Regularized Decomposition of Stochastic Programs: Algorithmic Techniques and Numerical Results
Andrzej Ruszczyński · IIASA PURE (International Institute of Applied Systems Analysis) · 1993
A finitely convergent non-simplex method for large scale structured linear programming problems arising in stochastic programming is presented. The method combines the ideas of the Dantzig-Wolfe decomposition principle and modern nonsmooth optimization methods. Algorithmic techniques taking advantage of properties of stochastic programs are described and numerical results for large real world problems reported.