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Global Optimization Algorithms as Statistical Decision Procedures — The Information Approach

  • Roman G. Strongin
  • Yaroslav D. Sergeyev
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 45)

Abstract

Coming back to optimization problems of the type (1.1.8), i.e.,
(2.1.1)
, let us accept that the sought approximation to the global optimizer x* is x* N provided by the uniform grid technique (1.1.13)–(1.1.15) for some specified number N of trials. This assumption, which is quite natural due to the relation (1.1.17), reduces the continuous problem (2.1.1) to the discrete problem of finding the node x α of the uniform grid
(2.1.2)
, satisfying the inequalities
(2.1.3)
, where
(2.1.4)
.

Keywords

Global Optimizer Decision Rule Global Minimizer Conditional Density Residual Function 
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 2000

Authors and Affiliations

  • Roman G. Strongin
    • 1
  • Yaroslav D. Sergeyev
    • 1
    • 2
  1. 1.Nizhni Novgorod State UniversityNizhni NovgorodRussia
  2. 2.Institute of Systems Analysis and Information TechnologyUniversity of CalabriaRendeItaly

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