Nonasymptotic Upper Estimates for Errors of the Sample Average Approximation Method to Solve Risk-Averse Stochastic Programs

.We study statistical properties of the optimal value of the sample average approximation (SAA). The focus is on the tail function of the absolute error induced by the SAA, deriving upper estimates of its outcomes dependent on the sample size. The estimates allow to conclude immediately convergence rates for the optimal value of the SAA. As a crucial point, the investigations are based on new types of conditions from the theory of empirical processes which do not rely on pathwise analytical properties of the goal functions. In particular, continuity in the parameter is not imposed in advance as often in the literature on the SAA method. It is also shown that the new condition is satisfied if the paths of the goal functions are Hölder continuous so that the main results carry over in this case. Moreover, the main results are applied to goal functions whose paths are piecewise Hölder continuous as, e.g., in two-stage mixed-integer programs. The main results are shown for classical risk-neutral stochastic programs, but we also demonstrate how to apply them to the sample average approximation of risk-averse stochastic programs. In this respect, we consider stochastic programs expressed in terms of mean upper semideviations and divergence risk measures.Keywordsrisk-averse stochastic programssample average approximationmean upper semideviationsdivergence risk measuresTalagrand's inequalitiescovering numbersVC-subgraph classesMSC codes90C1560E1560B12