The proposed strategies for deleting scenarios are based on postoptimality analysis of the optimal value function with respect to probabilities of the included scenarios. These strategies can be used to reduce the size of the large scenario based problems or of the problems constructed in the course of specific numerical procedures, such as stochastic decomposition or scenario aggregation. A convex nonsmooth optimization problem is replaced by a sequence of line search problems along recursively updated rays. Convergence of the method is proved and applications indicated
The field of multi-stage stochastic programming provides a rich modelling framework to tackle a broa...
The field of multi-stage stochastic programming provides a rich modelling framework to tackle a broa...
A framework for the reduction of scenario trees as inputs of (linear) multistage stochastic programs...
Scenarios are indispensable ingredients for the numerical solution of stochastic optimization proble...
Scenarios are indispensable ingredients for the numerical solution of stochastic programs. Earlier a...
We consider convex stochastic programs with an (approximate) initial probability distribution P havi...
We consider convex stochastic programs with an (approximate) initial probability distribution P havi...
We consider convex stochastic programs with an (approximate) initial probability distribution P havi...
Scenarios are indispensable ingredients for the numerical solution of stochastic programs. Earlier a...
Une approche classique pour traiter les problèmes d’optimisation avec incertitude à deux- et multi-...
Scenarios are indispensable ingredients for the numerical solution of stochastic optimization proble...
In the field of data-driven optimization under uncertainty, scenario reduction is a commonly used t...
A framework for the reduction of scenario trees as inputs of (linear) multi-stage stochastic program...
This work presents an empirical analysis of popular scenario generation methods for stochastic optim...
Stochastic programming concerns mathematical programming in the presence of uncertainty. In a stocha...
The field of multi-stage stochastic programming provides a rich modelling framework to tackle a broa...
The field of multi-stage stochastic programming provides a rich modelling framework to tackle a broa...
A framework for the reduction of scenario trees as inputs of (linear) multistage stochastic programs...
Scenarios are indispensable ingredients for the numerical solution of stochastic optimization proble...
Scenarios are indispensable ingredients for the numerical solution of stochastic programs. Earlier a...
We consider convex stochastic programs with an (approximate) initial probability distribution P havi...
We consider convex stochastic programs with an (approximate) initial probability distribution P havi...
We consider convex stochastic programs with an (approximate) initial probability distribution P havi...
Scenarios are indispensable ingredients for the numerical solution of stochastic programs. Earlier a...
Une approche classique pour traiter les problèmes d’optimisation avec incertitude à deux- et multi-...
Scenarios are indispensable ingredients for the numerical solution of stochastic optimization proble...
In the field of data-driven optimization under uncertainty, scenario reduction is a commonly used t...
A framework for the reduction of scenario trees as inputs of (linear) multi-stage stochastic program...
This work presents an empirical analysis of popular scenario generation methods for stochastic optim...
Stochastic programming concerns mathematical programming in the presence of uncertainty. In a stocha...
The field of multi-stage stochastic programming provides a rich modelling framework to tackle a broa...
The field of multi-stage stochastic programming provides a rich modelling framework to tackle a broa...
A framework for the reduction of scenario trees as inputs of (linear) multistage stochastic programs...