Numerical experiences with new truncated Newton methods in large scale unconstrained optimization

Numerical experiences with new truncated Newton methods in large scale unconstrained optimization

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Article ID: iaor19981456
Country: Netherlands
Volume: 7
Issue: 1
Start Page Number: 71
End Page Number: 87
Publication Date: Jan 1997
Journal: Computational Optimization and Applications
Authors: ,
Keywords: large-scale optimization
Abstract:

Recently, a very general class of truncated Newton methods has been proposed for solving large scale unconstrained optimization problems. In this work we present the results of an extensive numerical experience obtained by different algorithms which belong to the preceding class. This numerical study, besides investigating which are the best algorithmic choices of the proposed approach, clarifies some significant points which underlie every truncated Newton based algorithm.

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