Sampling-Based Decomposition Methods for Multistage Stochastic Programs Based on Extended Polyhedral Risk Measures

2012 ◽  
Vol 22 (2) ◽  
pp. 286-312 ◽  
Author(s):  
Vincent Guigues ◽  
Werner Römisch
2013 ◽  
Vol 61 (4) ◽  
pp. 957-970 ◽  
Author(s):  
Andy Philpott ◽  
Vitor de Matos ◽  
Erlon Finardi

2013 ◽  
Vol 50 (02) ◽  
pp. 533-541 ◽  
Author(s):  
Alexander Shapiro

In this paper we study asymptotic consistency of law invariant convex risk measures and the corresponding risk averse stochastic programming problems for independent, identically distributed data. Under mild regularity conditions, we prove a law of large numbers and epiconvergence of the corresponding statistical estimators. This can be applied in a straightforward way to establish convergence with probability 1 of sample-based estimators of risk averse stochastic programming problems.


2017 ◽  
Vol 27 (3) ◽  
pp. 1772-1800 ◽  
Author(s):  
Burhaneddi̇n Sandikçi ◽  
Osman Y. Özaltin

2020 ◽  
Vol 30 (3) ◽  
pp. 2083-2102
Author(s):  
Alexander Shapiro ◽  
Lingquan Ding

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