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

A Cristofari; M De Santis; S Lucidi; F Rinaldi

doi:10.1016/j.dib.2018.11.061

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

Data Brief. 2018 Nov 20:21:2155-2169. doi: 10.1016/j.dib.2018.11.061. eCollection 2018 Dec.

Authors

A Cristofari¹, M De Santis², S Lucidi², F Rinaldi¹

Affiliations

¹ Dipartimento di Matematica, Università di Padova, Via Trieste, 63, 35121 Padua, Italy.
² Dipartimento di Ingegneria Informatica, Automatica e Gestionale Sapienza Università di Roma, Via Ariosto, 25, 00185 Rome, Italy.

Abstract

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.