A smooth penalty-based sample average approximation method for stochastic complementarity problems

Sample average approximation method is one of the effective methods in the stochastic optimization. A smooth penalty-based sample average approximation method for stochastic nonlinear complementarity problems is presented in this paper. Based on a smooth penalty function, a reformulation is proposed...

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Bibliographic Details
Published inJournal of computational and applied mathematics Vol. 287; pp. 20 - 31
Main Authors He, Suxiang, Wei, Min, Tong, Hengqing
Format Journal Article
LanguageEnglish
Published Elsevier B.V 15.10.2015
Subjects
Approximation
Computational efficiency
Convergence
Equivalence
Mathematical analysis
Mathematical models
Optimization
Penalty function
Sample average approximation method
Stationary point
Stochastic complementarity problems
Stochasticity
Penalty function
90C30
Stationary point
90C15
Stochastic complementarity problems
Sample average approximation method
Convergence
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ISSN0377-0427
1879-1778
DOI10.1016/j.cam.2015.03.017

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Summary:Sample average approximation method is one of the effective methods in the stochastic optimization. A smooth penalty-based sample average approximation method for stochastic nonlinear complementarity problems is presented in this paper. Based on a smooth penalty function, a reformulation is proposed for the equivalent problem of EV formulation for stochastic complementary problems and it is proven that its solutions are existent under some mild assumptions. An implementable sample average approximation method for the reformulation is further established and its convergence is analyzed. The numerical results for some test examples are reported at last to show efficiency of the proposed method.
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ISSN:0377-0427
1879-1778
DOI:10.1016/j.cam.2015.03.017