Optimal Scenario Tree Reduction for Financial Optimization Problems
نویسندگان
چکیده
The solution of a multistage stochastic programming problem needs a suitable representation of uncertainty which may be obtained through a satisfactory scenario tree construction. Unfortunately there is a trade-off between the level of accuracy in the description of the stochastic component and the computational tractability of the resulting scenario-based problem. In this contribution we address the problem of how to face such a trade-off which plays a crucial role in the determination of the optimal solution. To this aim we discuss methods which allow to progressively reduce a given scenario tree by means of state aggregation. In this process it is important to take into account the choice of proper aggregation criteria in order to try to preserve all the relevant information.
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