Please use this identifier to cite or link to this item: https://doi.org/10.1007/s10957-010-9676-3
Title: Stochastic Optimization Problems with CVaR Risk Measure and Their Sample Average Approximation
Authors: Meng, F.W.
Sun, J. 
Goh, M. 
Keywords: Conditional value-at-risk
Sample average approximation
Stochastic optimization
Variational analysis
Issue Date: 2010
Source: Meng, F.W., Sun, J., Goh, M. (2010). Stochastic Optimization Problems with CVaR Risk Measure and Their Sample Average Approximation. Journal of Optimization Theory and Applications 146 (2) : 399-418. ScholarBank@NUS Repository. https://doi.org/10.1007/s10957-010-9676-3
Abstract: We provide a refined convergence analysis for the SAA (sample average approximation) method applied to stochastic optimization problems with either single or mixed CVaR (conditional value-at-risk) measures. Under certain regularity conditions, it is shown that any accumulation point of the weak GKKT (generalized Karush-Kuhn-Tucker) points produced by the SAA method is almost surely a weak stationary point of the original CVaR or mixed CVaR optimization problems. In addition, it is shown that, as the sample size increases, the difference of the optimal values between the SAA problems and the original problem tends to zero with probability approaching one exponentially fast. © 2010 Springer Science+Business Media, LLC.
Source Title: Journal of Optimization Theory and Applications
URI: http://scholarbank.nus.edu.sg/handle/10635/44000
ISSN: 00223239
DOI: 10.1007/s10957-010-9676-3
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

7
checked on Dec 6, 2017

WEB OF SCIENCETM
Citations

7
checked on Nov 21, 2017

Page view(s)

47
checked on Dec 10, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.