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

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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.

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Authors and Affiliations

  1. School of Management, Dalian University of Technology, Dalian, 116024, China

    M. Wang

  2. School of Computational and Applied Mathematics, University of the Witwatersrand, Wits 2050, Johannesburg, South Africa

    M. Wang & M. M. Ali

Authors
  1. M. Wang
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  2. M. M. Ali
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Corresponding author

Correspondence to M. Wang.

Additional information

Communicated by Masao Fukushima.

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Wang, M., Ali, M.M. Stochastic Nonlinear Complementarity Problems: Stochastic Programming Reformulation and Penalty-Based Approximation Method. J Optim Theory Appl 144, 597–614 (2010). https://doi.org/10.1007/s10957-009-9606-4

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  • DOI: https://doi.org/10.1007/s10957-009-9606-4

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