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Adaptive Stochastic Optimization Procedures

  • János D. Pintér
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 6)

Abstract

Consider the following stochastic programming problem:
(3.6.1)
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 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.

Keywords

Stochastic 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.

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Copyright information

© Springer Science+Business Media Dordrecht 1996

Authors and Affiliations

  • János D. Pintér
    • 1
  1. 1.Pintér Consulting ServicesDalhousie UniversityCanada

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