Methods for Multistage Stochastic Linear Programs

Wim Stefanus Ackooij and Welington Luis de Oliveira
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Wim Stefanus Ackooij: Électricité de France (EDF R&D)
Welington Luis de Oliveira: Mines Paris - PSL

Chapter Chapter 18 in Methods of Nonsmooth Optimization in Stochastic Programming, 2025, pp 497-519 from Springer

Abstract: Abstract Instead of splitting methods that decompose stochastic programs into scenarios, this chapter focuses on node-decomposition approaches for solving multistage stochastic programs. Given a multistage scenario tree, strategies based on node decomposition yield smaller and simpler subproblems, reliable lower bound on the optimal value, and, more importantly, cutting-plane approximations of dynamic functions modelling future random costs. This chapter starts with the well-known nested decomposition (ND), which is an extension of the Benders decomposition to multistage stochastic linear programs. It then passes to a randomized variant of ND, denoted by stochastic dual dynamic programming (SDDP) algorithm. The focus is the linear setting.

Keywords: Recourse functions; Nested decomposition; SDDP (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-031-84837-7_18

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