We outline cutting-plane and regularisation techniques adapted to two-stage stochastic programming problems. We also consider the regularisation of feasibility issues. We sketch an approach that allows progressive approximation of the distribution of the random parameters.
We present a computational study comparing different approaches over a reasonably diverse collection of problems.
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