The CG-BFGS Method for Unconstrained Optimization Problems

被引：0
|
作者：
Bin Ibrahim, Mohd Asrul Hery [1 ]
Mamat, Mustafa [2 ]
June, Leong Wah [3 ]
Sofi, Azfi Zaidi Mohammad [4 ]
机构：
[1] IUKL, Fac Appl Sci & Fdn, Kajang, Malaysia
[2] Univ Malaysia Terengganu UMT, Fac Sci & Technol, Dept Math, Terengganu, Malaysia
[3] Univ Putra Malaysia, Fac Sci, Dept Math, Serdang 43400, Malaysia
[4] Kolej Univ Islam Antarabangsa Selangor KUIS, Fac Sci & Informat Technol, Kajang, Selangor, Malaysia
来源：
PROCEEDINGS OF THE 21ST NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES (SKSM21): GERMINATION OF MATHEMATICAL SCIENCES EDUCATION AND RESEARCH TOWARDS GLOBAL SUSTAINABILITY | 2014年 / 1605卷
关键词：
Conjugate Gradient method; BFGS method; Search Direction; CONJUGATE-GRADIENT ALGORITHMS; QUASI-NEWTON METHODS; CONVERGENCE CONDITIONS; ASCENT METHODS; MINIMIZATION; SOFTWARE;
D O I：
10.1063/1.4887583
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper we present a new search direction known as the CG-BFGS method, which uses the search direction of the conjugate gradient method approach in the quasi-Newton methods. The new algorithm is compared with the quasi-Newton methods in terms of the number of iterations and CPU-time. The Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is used as an updating formula for the approximation of the Hessian for both methods. Our numerical analysis provides strong evidence that our CG-BFGS method is more efficient than the ordinary BFGS method. Besides, we also prove that the new algorithm is globally convergent.
引用
收藏
页码：167 / 172
页数：6
相关论文
共 50 条
12345