Globally convergent algorithms for solving unconstrained optimization problems

被引：3
|
作者：
Taheri, Sona [1 ]
Mammadov, Musa [1 ,2 ]
Seifollahi, Sattar [3 ]
机构：
[1] Univ Ballarat, Ctr Informat & Appl Optimizat, Grad Sch Sci Informat Technol & Engn, Ballarat, Vic 3353, Australia
[2] Univ Melbourne, Dept Elect & Elect Engn, Natl ICT, Melbourne, Vic 3010, Australia
[3] Univ Adelaide, Sch Civil Environm & Min Engn, Adelaide, SA 5005, Australia
来源：
OPTIMIZATION | 2015年 / 64卷 / 02期
关键词：
65K05; 90C53; 49M15; quasi-Newton method; Newton method; global convergence; superlinear convergence; gradient method; unconstrained optimization; CONJUGATE-GRADIENT; NEWTON METHOD; EQUATIONS; SYSTEMS;
D O I：
10.1080/02331934.2012.745529
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：
New algorithms for solving unconstrained optimization problems are presented based on the idea of combining two types of descent directions: the direction of anti-gradient and either the Newton or quasi-Newton directions. The use of latter directions allows one to improve the convergence rate. Global and superlinear convergence properties of these algorithms are established. Numerical experiments using some unconstrained test problems are reported. Also, the proposed algorithms are compared with some existing similar methods using results of experiments. This comparison demonstrates the efficiency of the proposed combined methods.
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页码：249 / 263
页数：15
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