Decomposition methods in stochastic programming
Copyright policy )Decomposition methods in stochastic programming
Stochastic programming problems have very large dimension and characteristic structures which are tractable by decomposition. We review basic ideas of cutting plane methods, augmented Lagrangian and splitting methods, and stochastic decomposition methods for convex polyhedral multi-stage stochastic programming problems..
- Autonomous University of Barcelona Spain
- University of Wisconsin–Madison United States
- University of Wisconsin–Oshkosh United States
- International Institute for Applied Systems Analysis Austria
Decomposition, Stochastic methods, Dual methods, cutting plane methods, splitting, stochastic decomposition, augmented Lagrangian, Stochastic programming, convex polyhedral multi-stage stochastic programming, Primal methods
Decomposition, Stochastic methods, Dual methods, cutting plane methods, splitting, stochastic decomposition, augmented Lagrangian, Stochastic programming, convex polyhedral multi-stage stochastic programming, Primal methods
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