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求解随机非线性互补问题的一种光滑化样本均值逼近方法
A Class of Smoothing SAA Methods for A Stochastic Nonlinear Complementarity Problem
【摘要】 提出一种基于光滑Fischer-Burmeister函数的光滑化样本均值逼近方法,并用该方法求解随机非线性互补问题,在适当的条件下,证明了光滑化SAA问题的最优解几乎处处指数收敛到真问题的最优解.算例的数值计算结果验证了算法的合理性和有效性.
【Abstract】 A smoothing sample average approximation method based on a version of Fischer-Burmeister smoothing functions is proposed in this paper.It is used to solve the stochastic nonlinear complementarity problem.Under suitable conditions,the almost sure convergence and exponential rate of this method is proved.The numerical test results indicate the efficiency of our method.
【关键词】 随机非线性互补问题;
光滑Fischer-Burmeister函数;
样本均值逼近;
收敛性;
【Key words】 stochastic nonlinear complementarity problem; smoothing Fischer-Burmeister function; sample average approximation; convergence;
【Key words】 stochastic nonlinear complementarity problem; smoothing Fischer-Burmeister function; sample average approximation; convergence;
【基金】 呼伦贝尔学院青年项目(YJQN2C201203)
- 【文献出处】 内蒙古师范大学学报(自然科学汉文版) ,Journal of Inner Mongolia Normal University(Natural Science Edition) , 编辑部邮箱 ,2015年01期
- 【分类号】O221.2
- 【被引频次】1
- 【下载频次】62