Nonlinear Decision Rules Made Scalable by Nonparametric Liftings

Eojin Han () and Omid Nohadani ()
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Eojin Han: Mendoza College of Business, University of Notre Dame, Notre Dame, Indiana 46556
Omid Nohadani: Artificial Intelligence and Data Science, Benefits Science Technologies, Boston, Massachusetts 02108

Management Science, 2025, vol. 71, issue 4, 3449-3471

Abstract: Sequential decision making often requires dynamic policies, which are computationally not tractable in general. Decision rules provide approximate solutions by restricting decisions to simple functions of uncertainties. In this paper, we consider a nonparametric lifting framework where the uncertainty space is lifted to higher dimensions to obtain nonlinear decision rules. Current lifting-based approaches require predetermined functions and are parametric. We propose two nonparametric liftings, which derive the nonlinear functions by leveraging the uncertainty set structure and problem coefficients. Both methods integrate the benefits from lifting and nonparametric approaches, and hence provide scalable decision rules with performance bounds. More specifically, the set-driven lifting is constructed by finding polyhedrons within uncertainty sets, inducing piecewise-linear decision rules with performance bounds. The dynamics-driven lifting, on the other hand, is constructed by extracting geometric information and accounting for problem coefficients. This is achieved by using linear decision rules of the original problem, also enabling one to quantify lower bounds of objective improvements over linear decision rules. Numerical comparisons with competing methods demonstrate superior computational scalability and comparable performance in objectives. These observations are magnified in multistage problems with extended time horizons, suggesting practical applicability of the proposed nonparametric liftings in large-scale dynamic robust optimization.

Keywords: dynamic optimization; robust optimization; decision rules; lifting (search for similar items in EconPapers)
Date: 2025
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