Large-Scale Stochastic Linear Programs: Importance Sampling and Benders Decomposition

Abstract
The paper demonstrates how large-scale stochastic linear programs with recourse can be efficiently solved by using a blending of classical Benders decomposition with a relatively new technique called importance sampling. Numerical results of large-scale problems in the area of expansion planning of power systems and financial planning are presented.

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