An adaptive truncation criterion,for linesearch-based truncated Newton methods in large scale nonconvex optimization |
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| Authors: | Andrea Caliciotti Giovanni Fasano Stephen G Nash Massimo Roma |
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| Institution: | 1. Dipartimento di Ingegneria Informatica, Automatica e Gestionale “A. Ruberti” SAPIENZA, Università di Roma, via Ariosto, 25 –00185 Roma, Italy;2. Department of Management, University Ca’ Foscari of Venice, S. Giobbe, Cannaregio 873 –30121 Venice, Italy;3. Systems Engineering & Operations Research Department, George Mason University, 4400 University Drive Fairfax –VA 22030, USA |
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| Abstract: | 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. |
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| Keywords: | Large scale nonconvex optimization Linesearch-based truncated Newton methods Krylov subspace methods Adaptive truncation criterion |
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