Geodesic
Approximative solutions of stochastic optimization problems
Kybernetika, Tome 46 (2010) no. 3, pp. 513-523 Cet article a éte moissonné depuis la source Czech Digital Mathematics Library

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The 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.
The 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.
Classification : 60F99, 62F12, 90C15, 90C31
Keywords: the optimal solution; $\varepsilon $-minimal solutions; level-minimal solutions; randomness 
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     author = {Lachout, Petr},
     title = {Approximative solutions of stochastic optimization problems},
     journal = {Kybernetika},
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     zbl = {1229.90110},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/KYB_2010_46_3_a14/}
}
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Lachout, Petr. Approximative solutions of stochastic optimization problems. Kybernetika, Tome 46 (2010) no. 3, pp. 513-523. http://geodesic.mathdoc.fr/item/KYB_2010_46_3_a14/

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