When can we improve on sample average approximation for stochastic optimization?

被引：2

作者：

Anderson, Edward ^{[1
,2
]}

Nguyen, Harrison ^{[2
]}

机构：

[1] Imperial Coll London, London SW7 2BU, England

[2] Univ Sydney, Sydney, NSW 2006, Australia

来源：

OPERATIONS RESEARCH LETTERS | 2020年 / 48卷 / 05期

关键词：

Stochastic optimization; Sample average approximation; Maximum likelihood estimation; Bagging; Kernel density estimation;

D O I：

10.1016/j.orl.2020.05.016

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

We explore the performance of sample average approximation in comparison with several other methods for stochastic optimization. The methods we evaluate are (a) bagging; (b) kernel density estimation; (c) maximum likelihood estimation; and (d) a Bayesian approach. We use two test sets: first a set of quadratic objective functions allowing different types of interaction between the random component and the univariate decision variable; and second a set of portfolio optimization problems. We make recommendations for effective approaches. (C) 2020 Elsevier B.V. All rights reserved.

引用

页码：566 / 572

页数：7

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