Dynamic Stochastic Optimization

Marti K, Ermoliev YM, & Pflug GC (2003). Dynamic Stochastic Optimization. Heidelberg: Springer-Verlag. ISBN 3-540-40506-2

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Abstract

Optimization problems arising in practice involve random parameters. For the computation of robust optimal solutions, i.e., optimal solutions being insensitive with respect to random parameter variations, deterministic substitute problems are needed. Based on the distribution of the random data, and using decision theoretical concepts, optimization problems under stochastic uncertainty are converted into deterministic substitute problems. Due to the occurring probabilities and expectations, approximative solution techniques must be applied. Deterministic and and stochastic approximation methods and their analytical properties are provided: Taylor expansion, regression and response surface methods, probability inequalities, First Order Reliability Methods, convex approximation/deterministic descent directions/efficient points, stochastic approximation methods, differentiation of probability and mean value functions. Convergence results of the resulting iterative solution procedures are given.

Item Type: Book
Research Programs: Risk, Modeling and Society (RMS)
Bibliographic Reference: Springer-Verlag, Heidelberg, Germany [2003]
Related URLs:
Depositing User: IIASA Import
Date Deposited: 15 Jan 2016 02:15
Last Modified: 10 Feb 2016 16:02
URI: http://pure.iiasa.ac.at/6959

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