Numerical Experiences with New Truncated Newton Methods in Large Scale Unconstrained Optimization

被引：0
|
作者：
Stefano Lucidi
Massimo Roma
机构：
[1] Università di Roma “La Sapienza”,Dipartimento di Informatica e Sistemistica
来源：
Computational Optimization and Applications | 1997年 / 7卷
关键词：
Large scale unconstrained optimization; Truncated Newton methods; negative curvature direction; curvilinear linesearch; Lanczos method;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Recently, in [12] a very general class oftruncated Newton methods has been proposed for solving large scale unconstrained optimization problems. In this work we present the results of an extensive numericalexperience obtained by different algorithms which belong to the preceding class. This numerical study, besides investigating which arethe best algorithmic choices of the proposed approach, clarifies some significant points which underlies every truncated Newton based algorithm.
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页码：71 / 87
页数：16
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