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A remark on multiobjective stochastic optimization via strongly convex functions

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    0450553 - ÚTIA 2017 RIV DE eng J - Článek v odborném periodiku
    Kaňková, Vlasta
    A remark on multiobjective stochastic optimization via strongly convex functions.
    Central European Journal of Operations Research. Roč. 24, č. 2 (2016), s. 309-333. ISSN 1435-246X. E-ISSN 1613-9178
    Grant CEP: GA ČR GA13-14445S
    Institucionální podpora: RVO:67985556
    Klíčová slova: Stochasticmultiobjective optimization problem * Efficient solution * Wasserstein metric and L_1 norm * Stability and empirical estimates
    Kód oboru RIV: BB - Aplikovaná statistika, operační výzkum
    Impakt faktor: 0.659, rok: 2016
    http://library.utia.cas.cz/separaty/2015/E/kankova-0450553.pdf

    Many economic and financial applications lead (from the mathematical point of view) to deterministic optimization problems depending on a probability measure. These problems can be static (one stage), dynamic with finite (multistage) or infinite horizon, single objective or multiobjective. We focus on one-stage case in multiobjective setting. Evidently, well known results from the deterministic optimization theory can be employed in the case when the "underlying" probability measure is completely known. The assumption of a complete knowledge of the probability measure is fulfilled very seldom. Consequently, we have mostly to analyze the mathematical models on the data base to obtain a stochastic estimate of the corresponding "theoretical" characteristics. However, the investigation of these estimates has been done mostly in one-objective case. In this paper we focus on the investigation of the relationship between "characteristics" obtained on the base of complete knowledge of the probability measure and estimates obtained on the (above mentioned) data base, mostly in the multiobjective case.
    Trvalý link: http://hdl.handle.net/11104/0252048

     
     
Počet záznamů: 1  

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