Approximation and Solution Schemes for Stochastic Dynamic Optimization Problems

eBook (PDF), 117 Pages
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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.
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Hot Diggity Books
September 28, 2011
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1.59 MB
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