A clustering approach for scenario tree reduction: an application to a stochastic programming portfolio optimization problem

作     者：Beraldi, Patrizia Bruni, Maria Elena 

作者机构：Univ Calabria Dept Mech Energy & Management Engn I-87036 Arcavacata Di Rende CS Italy 

出 版 物：《TOP》 (TOP：西班牙统计学与运筹学学会杂志)

年 卷 期：2014年第22卷第3期

页      面：934-949页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 

主　　题：Scenario tree reduction Clustering algorithms Stochastic programming Portfolio optimization 

摘      要：This paper deals with the problem of scenario tree reduction for stochastic programming problems. In particular, a reduction method based on cluster analysis is proposed and tested on a portfolio optimization problem. Extensive computational experiments were carried out to evaluate the performance of the proposed approach, both in terms of computational efficiency and efficacy. The analysis of the results shows that the clustering approach exhibits good performance also when compared with other reduction approaches.