Work

A data-driven approach to beating SAA out-of-sample

Year: 2021

Type: preprint

Abstract: While 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 is limited.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2105.12342

Authors: Gotoh, Jun-ya, Kim, Michael Jong, Lim, Andrew E. B.

Institutions: Chuo University, University of British Columbia, National University of Singapore, Institute of Operations Research and Analytics

Language: English

Topic: Risk and Portfolio Optimization

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W4287168402

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