Distributionally robust expected residual minimization for stochastic variational inequality problems

• Published in: Optimization methods & software Vol. 38; no. 4; pp. 756 - 780
• Main Authors: Hori, Atsushi, Yamakawa, Yuya, Yamashita, Nobuo
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 04.07.2023; Taylor & Francis Ltd
• Subjects: distributionally robust optimization; expected residual minimization; Expected values; Numerical integration; Optimization; Random variables; Robustness (mathematics); Semidefinite programming; Stochastic variational inequality; Variance
• ISSN: 1055-6788; 1029-4937
• DOI: 10.1080/10556788.2023.2167995

The stochastic variational inequality problem (SVIP) is an equilibrium model that includes random variables and has been widely applied in various fields such as economics and engineering. Expected residual minimization (ERM) is an established model for obtaining a reasonable solution for the SVIP, and its objective function is an expected value of a suitable merit function for the SVIP. However, the ERM is restricted to the case where the distribution is known in advance. We extend the ERM to ensure the attainment of robust solutions for the SVIP under the uncertainty distribution (the extended ERM is referred to as distributionally robust expected residual minimization (DRERM), where the worst-case distribution is derived from the set of probability measures in which the expected value and variance take the same sample mean and variance, respectively). Under suitable assumptions, we demonstrate that the DRERM can be reformulated as a deterministic convex nonlinear semidefinite programming to avoid numerical integration.