Fast gradient descent method for Mean-CVaR optimization

被引：21
|
作者：
Iyengar, Garud [1 ]
Ma, Alfred Ka Chun [2 ,3 ]
机构：
[1] Columbia Univ, Dept Ind Engn & Operat Res, New York, NY 10027 USA
[2] Chinese Univ Hong Kong, Dept Math, Hong Kong, Hong Kong, Peoples R China
[3] Celestial Asia Secur Holdings, Hong Kong, Hong Kong, Peoples R China
来源：
ANNALS OF OPERATIONS RESEARCH | 2013年 / 205卷 / 01期
基金：
美国国家科学基金会;
关键词：
Conditional value-at-risk; Portfolio optimization; VALUE-AT-RISK; PORTFOLIO OPTIMIZATION; DEVIATION;
D O I：
10.1007/s10479-012-1245-8
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 propose an iterative gradient descent algorithm for solving scenario-based Mean-CVaR portfolio selection problem. The algorithm is fast and does not require any LP solver. It also has efficiency advantage over the LP approach for large scenario size.
引用
收藏
页码：203 / 212
页数：10
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