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
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© Springer Science+Business Media Dordrecht 1996