.png){kind=icon}
Return
Fast gradient descent method for Mean-CVaR optimization
.png){kind=icon}
PRE
AI
DOI：10.1007/s10479-012-1245-8.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
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.
Keywords:
Conditional value-at-risk
Portfolio optimization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.5
Papers:
8.1K
Citations:
2.1W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSave
ShareSaveShareSave
View more