Adaptive Partition-based SDDP Algorithms for Multistage Stochastic Linear Programming

2019/08/29 by Murwan Siddig, Siddig, Murwan, Yongjia Song +1
Computer Science · Decision Sciences · Engineering · Mathematics · #Computational Engineering #Electric Power System Optimization #FOS: Computer and information sciences #FOS: Mathematics #Finance #Optimization and Control (math.OC) #Risk and Portfolio Optimization #Water resources management and optimization #and Science (cs.CE) #cs.CE #math.OC

paper · pdf · doi:10.48550/arxiv.1908.11346

27 pages, 6 figures

arxiv created 2019/08/29 · openalex publication_date 2019/08/29 · arxiv updated 2019/08/30 · openalex created_date 2019/09/05 · openalex updated_date 2026/07/28

Abstract

In this paper, we extend the adaptive partition-based approach for solving two-stage stochastic programs with fixed recourse to the multistage stochastic programming setting. The proposed algorithms integrate the adaptive partition-based strategy with a popular approach for solving multistage stochastic programs, the stochastic dual dynamic programming, via different tree-traversal strategies in order to enhance its computational efficiency. Our numerical experiments on a hydro-thermal power generation planning problem show the effectiveness of the proposed algorithms.

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