An ergodic theorem for stochastic programming problems

被引：0

作者：

Korf, LA ^{[1
]}

Wets, RJB

机构：

[1] IBM Corp, Thomas J Watson Res Ctr, Yorktown Hts, NY 10598 USA

[2] Univ Calif Davis, Dept Math, Davis, CA 95616 USA

来源：

OPTIMIZATION | 2000年 / 48卷

关键词：

ergodic theorem; laws of large numbers; stochastic programming; stochastic programs with recourse; stochastic programs with chance constraints; random lsc functions; epi-convergence;

D O I：

暂无

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

To justify the use of sampling to solve stochastic programming problems one usually relies on a law of large numbers for random Isc (lower semicontinuous) functions when the samples come from independent, identical experiments. If the samples come from a stationary process, one can appeal to the ergodic theorem proved here. The proof relies on the 'scalarization' of random lsc functions.

引用

页码：203 / 217

页数：15