Stochastic Nonlinear Complementarity Problems: Stochastic Programming Reformulation and Penalty-Based Approximation Method

M. Wang () and M. M. Ali ()
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M. Wang: Dalian University of Technology
M. M. Ali: University of the Witwatersrand

Journal of Optimization Theory and Applications, 2010, vol. 144, issue 3, No 10, 597-614

Abstract: Abstract We consider a class of stochastic nonlinear complementarity problems. We first reformulate the stochastic complementarity problem as a stochastic programming model. Based on the reformulation, we then propose a penalty-based sample average approximation method and prove its convergence. Finally, we report on some numerical test results to show the efficiency of our method.

Keywords: Stochastic nonlinear complementarity problems; Stochastic programming; Sample average approximation; Penalty method; Convergence (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1007/s10957-009-9606-4

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