A branch-and-bound algorithm with growing datasets for large-scale parameter estimation

The solution of nonconvex parameter estimation problems with deterministic global optimization methods is desirable but challenging, especially if large measurement datasets are considered. We propose to exploit the structure of this class of optimization problems to enable their solution with the s...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	European journal of operational research Jg. 316; H. 1; S. 36 - 45
Hauptverfasser:	Sass, Susanne, Mitsos, Alexander, Bongartz, Dominik, Bell, Ian H., Nikolov, Nikolay I., Tsoukalas, Angelos
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.07.2024
Schlagworte:	Global optimization Large scale optimization Nonlinear programming Regression Spatial branch and bound algorithm Large scale optimization Regression Global optimization Nonlinear programming Spatial branch and bound algorithm
ISSN:	0377-2217
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!