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Dettaglio pubblicazione

2018, OPERATIONS RESEARCH LETTERS, Pages 7-12 (volume: 46)

An adaptive truncation criterion, for linesearch-based truncated Newton methods in large scale nonconvex optimization (01a Articolo in rivista)

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

Starting from the paper by Nash and Sofer (1990), we propose a heuristic adaptive truncation criterionfor the inner iterations within linesearch-based truncated Newton methods. Our aim is to possibly avoid ‘‘over-solving’’ of the Newton equation, based on a comparison between the predicted reduction of the objective function and the actual reduction obtained. A numerical experience on unconstrained optimization problems highlights a satisfactory effectiveness and robustness of the adaptive criterion proposed, when a residual-based truncation criterion is selected.
Gruppo di ricerca: Continuous Optimization
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