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July  2021, 17(4): 1863-1886. doi: 10.3934/jimo.2020050

## Convergence analysis of a smoothing SAA method for a stochastic mathematical program with second-order cone complementarity constraints

 1 School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China 2 City Institute, Dalian University of Technology, Dalian 116600, China 3 Key Laboratory of Operations Research and Control of Universities in Fujian, College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China 4 School of Mathematics, Liaoning Normal University, Dalian 116029, China

* Corresponding author: Bo Wang

Received  May 2019 Revised  October 2019 Published  March 2020

Fund Project: The second author's research is supported in part by the National Natural Science Foundation of China under Project No. 11701091, and Fujian Education and Research Program for Young Teachers under Project No. JAT170096. The third author's research is supported by the National Natural Science Foundation of China under Project No. 11671183 and No. 11671184, Program for Liaoning Excellent Talents in University under Project No. LR2017049, Scientific Research Fund of Liaoning Provincial Education Department under Project No. L201783638, Liaoning BaiQianWan Talents Program, and Project of Liaoning Provincial Natural Science Foundation of China No. 2019MS-217

A stochastic mathematical program model with second-order cone complementarity constraints (SSOCMPCC) is introduced in this paper. It can be considered as a non-trivial extension of stochastic mathematical program with complementarity constraints, and could arise from a hard-to-handle class of bilivel second-order cone programming and inverse stochastic second-order cone programming. By introducing the Chen-Harker-Kanzow-Smale (CHKS) type function to replace the projection operator onto the second-order cone, a smoothing sample average approximation (SAA) method is proposed for solving the SSOCMPCC problem. It can be shown that with proper assumptions, as the sample size goes to infinity, any cluster point of global solutions of the smoothing SAA problem is a global solution of SSOCMPCC almost surely, and any cluster point of stationary points of the former problem is a C-stationary point of the latter problem almost surely. C-stationarity can be strengthened to M-stationarity with additional assumptions. Finally, we report a simple illustrative numerical test to demonstrate our theoretical results.

Citation: Li Chu, Bo Wang, Jie Zhang, Hong-Wei Zhang. Convergence analysis of a smoothing SAA method for a stochastic mathematical program with second-order cone complementarity constraints. Journal of Industrial & Management Optimization, 2021, 17 (4) : 1863-1886. doi: 10.3934/jimo.2020050
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##### References:
Numerical result for Problem (22)
 N $\bar{f}$ $\bar{\varepsilon}_u$ $\bar{\varepsilon}_v$ infea time(s) 1000 1.53 8.88E-02 4.07E-02 5.43E-06 0.02 10000 1.49 5.14E-02 2.82E-02 3.97E-05 0.02 100000 1.54 5.74E-02 6.77E-02 4.74E-03 0.02 1000000 1.44 3.74E-04 5.22E-04 6.23E-06 0.13 10000000 1.44 1.82E-04 1.89E-04 7.35E-06 1.23
 N $\bar{f}$ $\bar{\varepsilon}_u$ $\bar{\varepsilon}_v$ infea time(s) 1000 1.53 8.88E-02 4.07E-02 5.43E-06 0.02 10000 1.49 5.14E-02 2.82E-02 3.97E-05 0.02 100000 1.54 5.74E-02 6.77E-02 4.74E-03 0.02 1000000 1.44 3.74E-04 5.22E-04 6.23E-06 0.13 10000000 1.44 1.82E-04 1.89E-04 7.35E-06 1.23
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