On level regularization with normal solutions in decomposition methods for multistage stochastic programming problems

Wim Ackooij (), Welington Oliveira () and Yongjia Song ()
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Wim Ackooij: EDF R&D. OSIRIS 7
Welington Oliveira: MINES ParisTech, PSL - Research University, CMA - Centre de Mathématiques Appliquées
Yongjia Song: Clemson University

Computational Optimization and Applications, 2019, vol. 74, issue 1, No 1, 42 pages

Abstract: Abstract We consider well-known decomposition techniques for multistage stochastic programming and a new scheme based on normal solutions for stabilizing iterates during the solution process. The given algorithms combine ideas from finite perturbation of convex programs and level bundle methods to regularize the so-called forward step of these decomposition methods. Numerical experiments on a hydrothermal scheduling problem indicate that our algorithms are competitive with the state-of-the-art approaches such as multistage regularized decomposition, nested decomposition and stochastic dual dynamic programming.

Keywords: Normal solution; SDDP algorithm; Stochastic optimization; Nonsmooth optimization (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10589-019-00104-x

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