Technical Note—A Data-Driven Approach to Beating SAA Out of Sample
Jun-ya Gotoh, Michael Jong Kim, Andrew E. B. Lim
Operations Research · 2025 · Volume 73 · Issue 2
Abstract
Whereas solutions of distributionally robust optimization (DRO) problems can sometimes have a higher out-of-sample expected reward than the sample average approximation (SAA), there is no guarantee. In this paper, we introduce a class of distributionally optimistic optimization (DOO) models and show that it is always possible to “beat” SAA out-of-sample if we consider not just worst case (DRO) models but also best case (DOO) ones. We also show, however, that this comes at a cost: optimistic solutions are more sensitive to model error than either worst case or SAA optimizers and, hence, are less robust, and calibrating the worst or best case model to outperform SAA may be difficult when data are limited. Funding: J. Gotoh is supported in part by the Japan Society for the Promotion of Science [Grant 20H00285]. M. J. Kim is supported in part by the Natural Sciences and Engineering Research Council of Canada [Discovery Grant RGPIN-2015-04019]. A. E. B. Lim is supported by the Ministry of Education, Singapore, under its 2021 Academic Research Fund Tier 2 grant call [Grant MOE-T2EP20121-0014]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2021.0393 .
Keywords
Optimization, distributionally optimistic optimization, distributionally robust optimization, sample average approximation, data-driven optimization, model uncertainty, worst case sensitivity, out-of-sample performance
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