Journal of Inequalities and Applications· 2026Q2
A weighted expected residual minimization method with sample average approximation for stochastic vector variational inequalities
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- Q2SCImago
- 2026year
Short summary
A novel weighted expected residual minimization (WERM) method, incorporating both LAD and LS residuals, is proposed to solve stochastic vector variational inequalities (SVVI).
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Key points
- Proposes a weighted expected residual minimization (WERM) method for stochastic vector variational inequalities (SVVI).
- Incorporates a regularized gap function using convex combinations of LAD and LS residuals.
- Develops a sample average approximation (SAA) technique for solving the WERM problem.
- Provides theoretical analysis of core properties (differentiability, error bounds) and SAA convergence.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract In this paper, we propose a novel solution approach for a specific class of stochastic vector variational inequalities (SVVI). Our method adopts a weighted expected residual framework that includes a regularized gap function, which incorporates convex combinations of residuals derived from both the least absolute deviation (LAD) and least squares (LS) methods. We conduct a detailed analysis of the core properties of the resulting weighted expected residual minimization (WERM) problem, including differentiability, level set boundedness, global error bounds, and solution robustness. To enhance its practical utility, we further develop a sample average approximation (SAA) technique for solving the WERM problem, and provide a comprehensive convergence analysis of this approximation scheme. Finally, a series of numerical experiments are carried out to validate the theoretical results of this study and demonstrate the effectiveness of the proposed approach.
The authors' abstract, as published at the source. Journal of Inequalities and Applications, 2026 · DOI ↗
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Field: Management Science and Operations Research
Management Science and Operations ResearchDecision Sciences