Scenario tree reduction methods through clustering nodes

•We design new scenario tree reduction algorithms through clustering tree nodes.•A new distance to measure the difference between two scenario trees is defined.•We construct a new model for general multiperiod scenario tree reduction.•Several scenario tree reduction algorithms are designed by solvin...

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Vydané v:Computers & chemical engineering Ročník 109; s. 96 - 111
Hlavní autori: Chen, Zhiping, Yan, Zhe
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 04.01.2018
Predmet:
Clustering
Computational efficiency
Portfolio selection
Scenario tree reduction
Stability
Stochastic programs
Stochastic programs
Scenario tree reduction
Stability
Portfolio selection
Computational efficiency
Clustering
ISSN:0098-1354, 1873-4375
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Popis
Shrnutí:•We design new scenario tree reduction algorithms through clustering tree nodes.•A new distance to measure the difference between two scenario trees is defined.•We construct a new model for general multiperiod scenario tree reduction.•Several scenario tree reduction algorithms are designed by solving the model.•The new algorithm is superior to the simultaneous backward reduction method. To develop practical and efficient scenario tree reduction methods, we introduce a new methodology which depends on clustering nodes, and thus an easy-to-handle distance function to measure the difference between two scenario trees is designed. On the basis of minimizing the new distance, we construct a multiperiod scenario tree reduction model which is supported theoretically by the stability results of stochastic programs. By solving the model, we design a stage-wise scenario tree reduction algorithm which is superior to the simultaneous backward reduction method in terms of both computational complexity and solution results of stochastic programming problems, the corresponding reduction algorithm especially for fan-liked trees is also presented. We further design a multiperiod scenario tree reduction algorithm with a pre-specified distance by utilizing the stability results of stochastic programs. A series of numerical experiments with real trading data and the application to multiperiod portfolio selection problem demonstrate the practicality, efficiency and robustness of proposed reduction model and algorithms.
ISSN:0098-1354
1873-4375
DOI:10.1016/j.compchemeng.2017.10.017