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Fast gradient descent method for Mean-CVaR optimization

delete2013-02-07
delete29
PRE
AI
G
Garud Iyengar
A
Alfred Ka Chun *
DOI：10.1007/s10479-012-1245-8delete
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摘要

摘要

En 中文
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.
Keyword:
Conditional value-at-risk
Portfolio optimization
AI总结

AI总结

对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

Annals of Operations Research 封面图
Annals of Operations Research
IF:
4.5
论文数:
8.1K
被引数:
2.1W

机构

C
Columbia University
学者数:
7.1W
论文数: 6.4W
被引数: 263
C
Chinese University of Hong Kong
学者数:
3.4W
论文数: 3.2W
被引数: 5.6W
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