1st ed. 2004
Volume:   532
Dimensions:   Width: 15.50cm , Height: 1.80cm , Length: 23.50cm
Weight:   1.080kg
ISBN:  

9783540405061

ISBN 10:   3540405062
Pages:   336
Publication Date:   29 October 2003
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print  
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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.

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