Constraint generation for risk averse two-stage stochastic programs Articles uri icon

publication date

  • January 2021

start page

  • 194

end page

  • 206


  • 1


  • 288

International Standard Serial Number (ISSN)

  • 0377-2217

Electronic International Standard Serial Number (EISSN)

  • 1872-6860


  • 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.


  • Statistics


  • cvar; decision analysis under uncertainty; risk aversion; stochastic programming