Sass, S.
, Mitsos, A.
, Bongartz, D.
, Bell, I.
, Nikolov, N.
and Tsoukalas, A.
(2024),
A branch-and-bound algorithm with growing datasets for large-scale parameter estimation, European Journal of Operational Research, [online], https://doi.org/10.1016/j.ejor.2024.02.020, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=936391
(Accessed October 7, 2024)
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A branch-and-bound algorithm with growing datasets for large-scale parameter estimation
Published
Author(s)
Susanne Sass, Alexander Mitsos, Dominik Bongartz, , Nikolay Nikolov, Angelos Tsoukalas
Abstract
The solution of nonconvex parameter estimation problems with deterministic global optimization methods is desirable but challenging, especially if large measurement data sets are considered. We propose to exploit the structure of this class of optimization problems to enable their solution with the spatial branch-and-bound algorithm. In detail, we start with a reduced dataset in the root node and progressively augment it, converging to the full dataset. We show for nonlinear programs (NLPs) that our algorithm converges to the global solution of the original problem considering the full dataset. A numerical case study with a mixed-integer nonlinear program (MINLP) from chemical engineering, namely fitting the equation of state of propane, emphasizes the potential for savings of computational effort with our proposed approach.Citation
European Journal of Operational Research
Volume
316
Pub Type
Journals
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Created February 23, 2024, Updated September 10, 2024