Fast gradient descent method for Mean-CVaR optimization

被引：0
|
作者：
Garud Iyengar
Alfred Ka Chun Ma
机构：
[1] Columbia University,Department of Industrial Engineering and Operations Research
[2] The Chinese University of Hong Kong,Department of Mathematics
[3] Celestial Asia Securities Holdings,undefined
来源：
Annals of Operations Research | 2013年 / 205卷
关键词：
Conditional value-at-risk; Portfolio optimization;
D O I：
暂无
中图分类号：
学科分类号：
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.
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页码：203 / 212
页数：9
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