A major issue in any application of multistage stochastic programming is the representation of the underlying random data process. We discuss the case when enough data paths can be generated according to an accepted parametric or nonparametric stochastic model. No assumptions on convexity with respect to the random parameters are required. We emphasize the notion of representative scenarios (or a representative scenario tree) relative to the problem being modeled
A multistage stochastic linear program (MSLP) is a model of sequential stochastic optimization where...
An important issue for solving multistage stochastic programs consists in the approximate representa...
This paper presents new algorithms for the dynamic generation of scenario trees for multistage stoch...
This thesis deals with multi-stage stochastic programming in the context of random process represent...
This thesis deals with multi-stage stochastic programming in the context of random process represent...
This thesis deals with multi-stage stochastic programming in the context of random process represent...
In recent years, stochastic programming has gained an increasing popularity within the mathematical ...
Stochastic programs can only be solved with discrete distributions of limited cardinality. Input, ho...
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...
In stochastic programming models we always face the problem of how to represent the random variables...
A framework for the reduction of scenario trees as inputs of (linear) multi-stage stochastic program...
An important issue for solving multistage stochastic programs consists inthe approximate representat...
A framework for the reduction of scenario trees as inputs of (linear) multistage stochastic programs...
A multistage stochastic linear program (MSLP) is a model of sequential stochastic optimization where...
An important issue for solving multistage stochastic programs consists in the approximate representa...
This paper presents new algorithms for the dynamic generation of scenario trees for multistage stoch...
This thesis deals with multi-stage stochastic programming in the context of random process represent...
This thesis deals with multi-stage stochastic programming in the context of random process represent...
This thesis deals with multi-stage stochastic programming in the context of random process represent...
In recent years, stochastic programming has gained an increasing popularity within the mathematical ...
Stochastic programs can only be solved with discrete distributions of limited cardinality. Input, ho...
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...
In stochastic programming models we always face the problem of how to represent the random variables...
A framework for the reduction of scenario trees as inputs of (linear) multi-stage stochastic program...
An important issue for solving multistage stochastic programs consists inthe approximate representat...
A framework for the reduction of scenario trees as inputs of (linear) multistage stochastic programs...
A multistage stochastic linear program (MSLP) is a model of sequential stochastic optimization where...
An important issue for solving multistage stochastic programs consists in the approximate representa...
This paper presents new algorithms for the dynamic generation of scenario trees for multistage stoch...