Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/286909 
Erscheinungsjahr: 
2021
Quellenangabe: 
[Journal:] Computational Optimization and Applications [ISSN:] 1573-2894 [Volume:] 79 [Issue:] 1 [Publisher:] Springer US [Place:] New York, NY [Year:] 2021 [Pages:] 223-233
Verlag: 
Springer US, New York, NY
Zusammenfassung: 
We consider a nonconvex mixed-integer nonlinear programming (MINLP) model proposed by Goldberg et al. (Comput Optim Appl 58:523–541, 2014. https://doi.org/10.1007/s10589-014-9647-y) for piecewise linear function fitting. We show that this MINLP model is incomplete and can result in a piecewise linear curve that is not the graph of a function, because it misses a set of necessary constraints. We provide two counterexamples to illustrate this effect, and propose three alternative models that correct this behavior. We investigate the theoretical relationship between these models and evaluate their computational performance.
Schlagwörter: 
Mixed-integer nonlinear program
Linear spline regression
Branch-and-bound
Reformulation
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