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This is a Quantitative Study study.

September 29, 2025Open Access

Moment Relaxations for Data-Driven Wasserstein Distributionally Robust Optimization

SZShixuan ZhangShandong Normal University SZSuhan ZhongUniversity of California, San Diego

Key Points

Moment relaxations provide better solutions in distributionally robust optimization problems.
Asymptotic consistency conditions are identified for single-stage and two-stage optimization.
Numerical experiments validate the applicability of the proposed relaxations in practical scenarios.
Examples demonstrate the necessity of these conditions in optimization contexts.

Abstract

We propose moment relaxations for data-driven Wasserstein distributionally robust optimization problems. Conditions are identified to ensure asymptotic consistency of such relaxations for both single-stage and two-stage problems, together with examples that illustrate their necessity. Numerical experiments are also included to illustrate the proposed relaxations.

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Cite This Study

Zhang et al. (Sun,) studied this question.

synapsesocial.com/papers/68da58e0c1728099cfd118ef https://doi.org/https://doi.org/10.48550/arxiv.2505.19278