Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/26794
Title: Minimum cardinality non-anticipativity constraint sets for multistage stochastic programming
Contributor(s): Boland, Natashia (author); Dumitrescu, Irina (author); Froyland, Gary (author); Kalinowski, Thomas  (author)orcid 
Publication Date: 2016-05
Early Online Version: 2016-01-07
DOI: 10.1007/s10107-015-0970-6
Handle Link: https://hdl.handle.net/1959.11/26794
Abstract: We consider multistage stochastic programs, in which decisions can adapt over time, (i.e., at each stage), in response to observation of one or more random variables (uncertain parameters). The case that the time at which each observation occurs is decision-dependent, known as stochastic programming with endogeneous observation of uncertainty, presents particular challenges in handling non-anticipativity. Although such stochastic programs can be tackled by using binary variables to model the time at which each endogenous uncertain parameter is observed, the consequent conditional non-anticipativity constraints form a very large class, with cardinality in the order of the square of the number of scenarios. However, depending on the properties of the set of scenarios considered, only very few of these constraints may be required for validity of the model. Here we characterize minimal sufficient sets of non-anticipativity constraints, and prove that their matroid structure enables sets of minimum cardinality to be found efficiently, under general conditions on the structure of the scenario set.
Publication Type: Journal Article
Grant Details: ARC/LP0561744
Source of Publication: Mathematical Programming, 157(1), p. 69-93
Publisher: Springer
Place of Publication: Germany
ISSN: 1436-4646
0025-5610
Fields of Research (FoR) 2008: 010206 Operations Research
010303 Optimisation
Fields of Research (FoR) 2020: 490108 Operations research
490304 Optimisation
Socio-Economic Objective (SEO) 2008: 970101 Expanding Knowledge in the Mathematical Sciences
Socio-Economic Objective (SEO) 2020: 280118 Expanding knowledge in the mathematical sciences
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

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