Convergence Analysis of the Approximation Problems for Solving Stochastic Vector Variational Inequality Problems
Joint Authors
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-08
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
In this paper, we consider stochastic vector variational inequality problems (SVVIPs).
Because of the existence of stochastic variable, the SVVIP may have no solutions generally.
For solving this problem, we employ the regularized gap function of SVVIP to the loss function and then give a low-risk conditional value-at-risk (CVaR) model.
However, this low-risk CVaR model is difficult to solve by the general constraint optimization algorithm.
This is because the objective function is nonsmoothing function, and the objective function contains expectation, which is not easy to be computed.
By using the sample average approximation technique and smoothing function, we present the corresponding approximation problems of the low-risk CVaR model to deal with these two difficulties related to the low-risk CVaR model.
In addition, for the given approximation problems, we prove the convergence results of global optimal solutions and the convergence results of stationary points, respectively.
Finally, a numerical experiment is given.
American Psychological Association (APA)
Luo, Meiju& Zhang, Kun. 2020. Convergence Analysis of the Approximation Problems for Solving Stochastic Vector Variational Inequality Problems. Complexity،Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1139874
Modern Language Association (MLA)
Luo, Meiju& Zhang, Kun. Convergence Analysis of the Approximation Problems for Solving Stochastic Vector Variational Inequality Problems. Complexity No. 2020 (2020), pp.1-8.
https://search.emarefa.net/detail/BIM-1139874
American Medical Association (AMA)
Luo, Meiju& Zhang, Kun. Convergence Analysis of the Approximation Problems for Solving Stochastic Vector Variational Inequality Problems. Complexity. 2020. Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1139874
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139874