Introduction to algorithms
Optimization
Quantitative Stability in Stochastic Programming: The Method of Probability Metrics
Mathematics of Operations Research
Step decision rules for multistage stochastic programming: A heuristic approach
Automatica (Journal of IFAC)
Medium term scheduling of a hydro-thermal system using stochastic model predictive control
Automatica (Journal of IFAC)
Scenario reduction in stochastic programming with respect to discrepancy distances
Computational Optimization and Applications
A Q-learning approach to derive optimal consumption and investment strategies
IEEE Transactions on Neural Networks
Scenario reduction techniques in stochastic programming
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
A review of scenario generation methods
International Journal of Computing Science and Mathematics
Computers and Industrial Engineering
A note on scenario reduction for two-stage stochastic programs
Operations Research Letters
Scenario tree generation approaches using K-means and LP moment matching methods
Journal of Computational and Applied Mathematics
Scenario construction and reduction applied to stochastic power generation expansion planning
Computers and Operations Research
Optimal scenario tree reductions for the stochastic unit commitment problem
Proceedings of the Winter Simulation Conference
A decomposition-based crash-start for stochastic programming
Computational Optimization and Applications
An advanced system for portfolio optimisation
International Journal of Grid and Utility Computing
Hi-index | 0.01 |
We consider convex stochastic programs with an (approximate) initial probability distribution P having finite support supp P, i.e., finitely many scenarios. The behaviour of such stochastic programs is stable with respect to perturbations of P measured in terms of a Fortet-Mourier probability metric. The problem of optimal scenario reduction consists in determining a probability measure that is supported by a subset of supp P of prescribed cardinality and is closest to P in terms of such a probability metric. Two new versions of forward and backward type algorithms are presented for computing such optimally reduced probability measures approximately. Compared to earlier versions, the computational performance (accuracy, running time) of the new algorithms has been improved considerably. Numerical experience is reported for different instances of scenario trees with computable optimal lower bounds. The test examples also include a ternary scenario tree representing the weekly electrical load process in a power management model.