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A Smoothing Penalized Sample Average Approximation Method For Stochastic Programs With Second-Order Stochastic Dominance Constraints

Author

Listed:
  • HAILIN SUN

    (Department of Mathematics, Harbin Institute of Technology, Harbin, 150001, China)

  • HUIFU XU

    (School of Engineering and Mathematical Sciences, City University of London, London EC1V OHB, UK)

  • YONG WANG

    (Department of Mathematics, Harbin Institute of Technology, Harbin, 150001, China)

Abstract

In this paper, we propose a smoothing penalized sample average approximation (SAA) method for solving a stochastic minimization problem with second-order dominance constraints. The basic idea is to use sample average to approximate the expected values of the underlying random functions and then reformulate the discretized problem as an ordinary nonlinear programming problem with finite number of constraints. An exact penalty function method is proposed to deal with the latter and an elementary smoothing technique is used to tackle the nonsmoothness of the plus function and the exact penalty function. We investigate the convergence of the optimal value obtained from solving the smoothed penalized sample average approximation problem as sample size increases and show that with probability approaching to one at exponential rate with the increase of sample size the optimal value converges to its true counterpart. Some preliminary numerical results are reported.

Suggested Citation

  • Hailin Sun & Huifu Xu & Yong Wang, 2013. "A Smoothing Penalized Sample Average Approximation Method For Stochastic Programs With Second-Order Stochastic Dominance Constraints," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 30(03), pages 1-25.
    • Handle: RePEc:wsi:apjorx:v:30:y:2013:i:03:n:s0217595913400022
    DOI: 10.1142/S0217595913400022
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    References listed on IDEAS

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    1. Wenyu Sun & Ya-Xiang Yuan, 2006. "Optimization Theory and Methods," Springer Optimization and Its Applications, Springer, number 978-0-387-24976-6, September.
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