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  • Search: person:"Bongartz, Dominik"
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Subject
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Global optimization 3 Algorithm 1 Algorithmus 1 Branch and Bound 1 Branch-and-Bound 1 Data-driven modeling 1 Dynamic systems 1 Estimation theory 1 Hammerstein–Wiener 1 Large scale optimization 1 Linearization 1 MAiNGO 1 Mathematical programming 1 Mathematische Optimierung 1 McCormick 1 NMPC 1 Nonlinear programming 1 Optimal control 1 Regression 1 Regression analysis 1 Regressionsanalyse 1 Schätztheorie 1 Spatial branch and bound algorithm 1
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Online availability
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Free 3
Type of publication
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Article 3
Type of publication (narrower categories)
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Article 2 Article in journal 1 Aufsatz in Zeitschrift 1
Language
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English 3
Author
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Bongartz, Dominik 3 Mitsos, Alexander 3 Najman, Jaromił 2 Sass, Susanne 2 Bell, Ian H. 1 Kappatou, Chrysoula D. 1 Nikolov, Nikolai 1 Tsoukalas, Angelos 1
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Published in...
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Journal of Global Optimization 2 European journal of operational research : EJOR 1
Source
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EconStor 2 ECONIS (ZBW) 1
Showing 1 - 3 of 3
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A branch-and-bound algorithm with growing datasets for large-scale parameter estimation
Sass, Susanne; Mitsos, Alexander; Bongartz, Dominik; … - In: European journal of operational research : EJOR 316 (2024) 1, pp. 36-45
Persistent link: https://www.econbiz.de/10014566295
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Global dynamic optimization with Hammerstein–Wiener models embedded
Kappatou, Chrysoula D.; Bongartz, Dominik; Najman, Jaromił - In: Journal of Global Optimization 84 (2022) 2, pp. 321-347
Hammerstein–Wiener models constitute a significant class of block-structured dynamic models, as they approximate process nonlinearities on the basis of input–output data without requiring identification of a full nonlinear process model. Optimization problems with Hammerstein–Wiener models...
Persistent link: https://www.econbiz.de/10015194004
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Linearization of McCormick relaxations and hybridization with the auxiliary variable method
Najman, Jaromił; Bongartz, Dominik; Mitsos, Alexander - In: Journal of Global Optimization 80 (2021) 4, pp. 731-756
The computation of lower bounds via the solution of convex lower bounding problems depicts current state-of-the-art in deterministic global optimization. Typically, the nonlinear convex relaxations are further underestimated through linearizations of the convex underestimators at one or several...
Persistent link: https://www.econbiz.de/10014501779
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