SDDP for multistage stochastic programs: preprocessing via scenario reduction

Jitka Dupačová and Václav Kozmík ()
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Jitka Dupačová: Charles University in Prague
Václav Kozmík: Charles University in Prague

Computational Management Science, 2017, vol. 14, issue 1, No 4, 67-80

Abstract: Abstract 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.

Keywords: Multistage stochastic programs; Stochastic dual dynamic programming; Multiperiod CVaR; Scenario reduction; 65C05; 90C15; 91G60 (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s10287-016-0261-6

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