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SDDP for multistage stochastic programs: Preprocessing via scenario reduction

Jitka Dupacova (dupacova***at***karlin.mff.cuni.cz)
Vaclav Kozmik (vaclav***at***kozmik.cz)

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 SDDP. 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

Category 1: Stochastic Programming

Citation: The final publication is available at Springer: SDDP for multistage stochastic programs: preprocessing via scenario reduction

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Entry Submitted: 08/18/2015
Entry Accepted: 08/18/2015
Entry Last Modified: 06/17/2016

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