Multicut Algorithm for Two-Stage Stochastic Linear Programs

摘要

Outer linearization methods, such as Van Slyke and Wets's L-shaped method for stochastic linear programs, generally apply a single cut on the nonlinear objective at each major iteration. The structure of stochastic programs allows for several cuts to be placed at once. This paper describes a multicut algorithm to carry out this procedure. It presents experimental and theoretical justification for reductions in major iterations.