Data and performance of an active-set truncated Newton method with non-monotone line search for bound-constrained optimization

Autor: A. Cristofari, M. De Santis, S. Lucidi, F. Rinaldi
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Zdroj: Data in Brief, Vol 21, Iss , Pp 2155-2169 (2018)
Druh dokumentu: article
ISSN: 2352-3409
DOI: 10.1016/j.dib.2018.11.061
Popis: In this data article, we report data and experiments related to the research article entitled “A Two-Stage Active-Set Algorithm for Bound-Constrained Optimization”, by Cristofari et al. (2017). The method proposed in Cristofari et al. (2017), tackles optimization problems with bound constraints by properly combining an active-set estimate with a truncated Newton strategy. Here, we report the detailed numerical experience performed over a commonly used test set, namely CUTEst (Gould et al., 2015). First, the algorithm ASA-BCP proposed in Cristofari et al. (2017) is compared with the related method NMBC (De Santis et al., 2012). Then, a comparison with the renowned methods ALGENCAN (Birgin and Martínez et al., 2002) and LANCELOT B (Gould et al., 2003) is reported.
Databáze: Directory of Open Access Journals