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Dettaglio pubblicazione

2018, DATA IN BRIEF, Pages 246-255 (volume: 17)

Data and performance profiles applying an adaptive truncation criterion, within linesearch-based truncated Newton methods in large scale nonconvex optimization (01a Articolo in rivista)

Caliciotti Andrea, Fasano Giovanni, Nash S. G., Roma Massimo

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. [1]. In particular, in [1], 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 [1] to this aim. Here, we report the details concerning numerical experiences over a commonly used test set, namely CUTEst [2]. Moreover, comparisons are reported in terms of performance profiles [3], adopting different parameters settings. Finally, our linesearch-based scheme is compared with a renowned trust region method, namely TRON [4].
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