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SDDP for multistage stochastic programs: preprocessing via scenario reduction

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Even with recent enhancements, computation times for large-scale multistage problems with risk-averse objective functions can be very long. Therefore, preprocessing via scenario reduction could be considered as a way to significantly improve the overall performance. Stage-wise backward reduction of single scenarios applied to a fixed branching structure of the tree is a promising tool for efficient algorithms like stochastic dual dynamic programming. We provide computational results which show an acceptable precision of the results for the reduced problem and a substantial decrease of the total computation time.

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Jitka Dupačová has initiated this project and we have worked together till the very final form of the article, unfortunately, she passed away during the publication process. I would like to dedicate this paper to Jitka, for her restless guidance, knowledge, patience and care. The research was partly supported by the project of the Czech Science Foundation P/402/12/G097 ’DYME/Dynamic Models in Economics’.

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Correspondence to Václav Kozmík.

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Jitka Dupačová: Passed away 4 January 2016.

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Dupačová, J., Kozmík, V. SDDP for multistage stochastic programs: preprocessing via scenario reduction. Comput Manag Sci 14, 67–80 (2017).

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