Adaptive Stochastic Optimization Procedures
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 6)
Consider the following stochastic programming problem:In (3.6.1), x is a decision vector to be selected from a closed, bounded subset D 0 of the n-dimensional real Euclidean space ℝ n ; y is a q-dimensional vector valued random variable; f i , i = 0, 1,...,I, are respectively defined measurable functions; E is the symbol of mathematical expectation (the expected values are supposed to exist). Note that (3.6.1) is a fairly general stochastic programming model form; it encompasses—under suitable transformations—the ‘model block’ and types discussed in the previous chapter.
KeywordsStochastic Optimization Search Point Global Optimization Problem Stochastic Algorithm Implementation Aspect
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Unable to display preview. Download preview PDF.
© Springer Science+Business Media Dordrecht 1996