HYBRID BFGS-ZMRI METHODS WITH GLOBAL CONVERGENCE PROPERTIES

被引：0

作者：

Abidin, Zubai'ah Zainal ^{[1
]}

Aini, Nurul ^{[1
]}

Husin, Siti Farhana ^{[1
]}

Rivaie, Mohd ^{[2
]}

Mamat, Mustafa ^{[1
]}

机构：

[1] Univ Sultan Zainal Abidin UniSZA, Fac Informat & Comp, Terengganu, Malaysia

[2] Univ Teknol Mara UiTM, Dept Comp Sci & Math, Terengganu, Malaysia

来源：

PROCEEDING OF THE 25TH NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES (SKSM25): MATHEMATICAL SCIENCES AS THE CORE OF INTELLECTUAL EXCELLENCE | 2018年 / 1974卷

关键词：

BFGS; Line search; Quasi-Newton; Steepest descent; Unconstrained optimization; QUASI-NEWTON METHODS; OPTIMIZATION; ALGORITHM;

D O I：

10.1063/1.5041570

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

In this paper, we focus on the steepest descent and quasi-Newton method in solving unconstrained optimization problem. Therefore, we develop a new search direction for hybrid BFGS-ZMRI method with global convergence properties. Based on the numerical result, our method shows significant improvement in the number of iteration and CPU time.

引用

页数：8

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