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Approximative solutions of stochastic optimization problems. (English) Zbl 1229.90110
Author’s abstract: “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.”

MSC:
90C15 Stochastic programming
62F12 Asymptotic properties of parametric estimators
60F99 Limit theorems in probability theory
90C31 Sensitivity, stability, parametric optimization
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