A Fast Smoothing Procedure for Large-Scale Stochastic Programming

We develop a fast smoothing procedure for solving linear two-stage stochastic programs, which outperforms the well-known L-shaped algorithm on large-scale benchmarks. We derive problem-dependent bounds for the effect of smoothing and characterize the convergence rate of the proposed algorithm. The theory suggests that the smoothing scheme can be sped up by sacrificing accuracy in the final solution. To obtain an efficient and effective method, we suggest a hybrid solution that combines the speed of the smoothing scheme with the accuracy of the L-shaped algorithm. We benchmark a parallel implementation of the smoothing scheme against an efficient parallelized L-shaped algorithm on three large-scale stochastic programs, in a distributed environment with 32 worker cores. The smoothing scheme reduces the solution time by up to an order of magnitude compared to L-shaped.