## Identifying Effective Scenarios for Sample Average Approximation. (arXiv:1904.01550v1 [math.OC])

We introduce a method to improve the tractability of the well-known Sample Average Approximation (SAA) without compromising important theoretical properties, such as convergence in probability and the consistency of an independent and identically distributed (iid) sample. We consider each scenario as a polyhedron of the mix of first-stage and second-stage decision variables. According to John's theorem, the Lowner-John ellipsoid of each polyhedron will be unique which means that different scenarios will have correspondingly different Lowner-John ellipsoids. By optimizing the objective function regarding both feasible regions of the polyhedron and its unique Lowner-John ellipsoid, respectively, we obtain a pair of optimal values, which would be a coordinate on a two-dimensional plane. The scenarios, whose coordinates are close enough on the plane, will be treated as one scenario; thus our method reduces the sample size of an iid sample considerably. Instead of using a large iid sample d查看全文

## Solidot 文章翻译

 你的名字 留空匿名提交 你的Email或网站 用户可以联系你 标题 简单描述 内容 We introduce a method to improve the tractability of the well-known Sample Average Approximation (SAA) without compromising important theoretical properties, such as convergence in probability and the consistency of an independent and identically distributed (iid) sample. We consider each scenario as a polyhedron of the mix of first-stage and second-stage decision variables. According to John's theorem, the Lowner-John ellipsoid of each polyhedron will be unique which means that different scenarios will have correspondingly different Lowner-John ellipsoids. By optimizing the objective function regarding both feasible regions of the polyhedron and its unique Lowner-John ellipsoid, respectively, we obtain a pair of optimal values, which would be a coordinate on a two-dimensional plane. The scenarios, whose coordinates are close enough on the plane, will be treated as one scenario; thus our method reduces the sample size of an iid sample considerably. Instead of using a large iid sample d
﻿