Work

Stochastic tensor complementarity problem: CVaR-ERM model and the convergence analysis of stationary points for its approximation problem

Year: 2025

Type: article

Abstract: We focus on the expected residual minimization model with conditional value-at-risk constraints (CVaR-ERM Model) for solving the stochastic tensor complementarity problem (STCP). Our previous work (Zhang, Luo, Nguyen, in: J. Nonlinear Convex Anal. 26:61–76, 2025) explored the convergence of global optimal solutions of the approximate problem of the CVaR-ERM model. However, in some practical situations, both the proposed model and its approximate problem exhibit non-convex properties, which significantly raises the probability of obtaining stationary points during the solution process. Therefore, in this paper, we first formulate the approximate problem of the CVaR-ERM model using the smoothing method, the penalty function method, and the sample average approximation method. Subsequently, we prove the convergence of the stationary points of the proposed approximate problem.

Source: Journal of Inequalities and Applications

DOI: 10.1186/s13660-025-03405-4

Authors: Meiju Luo, Yue Zhang

Institutions: Liaoning University, Shanghai Normal University

Language: English

Topic: Optimization and Variational Analysis

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,190

Funder: Liaoning University