On the unconstrained optimization reformulations for a class of stochastic vector variational inequality problems

Author:

Dong Dan-dan,Tang Guo-ji,Qiu Hui-ming

Abstract

AbstractIn this paper, a class of stochastic vector variational inequality (SVVI) problems are considered. By employing the idea of a D-gap function, the SVVI problem is reformulated as a deterministic model, which is an unconstrained expected residual minimization (UERM) problem, while it is reformulated as a constrained expected residual minimization problem in the work of Zhao et al. Then, the properties of the objective function are investigated and a sample average approximation approach is proposed for solving the UERM problem. Convergence of the proposed approach for global optimal solutions and stationary points is analyzed. Moreover, we consider another deterministic formulation, i.e., the expected value (EV) formulation for an SVVI problem, and the global error bound of a D-gap function based on the EV formulation is given.

Funder

Innovation Project of Guangxi Graduate Education

National Natural Science Foundation of China

Natural Science Foundation of Guangxi Zhuang Autonomous Region

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Discrete Mathematics and Combinatorics,Analysis

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