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Accelerating the regularized decomposition method for two stage stochastic linear problems
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DOI：10.1016/S0377-2217(96)00401-8.png)
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Abstract
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Practical improvements of the regularized decomposition algorithm for two stage stochastic problems are presented. They are associated with the primal simplex method for solving subproblems. A penalty formulation of the subproblems is used, which facilitates crash and warm starts, and allows more freedom when creating the model. The computational results are highly encouraging. (C) 1997 Elsevier Science B.V.
Keywords:
stochastic programming
decomposition
non-smooth optimization
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