In this paper, we report data and experiments related to the research article entitled “An adaptive truncation criterion, for linesearch-based truncated Newton methods in large scale nonconvex optimization” by Caliciotti et al. . In particular, in Caliciotti et al. , large scale unconstrained optimization problems are considered by applying linesearch-based truncated Newton methods. In this framework, a key point is the reduction of the number of inner iterations needed, at each outer iteration, to approximately solving the Newton equation. A novel adaptive truncation criterion is introduced in Caliciotti et al.  to this aim. Here, we report the details concerning numerical experiences over a commonly used test set, namely CUTEst (Gould et al., 2015) . Moreover, comparisons are reported in terms of performance profiles (Dolan and Moré 2002) , adopting different parameters settings. Finally, our linesearch-based scheme is compared with a renowned trust region method, namely TRON (Lin and Moré 1999) .
Giovanni Fasano (Corresponding)
|Data di pubblicazione:||2018|
|Titolo:||Data and performance profiles applying an adaptive truncation criterion, within linesearch-based truncated Newton methods, in large scale nonconvex optimization|
|Rivista:||DATA IN BRIEF|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1016/j.dib.2018.01.012|
|Appare nelle tipologie:||2.1 Articolo su rivista |