Decision making under uncertainty is a challenge faced by many decision makers.
Computationally however, many models in stochastic programming remain unsolvable because of overwhelming dimensionality. For a model to be well solvable, its special structure must be explored. Most of the solution methods are based on decomposing the data. Numerical experiments show that our decomposition algorithm performs better than other methods for stochastic programming. We apply our decomposition method to a two-period portfolio selection problem using options on a stock index.