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
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
收藏
页码:566 / 572
页数:7
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