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Approximative solutions of stochastic optimization problem

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    SYSNO ASEP0348335
    Document TypeJ - Journal Article
    R&D Document TypeJournal Article
    Subsidiary JČlánek ve WOS
    TitleApproximative solutions of stochastic optimization problem
    Author(s) Lachout, Petr (UTIA-B)
    Source TitleKybernetika. - : Ústav teorie informace a automatizace AV ČR, v. v. i. - ISSN 0023-5954
    Roč. 46, č. 3 (2010), s. 513-523
    Number of pages11 s.
    Languageeng - English
    CountryCZ - Czech Republic
    KeywordsStochastic optimization problem ; sensitivity ; approximative solution
    Subject RIVBA - General Mathematics
    R&D ProjectsGA201/08/0539 GA ČR - Czech Science Foundation (CSF)
    CEZAV0Z10750506 - UTIA-B (2005-2011)
    UT WOS000280425000015
    AnnotationThe aim of this paper is to present some ideas how to relax the notion of the optimal solution of the stochastic optimization problem. In the deterministic case, $/varepsilon $-minimal solutions and level-minimal solutions are considered as desired relaxations. We call them approximative solutions and we introduce some possibilities how to combine them with randomness. Relations among random versions of approximative solutions and their consistency are presented in this paper. No measurability is assumed, therefore, treatment convenient for nonmeasurable objects is employed.
    WorkplaceInstitute of Information Theory and Automation
    ContactMarkéta Votavová,, Tel.: 266 052 201.
    Year of Publishing2011
Number of the records: 1