Infeasible interior-point algorithms based on sampling average approximations for a class of stochastic complementarity problems and their applications-Reference-Cited by-同舟云学术

Infeasible interior-point algorithms based on sampling average approximations for a class of stochastic complementarity problems and their applications

Published:2019-05 Issue: Volume:352 Page:382-400
ISSN:0377-0427
Container-title:Journal of Computational and Applied Mathematics
language:en
Short-container-title:Journal of Computational and Applied Mathematics

Author:

Yang Zhen-Ping,Zhang Jin,Zhu Xide,Lin Gui-Hua

Funder

National Nature Science Foundation of China (NSFC)

Ministry of Education of China

Publisher

Elsevier BV

Subject

Applied Mathematics,Computational Mathematics

Reference41 articles.

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4. A superlinear infeasible interior-point algorithm for monotone complementarity problems;Wright;Math. Oper. Res.,1996

5. An infeasible path-following method for monotone complementarity problems;Tseng;SIAM J. Optim.,1997

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