  We investigate the feasibility of sample average approximation (SAA) for
general stochastic optimization problems, including two-stage stochastic
programming without the relatively complete recourse assumption. Instead of
analyzing problems with specific structures, we utilize results from the
\textit{Vapnik-Chervonenkis} (VC) dimension and \textit{Probably Approximately
Correct} learning to provide a general framework that offers explicit
feasibility bounds for SAA solutions under minimal structural or distributional
assumption. We show that, as long as the hypothesis class formed by the
feasbible region has a finite VC dimension, the infeasibility of SAA solutions
decreases exponentially with computable rates and explicitly identifiable
accompanying constants. We demonstrate how our bounds apply more generally and
competitively compared to existing results.
