Geodesic
A smoothing SAA method for a stochastic mathematical program with complementarity constraints
Applications of Mathematics, Tome 57 (2012) no. 5, pp. 477-502.

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A smoothing sample average approximation (SAA) method based on the log-exponential function is proposed for solving a stochastic mathematical program with complementarity constraints (SMPCC) considered by Birbil et al. (S. I. Birbil, G. Gürkan, O. Listes: Solving stochastic mathematical programs with complementarity constraints using simulation, Math. Oper. Res. 31 (2006), 739–760). It is demonstrated that, under suitable conditions, the optimal solution of the smoothed SAA problem converges almost surely to that of the true problem as the sample size tends to infinity. Moreover, under a strong second-order sufficient condition for SMPCC, the almost sure convergence of Karash-Kuhn-Tucker points of the smoothed SAA problem is established by Robinson's stability theory. Some preliminary numerical results are reported to show the efficiency of our method.
DOI : 10.1007/s10492-012-0028-5
Classification : 90C15, 90C33
Keywords: smoothing SAA method; log-exponential function; stochastic mathematical program with complementarity constraints; almost sure convergence; complementarity constraints; sample average approximation; stability analysis 
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     title = {A smoothing {SAA} method for a stochastic mathematical program with complementarity constraints},
     journal = {Applications of Mathematics},
     pages = {477--502},
     publisher = {mathdoc},
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     zbl = {1265.90223},
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Zhang, Jie; Zhang, Li-wei; Wu, Yue. A smoothing SAA method for a stochastic mathematical program with complementarity constraints. Applications of Mathematics, Tome 57 (2012) no. 5, pp. 477-502. doi : 10.1007/s10492-012-0028-5. http://geodesic.mathdoc.fr/articles/10.1007/s10492-012-0028-5/

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