On conjugate gradient algorithms as objects of scientific study

被引：2

作者：

Nazareth, JL ^{[1
]}

机构：

[1] Computat Decis Support Syst, Bainbridge Isl, WA 98110 USA

来源：

OPTIMIZATION METHODS & SOFTWARE | 2003年 / 18卷 / 05期

关键词：

conjugate gradients; multialgorithms; algorithmic science; population-thinking; nonlinear optimization;

D O I：

10.1080/10556780310001603653

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

A family of conjugate gradient (CG) algorithms for nonlinear optimization is explored within the context of two contrasting algorithmic paradigms: "essentialist" vis-a-vis "population-based". New CG variants are proposed that can close the current gap between theory and practice in the nonlinear conjugate gradients area. Our discussion also serves as an illustrative case study, within the setting of a particular class of optimization algorithms, of broader and more fundamental "algorithmic science" themes that are considered, in detail, in a companion monograph (Nazareth [18]).

引用

页码：555 / 565

页数：11

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[1] A SUBSPACE STUDY ON CONJUGATE-GRADIENT ALGORITHMS
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