Tree approximation of scenario processes for multistage stochastic optimization: algorithms and fast implementations

Kirui, K.B., Pflug, G. ORCID: https://orcid.org/0000-0001-8215-3550, & Pichler, A. (2025). Tree approximation of scenario processes for multistage stochastic optimization: algorithms and fast implementations. Computational Management Science 22 (2) e14. 10.1007/s10287-025-00542-5.

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Abstract

For solving multistage stochastic optimization problems it is essential to develop finite approximations of the stochastic process. While the goal is always to find a finite model which represents a given knowledge about the real data process as accurate as possible, the ways of estimating the discrete approximating model may be quite different: (1) if the stochastic model is known as a solution of a stochastic differential equation, e.g., one may generate the scenario tree directly from the specified model; (2) if a simulation algorithm is available, which allows simulating trajectories from all conditional distributions, a scenario tree can be generated by stochastic approximation; (3) if only some observed trajectories of the scenario process are available, the construction of the approximating process can be based on non-parametric conditional density estimates. We also elaborate on the important concept of distances, which allows us to assess the quality of the approximation. We study these methods and apply them to electricity price data. Our fast implementation ScenTrees.jl including an exhaustive documentation is available for free at GitHub (ScenTrees.jl: https://github.com/aloispichler/ScenTrees.jl, cf. Kirui et al. (J Open Sour Softw 5(46):1912, 2020)).

Item Type:	Article
Uncontrolled Keywords:	Decision-making under uncertainty, Scenario tree generation, Scenario lattice generation, Nested distance
Research Programs:	Advancing Systems Analysis (ASA) Advancing Systems Analysis (ASA) > Systemic Risk and Resilience (SYRR)
Depositing User:	Luke Kirwan
Date Deposited:	01 Sep 2025 08:55
Last Modified:	01 Sep 2025 08:55
URI:	https://pure.iiasa.ac.at/20852

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