Scenario-based Regularization: A Tractable Framework for Distributionally Robust Stochastic Optimization

TL;DR

This paper introduces a scenario-based regularized framework for stochastic optimization that offers a computationally efficient and targeted robust alternative to Wasserstein DRO, with proven theoretical guarantees and practical benefits in inventory and portfolio problems.

Contribution

It develops a tractable regularization method inspired by WDRO's asymptotic expansion, enabling targeted robustness and theoretical analysis with finite sample guarantees.

Findings

Effective in multi-product newsvendor problem as an alternative to NP-hard WDRO.

Improves out-of-sample performance in portfolio optimization using crisis data.

Provides finite sample guarantees and asymptotic consistency.

Abstract

We propose a flexible scenario-based regularized Sample Average Approximation (SBR-SAA) framework for stochastic optimization. This work is motivated by challenges in standard Wasserstein Distributionally Robust Optimization (WDRO), where out-of-sample performance, particularly tail risk, is sensitive to the choice of the p-norm, and formulations can be computationally intractable. Our method is inspired by the asymptotic expansion of the WDRO objective and introduces a regularizer that penalizes the (sub)gradient norm of the objective at a selected set of scenarios. This framework serves a dual purpose: (i) it provides a computationally tractable alternative to WDRO by using a representative subset of the data, and (ii) it can provide targeted robustness by incorporating user-defined adverse scenarios. We establish the theoretical properties of this framework by proving its equivalence to a decision-dependent WDRO problem, from which we derive finite sample guarantees and asymptotic consistency. We demonstrate the method's efficacy in two applications: (1) a multi-product newsvendor problem, where SBR-SAA serves as a tractable alternative to NP-hard WDRO, and (2) a mean-risk portfolio optimization problem, where it successfully uses historical crisis data to improve out-of-sample performance.