Hybrid DFP-CG method for solving unconstrained optimization problems

被引：2
|
作者：
Osman, Wan Farah Hanan Wan [1 ,3 ]
Ibrahim, Mohd Asrul Hery [2 ]
Mamat, Mustafa [3 ]
机构：
[1] Tati Unic Coll, Fac Comp Media & Technol Management, Kemaman 24000, Terengganu, Malaysia
[2] Univ Malaysia Kelantan, Fac Entrepreneurship & Business, Kota Baharu 16100, Kelantan, Malaysia
[3] Univ Sultan Zainal Abidin, Dept Computat & Appl Math, Campus Tembila, Besut 22200, Terengganu, Malaysia
来源：
1ST INTERNATIONAL CONFERENCE ON APPLIED & INDUSTRIAL MATHEMATICS AND STATISTICS 2017 (ICOAIMS 2017) | 2017年 / 890卷
关键词：
D O I：
10.1088/1742-6596/890/1/012033
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
The conjugate gradient (CG) method and quasi-Newton method are both well known method for solving unconstrained optimization method. In this paper, we proposed a new method by combining the search direction between conjugate gradient method and quasi Newton method based on BFGS-CG method developed by Ibrahim et al. The DavidonFletcher-Powell (DFP) update formula is used as an approximation of Hessian for this new hybrid algorithm. Numerical result showed that the new algorithm perform well than the ordinary DFP method and proven to posses both sufficient descent and global convergence properties.
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页数：6
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