An adaptive truncation criterion, for linesearch-based truncated Newton methods in large scale nonconvex optimization

作者：Caliciotti Andrea; Fasano Giovanni; Nash Stephen G; Roma Massimo^*

来源：Operations Research Letters, 2018, 46(1): 7-12.

DOI：10.1016/j.orl.2017.10.014

• Large scale nonconvex optimization
• Linesearch-based truncated Newton methods
• Krylov subspace methods
• Adaptive truncation criterion

Starting from the paper by Nash and Sofer (1990), 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.

• 出版日期2018-1