Kybernetika 46 no. 3, 513-523, 2010

Approximative solutions of stochastic optimization problems

Petr Lachout


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 optimal solution, $\epsilon$-minimal solutions, level-minimal solutions, randomness


90C31, 62F12, 60F99