| 来源类型 | Article |
| 规范类型 | 其他 |
| DOI | 10.1016/S0377-2217(96)00396-7 |
| Optimal allocation of simulation experiments in discrete stochastic optimization. | |
| Pflug GC; Futschik A | |
| 发表日期 | 1997 |
| 出处 | European Journal of Operations Research 101 (2): 245-260 |
| 出版年 | 1997 |
| 语种 | 英语 |
| 摘要 | Approximate solutions for discrete stochastic optimization problems are often obtained via simulation. It is reasonable to complement these solutions by confidence regions for the argmin-set. We address the question how a certain total number of random draws should be distributed among the set of alternatives. Two goals are considered: the minimization of the costs caused by using a statistical estimate of the true argmin, and the minimization of the expected size of the confidence sets. We show that an asymptotically optimal sampling strategy in the case of normal errors can be obtained by solving a convex optimization problem. To reduce the computational effort we propose a regularization that leads to a simple one-step allocation rule. |
| 主题 | Risk, Modeling, Policy (RMP) |
| 关键词 | Discrete stochastic optimization Simulation Sampling strategy Large deviations |
| URL | http://pure.iiasa.ac.at/id/eprint/5048/ |
| 来源智库 | International Institute for Applied Systems Analysis (Austria) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/127588 |
| 推荐引用方式 GB/T 7714 | Pflug GC,Futschik A. Optimal allocation of simulation experiments in discrete stochastic optimization.. 1997. |
| 条目包含的文件 | 条目无相关文件。 | |||||
| 个性服务 |
| 推荐该条目 |
| 保存到收藏夹 |
| 导出为Endnote文件 |
| 谷歌学术 |
| 谷歌学术中相似的文章 |
| [Pflug GC]的文章 |
| [Futschik A]的文章 |
| 百度学术 |
| 百度学术中相似的文章 |
| [Pflug GC]的文章 |
| [Futschik A]的文章 |
| 必应学术 |
| 必应学术中相似的文章 |
| [Pflug GC]的文章 |
| [Futschik A]的文章 |
| 相关权益政策 |
| 暂无数据 |
| 收藏/分享 |
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
