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Approximation and Solution Schemes for Stochastic Dynamic Optimization Problems

ByLisa A. Korf

Optimization and control problems often need to be formulated in a way that takes the uncertainty of the future into account in order to accurately reflect a "good" decision that can stand up to a variety of possible future outcomes. One way of including uncertainty in such problems treats the uncertain parameters as a random vector with an underlying probability distribution. Doing this creates a stochastic programming model which is inherently infinite dimensional, or at best extremely large, in particular when many time stages are present. In order to solve such problems, a good approximation framework is needed that encompasses various approaches such as sampling and analytical methods for various problem classes. Complementing this should be a development of solution procedures that exploit a problem's structure, for example taking advantage of convexity and decomposability wherever possible. This dissertation addresses these key issues in four parts.

Details

Publication Date
Sep 28, 2011
Language
English
Category
Engineering
Copyright
All Rights Reserved - Standard Copyright License
Contributors
By (author): Lisa A. Korf

Specifications

Format
PDF

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