an:07064413
Zbl 7064413
Caliciotti, Andrea; Fasano, Giovanni; Nash, Stephen G.; Roma, Massimo
An adaptive truncation criterion, for linesearch-based truncated Newton methods in large scale nonconvex optimization
EN
Oper. Res. Lett. 46, No. 1, 7-12 (2018)
0167-6377
2018
j
90-XX
large scale nonconvex optimization; linesearch-based truncated Newton methods; Krylov subspace methods; adaptive truncation criterion
Summary: Starting from the paper by the third author and \textit{A. Sofer} [ibid. 9, No. 4, 219--221 (1990; Zbl 0706.90073)], we propose a heuristic adaptive truncation criterion for 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.
0706.90073