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

被引：11

作者：

Lucidi, S

Roma, M

机构：

[1] Dipto. di Informatica e Sistemistica, Univ. di Roma La Sapienza, Roma

来源：

COMPUTATIONAL OPTIMIZATION AND APPLICATIONS | 1997年 / 7卷 / 01期

关键词：

large scale unconstrained optimization; truncated Newton methods; negative curvature direction; curvilinear linesearch; Lanczos method;

D O I：

10.1023/A:1008619812615

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

Recently, in [12] 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 underlies every truncated Newton based algorithm.

引用

页码：71 / 87

页数：17