A Class of Smoothing Modulus-Based Iterative Methods for Solving the Stochastic Mixed Complementarity Problems

被引：0

作者：

Guo, Cong ^{[1
]}

Liu, Yingling ^{[2
]}

Li, Chenliang ^{[1
]}

机构：

[1] Guilin Univ Elect Technol, Sch Math & Comp Sci, Guilin 541004, Peoples R China

[2] Guilin Inst Informat Technol, Dept Math Teaching & Res, Guilin 541004, Peoples R China

来源：

SYMMETRY-BASEL | 2023年 / 15卷 / 01期

关键词：

smoothing modulus-based iterative method; stochastic mixed complementarity problems; expected value model; Newton method; SAMPLE AVERAGE APPROXIMATION; EQUILIBRIUM PROBLEMS; TRAFFIC EQUILIBRIUM;

D O I：

10.3390/sym15010229

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

In this paper, we present a smoothing modulus-based iterative method for solving the stochastic mixed complementarity problems (SMCP). The main idea is that we firstly transform the expected value model of SMCP into an equivalent nonsmooth system of equations, then obtain an approximation smooth system of equations by using a smoothing function, and finally solve it by the Newton method. We give the convergence analysis, and the numerical results show the effectiveness of the new method for solving the SMCP with symmetry coefficient matrices.

引用

页数：11

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