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• W3133808553 abstract "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." @default.
• W3133808553 created "2021-03-15" @default.
• W3133808553 creator A5015581947 @default.
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• W3133808553 date "2022-06-01" @default.
• W3133808553 modified "2023-10-12" @default.
• W3133808553 title "General Feasibility Bounds for Sample Average Approximation via Vapnik--Chervonenkis Dimension" @default.
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• W3133808553 doi "https://doi.org/10.1137/21m140211x" @default.
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