Refining bounds for stochastic linear programs with linearly transformed independent random variables (Q1079127)

The numerical solution of two-stage stochastic programming problems demands, as a rule, adaptive discretization schemes for the figuring random variables: these approximations are refined until the respective bounds on the second-stage problem satisfy some numerical stopping rule. In the paper on approach is proposed which leads to tractable approximations, when in the two-stage problem the right-hand side is a linear combination of independent random variables.