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

摘要

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 algorithmASA-BCPproposed in Cristofari et al. (2017) is compared with the related methodNMBC (De Santis et al., 2012). Then, a comparison with the renowned methodsALGENCAN(Birgin and Martínez et al., 2002) andLANCELOT B(Gould et al., 2003) is reported.