  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
Vapnik-Chervonenkis (VC) dimension and 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.
