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• W3033016679 abstract "A significant share of stochastic optimization problems in practice can be cast as two-stage stochastic programs. If uncertainty is available through a finite set of scenarios, which frequently occurs, and we are interested in accounting for risk aversion, the expectation in the recourse cost can be replaced with a worst-case function (i.e., robust optimization) or another risk-functional, such as conditional value-at-risk. In this paper we are interested in the latter situation especially when the number of scenarios is large. For computational efficiency we suggest a (clustering and) constraint generation algorithm. We establish convergence of these two algorithms and demonstrate their effectiveness through various numerical experiments." @default.
• W3033016679 created "2020-06-12" @default.
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• W3033016679 date "2021-01-01" @default.
• W3033016679 modified "2023-10-13" @default.
• W3033016679 title "Constraint generation for risk averse two-stage stochastic programs" @default.
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• W3033016679 doi "https://doi.org/10.1016/j.ejor.2020.05.064" @default.
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