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Regularized decomposition of stochastic programs: algorithmic techniques and numerical results (1993).
| Content Provider | CiteSeerX |
|---|---|
| Author | Ruszczynski, Andrzej |
| Abstract | 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. |
| File Format | |
| Publisher Date | 1993-01-01 |
| Access Restriction | Open |
| Subject Keyword | Numerical Result Stochastic Program Algorithmic Technique Large Real World Problem Large Scale Optimization Method Finitely Convergent Non-simplex Method Dantzig-wolfe Decomposition Principle Stochastic Programming Linear Programming Problem |
| Content Type | Text |