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Approximation and Solution Schemes for Stochastic Dynamic Optimization Problems

eBook (PDF), 117 Pages
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Price: $6.99
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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Product Details

Publisher
Hot Diggity Books
Published
September 28, 2011
Language
English
Pages
117
File Format
PDF
File Size
1.59 MB

Formats for this Ebook

PDF
Required Software Any PDF Reader, Apple Preview
Supported Devices Windows PC/PocketPC, Mac OS, Linux OS, Apple iPhone/iPod Touch... (See More)
# of Devices Unlimited
Flowing Text / Pages Pages
Printable? Yes
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