Abstract
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 Caliciotti et al. [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 Caliciotti et al. [1] to this aim. Here, we report the details concerning numerical experiences over a commonly used test set, namely CUTEst (Gould et al., 2015) [2]. Moreover, comparisons are reported in terms of performance profiles (Dolan and Moré, 2002) [3], adopting different parameters settings. Finally, our linesearch-based scheme is compared with a renowned trust region method, namely TRON (Lin and Moré, 1999) [4].
Highlights
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
Operations Research and Management Science Nonlinear Optimization Table, graph http://www.cuter.rl.ac.uk/, experimental output data Raw and filtered None Different codes have been experienced over the CUTEst test set; comparisons among their performance are provided in terms of performance profiles
As regards the set of test problems, we selected all the unconstrained convex and nonconvex large problems available in the CUTEst collection [2], and when a problem is of variable dimension, we considered two different dimensions
Summary
Title: Data and performance profiles applying an adaptive truncation criterion, within linesearchbased truncated Newton methods, in large scale nonconvex optimization. Authors: Andrea Caliciottia, Giovanni Fasanob, Stephen G. A Dipartimento di Ingegneria Informatica, Automatica e Gestionale “A. Ruberti”, SAPIENZA, Università di Roma, via Ariosto, 25 - 00185 Roma, Italy. B Department of Management, University Ca' Foscari of Venice, S. C Systems Engineering & Operations Research Department, George Mason University, 4400 University Drive Fairfax - VA 22030, USA
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